How to Automatically Post Events to Google Calendar with Apps Script

Learn how to automatically post events to Google Calendar with Apps Script. Use Apps Script to create Google Calendar events automatically. Add a list of events from a Google Sheet to a Google Calendar with Google Apps Script

Google Sheets - Use Apps Script to Create Google Calendar Events Automatically

How to add events to a Google Calendar and email guests all from a Google Sheet using Apps Script and a button.

⌚Chapter Time Markers:

00:00:00 - Intro
00:00:42 - I'm Eamonn. You're awesome
00:00:54 - Time in Google Sheets
00:01:07 - Dropdown Times
00:03:08 - Combine Date and Time
00:04:17 - Create a Calendar
00:04:44 - Apps Script
00:06:19 - For Each Loop
00:08:30 - Run Function with Button
00:09:44 - Optimize Columns 

CalendarApp Class: https://developers.google.com/apps-script/reference/calendar/calendar-app 

Google Sheets – How to Automatically Post Events to Google Calendar with Apps Script

In this article we'll link two Google services -> Google Sheets and Google Calendar.

By using a very short custom function in Google Apps Script, we can add a list of events from a Google Sheet to a Google Calendar. 🤯

And we'll even have it email our guests as well. 🔥

Google Sheets Setup

Our sheet is quite straightforward. We have event names, dates, start times, end times and guest emails.

The only curious thing is the formatting of our dates and times - I'll cover this as we go on, but you can see that columns B and C are repeating information from columns D, E and F...

screenshot of Google Sheet event information

Google Calendar needs to receive the start and end times in the form of a full date/time object. But in the Google Sheet, there's not an easy way to create a dropdown data validation for users to select a date/time object.

In column D, I've put data validation to select a valid date.

screenshot of data validation for a valid date

And in columns E and F, I've created a dropdown list of valid times.

screenshot of data validation for a valid time

Columns B and C combine these together into a format that's useable to send to Google Calendar by using the =TEXT() function to concatenate the date and times together.

screenshot of google sheets text concatenation

I promise it'll make more sense in a second! 😃

Calendar Setup

Let's make a new calendar in Google Calendar.

Screenshot of new calendar options

Underneath your calendars on the left sidebar of Google Calendar, click the plus icon to add a new one.

Give it a name and a description if you want, and then we're ready to roll.

Screenshot of creating a new calendar

Scroll down a bit in the calendar's settings to the Integrate Calendar section. Copy the calendar ID. This is how we'll get Apps Script talking to Calendar!

screenshot of calendar ID

Apps Script + CalendarApp

Apps Script is awesome. 👏

The Class CalendarApp allows a script to access a user's Google Calendar and make changes to it.

Here is the full script, and we'll walk through what's going on below.

// Creates an events variable which is an array of arrays
function createCalendarEvent() {
	let events = SpreadsheetApp.getActiveSpreadsheet().getRangeByName("events").getValues();

	// Creates an event for each item in events array
    
	events.forEach(function(e){
    	CalendarApp.getCalendarById("f7574e7b4d1ad00c9ecd7f1eba5bed329e8600e317cd387a400748d67f301d06@group.calendar.google.com").createEvent(
      	e[0],
      	new Date(e[1]),
      	new Date(e[2]),
      	{guests: e[6],sendInvites: true}
    );
  })
}

I've named the range A3:B8 as "events". Then in Apps Script, we create a variable named events that grabs all the values in that whole range. We used a small range, but you could make this as many rows long as need be.

let events = SpreadsheetApp.getActiveSpreadsheet().getRangeByName("events").getValues();

Then, we loop through each item and add the events to our calendar.

The first part is where we use that calendar ID string we grabbed from Google Calendar for the getCalendarById method.

Then we use the createEvent method to pull data from each row in our Google Sheet and make new events.

Here's the createEvent description from the developers page:

screenshot of createEvent method

You can think of each row of data in the Google Sheet as an array of values. In the zero position is the event name, in the one position is the event date and start time, and so on.

screenshot of array of arrays illustrated

By using e[0] we can access the element that is in the zero position for every time we loop through the forEach loop...effectively looping through each row of data.

And this is where the funky stuff we did with the start and end times comes into play.

Because the values in columns B and C are strings since we concatenated them together, we need to turn them back into complete date objects now.

That's why we're passing new Date(e[1]) and new Date (e[2]) into our createEvent function.

It's a bit of a cumbersome way to allow ourselves to use those dropdown selections in Google Sheets rather than painfully typing in a full date/time object.

User experience > code. 👍

And lastly, we add an optional parameter to send invites to guests.

Send with Button

That's all there is to the Apps Script. 🎉

As an added feature, we've attached a script to the rounded rectangle drawing to make it function like a button. Any time this is pressed, the events in the Google Sheet will populate the Google Calendar.

Screenshot of assigning a script to a drawing in Google Sheets

I hope this has been useful for you!

Source: https://www.freecodecamp.org

#googlesheets #spreadsheets #dataanalytics #googlecalendar #appsscript

Lộ trình để trở thành Nhà phân tích dữ liệu (Data Analytics)

Lộ trình hoàn chỉnh để trở thành Nhà phân tích dữ liệu (Data Analytics) mà không cần có kinh nghiệm trước đó. Làm theo các bước phác thảo trong lộ trình này sẽ giúp bạn có được những kỹ năng và kiến ​​thức cần thiết để trở thành một nhà phân tích dữ liệu thành công. Từ việc hiểu vai trò của một nhà phân tích dữ liệu, đến việc thành thạo SQL và Python, đến kết nối mạng với các nhà phát triển khác, mỗi bước đều rất quan trọng để đạt được thành công trong lĩnh vực này.

Xin chào và chào mừng đến với thế giới phân tích dữ liệu! Nếu bạn đang xem xét một nghề nghiệp trong lĩnh vực này, bạn đang ở trong một công ty tốt. Phân tích dữ liệu là một lĩnh vực đang phát triển và thú vị, ngày càng trở nên quan trọng trong thế giới dựa trên dữ liệu ngày nay.

Hãy đối mặt với nó, ngày nay tất cả chúng ta đều chìm trong dữ liệu. Từ các bài đăng trên mạng xã hội đến các giao dịch tài chính đến hồ sơ y tế, không thiếu thông tin để sàng lọc.

Đó là lúc các nhà phân tích dữ liệu tham gia. Họ là những người giúp chúng tôi hiểu được tất cả dữ liệu đó và biến dữ liệu đó thành thông tin chi tiết có giá trị.

Và những hiểu biết đó có thể thay đổi cuộc chơi. Họ có thể giúp các doanh nghiệp cải thiện sản phẩm và dịch vụ của họ, chính phủ đưa ra các quyết định chính sách sáng suốt hơn và các cá nhân đưa ra lựa chọn tốt hơn trong cuộc sống cá nhân và nghề nghiệp của họ.

Nhưng nó không chỉ là về tác động. Phân tích dữ liệu cũng có thể khá sinh lợi. Theo các nghiên cứu gần đây, mức lương trung bình cho một nhà phân tích dữ liệu ở Mỹ là khoảng 70.000 USD mỗi năm và con số đó có thể tăng cao hơn nữa nếu có kinh nghiệm và chuyên môn.

Tất nhiên, giống như bất kỳ nghề nào, phân tích dữ liệu cũng có những thách thức của nó. Đôi khi có bộ dữ liệu gây đau đầu, mối đe dọa luôn hiện hữu của hội chứng kẻ mạo danh và các cuộc tranh luận bất tận về ngôn ngữ lập trình hoặc công cụ trực quan hóa dữ liệu tốt nhất. Nhưng này, nếu bạn sẵn sàng đối mặt với thử thách, đây có thể là lĩnh vực dành cho bạn.

Vì vậy, nếu bạn đang cố gắng trở thành Nhà phân tích dữ liệu, thì bài viết này là dành cho bạn. Hy vọng rằng nó sẽ giúp bạn tiết kiệm được nhiều thời gian và công sức và bạn không phải lãng phí thời gian của mình để học cả đống thứ không liên quan như tôi đã từng làm.

Mục lục:

• Bước 1: Tìm hiểu vai trò của Nhà phân tích dữ liệu
• Bước 2: Khám phá các Yêu cầu Công việc đối với Vai trò Nhà phân tích Dữ liệu
• Bước 3: Làm quen với Toán học và Thống kê
• Bước 4: Làm chủ Excel để phân tích dữ liệu
• Bước 5: Làm chủ SQL để trích xuất dữ liệu
• Bước 6: Tìm hiểu Python để phân tích dữ liệu
• Bước 7: Làm chủ một công cụ trực quan hóa dữ liệu
• Bước 8: Kết nối với các nhà phát triển và phân tích dữ liệu khác
• Bước 9: Đừng Quên "Kỹ Năng Mềm"
• Phần kết luận

Bước 1: Tìm hiểu vai trò của Nhà phân tích dữ liệu

Bạn đang xem xét một nghề nghiệp như một nhà phân tích dữ liệu? Điều đó thật tuyệt! Hãy dành một chút thời gian để nắm bắt ý chính của nó trước khi tiếp tục.

Bạn thấy đấy, công việc của một nhà phân tích dữ liệu không chỉ liên quan đến việc tính toán số liệu và tạo biểu đồ (mặc dù những việc đó cũng có thể khá thú vị). Nó liên quan đến việc tận dụng dữ liệu để tìm thông tin chi tiết và giải quyết các vấn đề. Điều này có nghĩa là đưa ra các câu hỏi chính xác, sắp xếp và đánh giá dữ liệu, đồng thời giải thích kết luận của bạn cho người khác.

Một số kỹ năng và hoạt động cốt lõi mà nhà phân tích dữ liệu thường thực hiện bao gồm:

• Thu thập và phân tích các tập dữ liệu lớn để xác định các mẫu, xu hướng và thông tin chi tiết có thể cung cấp thông tin cho các quyết định kinh doanh.
• Sử dụng các công cụ và kỹ thuật thống kê để rút ra những hiểu biết sâu sắc từ dữ liệu.
• Phát triển và triển khai các hệ thống thu thập dữ liệu và các chiến lược khác nhằm tối ưu hóa hiệu quả và chất lượng dữ liệu.
• Cộng tác với các nhóm khác để xác định nhu cầu kinh doanh và phát triển các giải pháp dữ liệu nhằm giải quyết các nhu cầu đó.
• Truyền đạt những phát hiện và hiểu biết sâu sắc cho các bên liên quan một cách rõ ràng và có thể hành động.

Vì vậy, nếu bạn không phải là thần đồng toán học hay chuyên gia máy tính, đừng lo lắng. Thống kê, khoa học máy tính và kinh doanh chỉ là một vài trong số rất nhiều nguyên tắc được đưa vào chủ đề phân tích dữ liệu đa ngành. Tất cả những ai thích học hỏi, giải quyết vấn đề và tạo ra sự khác biệt đều có thể thực sự thích nó.

Vậy làm thế nào bạn có thể tìm hiểu thêm về những gì một nhà phân tích dữ liệu làm? Bạn có thể bắt đầu bằng cách xem qua nhiều tài nguyên miễn phí có thể truy cập trực tuyến. Ví dụ: đây là một bài báo thảo luận về những gì các nhà phân tích dữ liệu thực sự làm . Và đây là phần thảo luận về vai trò của nhà phân tích dữ liệu và khoa học dữ liệu .

Nhiều blog, podcast và kênh YouTube cung cấp nội dung giải trí và thông tin về chủ đề phân tích dữ liệu. Tôi sẽ bỏ một số kênh YouTube đã giúp tôi trong nhiều năm dưới đây.

Để hiểu được các kỹ năng và phẩm chất cần thiết, bạn cũng có thể kết nối với các nhà phân tích dữ liệu khác, tham dự các cuộc họp hoặc hội thảo và nghiên cứu mô tả công việc.

Đừng quên xem xét liệu bạn có thích xử lý dữ liệu hay không. Bạn có thích tìm các mẫu và giải câu đố không? Bạn có muốn thay đổi thế giới tốt đẹp hơn không? Phân tích dữ liệu có thể là lựa chọn nghề nghiệp lý tưởng cho bạn nếu câu trả lời là có.

Bước đầu tiên trên con đường của bạn là hiểu những gì một nhà phân tích dữ liệu làm. Hãy tận hưởng, dành thời gian của bạn và đừng ngần ngại đặt câu hỏi.

Bước 2: Khám phá các Yêu cầu Công việc đối với Vai trò Nhà phân tích Dữ liệu

Bây giờ bạn đã hiểu rõ hơn về vai trò của một nhà phân tích dữ liệu, đã đến lúc bắt đầu xem xét những gì nhà tuyển dụng đang tìm kiếm. Rốt cuộc, bạn muốn đảm bảo rằng các kỹ năng và kiến ​​thức của bạn phù hợp với những gì thị trường việc làm yêu cầu.

Nhưng trước khi bạn bắt đầu lo lắng về việc không có đủ kinh nghiệm, hãy nhớ rằng mọi công ty đều khác nhau. Một số có thể ưu tiên các kỹ năng lập trình, trong khi những người khác có thể coi trọng khả năng giao tiếp và sự nhạy bén trong kinh doanh. Đó là lý do tại sao điều quan trọng là bạn phải nghiên cứu và tìm ra những kỹ năng và trình độ cụ thể nào được yêu cầu nhiều nhất trong ngành hoặc công ty bạn mong muốn.

Vì vậy, làm thế nào để bạn tìm kiếm thông tin này? Chà, một nơi tuyệt vời để bắt đầu là xem danh sách công việc và mô tả trên các bảng công việc như LinkedIn, Thật hoặc Glassdoor. Điều này có thể giúp bạn hiểu rõ các yêu cầu và trình độ chính đối với các vai trò phân tích dữ liệu khác nhau.

Một số ví dụ về những gì danh sách công việc có thể yêu cầu bao gồm:

• Thành thạo SQL và kinh nghiệm làm việc với các tập dữ liệu lớn
• Quen thuộc với Python và các công cụ trực quan hóa dữ liệu như Tableau hoặc Power BI
• Kỹ năng phân tích mạnh mẽ và khả năng rút ra những hiểu biết sâu sắc từ dữ liệu phức tạp
• Kinh nghiệm với phân tích thống kê và kỹ thuật mô hình hóa
• Kỹ năng giao tiếp xuất sắc và khả năng giải thích những phát hiện phức tạp cho cả khán giả kỹ thuật và phi kỹ thuật

Nhưng đừng dừng lại ở đó! Bạn cũng có thể liên hệ với những người làm việc trong lĩnh vực này hoặc những người có chức danh công việc mà bạn quan tâm. Hỏi họ về kinh nghiệm của họ và những kỹ năng nào họ nghĩ là quan trọng nhất để thành công trong vai trò của họ. Bạn thậm chí có thể muốn xem xét việc thiết lập các cuộc phỏng vấn thông tin để tìm hiểu thêm về lĩnh vực này và nhận lời khuyên về cách bắt đầu.

Và nói về việc bắt đầu, điều quan trọng cần nhớ là không có gì thay thế được kinh nghiệm thực hành. Dù rất hấp dẫn nếu bạn dành toàn bộ thời gian để xem hướng dẫn, nhưng bạn sẽ học nhanh hơn và hiệu quả hơn bằng cách thực sự xây dựng mọi thứ và làm việc trên các dự án phân tích dữ liệu thực.

Vì vậy, hãy dành chút thời gian để khám phá các yêu cầu công việc, nhưng đừng quên tiếp tục xây dựng các kỹ năng của bạn và tích lũy kinh nghiệm thực tế. Với một chút nỗ lực và rất nhiều sự tò mò, bạn sẽ vững bước trên con đường trở thành một nhà phân tích dữ liệu thành công.

Bước 3: Làm quen với Toán học và Thống kê

Được rồi, tôi biết bạn đang nghĩ gì. Toán học và thống kê? Rất tiếc!

Nhưng hãy nghe tôi nói trước khi bạn chạy đến những ngọn đồi. Để một nhà phân tích dữ liệu có thể hiểu được dữ liệu và rút ra những hiểu biết có giá trị từ dữ liệu đó, việc hiểu biết cơ bản về các khái niệm này là điều cần thiết.

Vậy những khái niệm và công thức thống kê cơ bản nào bạn nên làm quen?

Bây giờ, để bắt đầu, có các phép đo xu hướng trung tâm được gọi là giá trị trung bình, trung vị và chế độ, có thể cho bạn ý tưởng về giá trị tiêu biểu trong tập dữ liệu. Bạn nên biết cách tính toán chúng.

Độ lệch chuẩn là thước đo mức độ phổ biến của dữ liệu so với mức trung bình và bạn sẽ có thể tính toán được.

Và để tìm mối quan hệ giữa các biến và đưa ra dự đoán dựa trên các mối liên hệ đó, bạn cũng nên làm quen với tương quan và hồi quy.

Nhưng nó không chỉ là về những con số. Bạn cũng sẽ cần hiểu biết cơ bản về đại số tuyến tính, được sử dụng trong nhiều phương pháp phân tích dữ liệu. Đây là một khóa học chuyên sâu (và sách giáo khoa) để giúp bạn bắt đầu.

Bạn có thể cần sử dụng ma trận để sửa đổi và thao tác dữ liệu hoặc bạn có thể cần sử dụng hồi quy tuyến tính để dự báo các giá trị trong tương lai dựa trên xu hướng lịch sử.

Nếu bạn không có nền tảng toán học vững chắc, điều này có vẻ khó khăn. Nhưng đừng lo lắng! Có rất nhiều tài nguyên có sẵn để hỗ trợ bạn trong quá trình học tập.

Ví dụ: Khan Academy cung cấp các bài học và nhiệm vụ thực hành về toán học và thống kê. Nếu bạn thích sách, "Khoa học dữ liệu cho người mới bắt đầu" của Andrew Park là một nguồn tài nguyên tuyệt vời bao gồm cả nguyên tắc thống kê và toán học theo cách dễ tiếp cận.

freeCodeCamp đang phát triển một chương trình dạy toán mà bạn có thể đọc tại đây .

Và đây là hướng dẫn về số liệu thống kê bạn cần biết để tiếp cận khoa học dữ liệu và theo đuổi các lĩnh vực như Học máy.

Điều quan trọng là bắt đầu nhỏ và xây dựng kiến ​​​​thức của bạn dần dần. Đừng ngại đặt câu hỏi hoặc tìm kiếm sự giúp đỡ khi bạn cần. Với một chút luyện tập và kiên trì, bạn sẽ sớm thấy rằng toán học và thống kê thực sự khá thú vị (không, nghiêm túc đấy!).

Bước 4: Làm chủ Excel để phân tích dữ liệu

Excel là một công cụ quan trọng trong kho vũ khí của nhà phân tích dữ liệu. Nó được sử dụng bởi hầu hết mọi tổ chức ngoài kia và việc thành thạo nó sẽ giúp bạn dọn dẹp, thao tác và phân tích dữ liệu một cách dễ dàng.

Với Excel, bạn có thể tạo các công thức và hàm để thực hiện phép tính, bảng tổng hợp và biểu đồ để trực quan hóa dữ liệu, đồng thời sử dụng các công cụ phân tích dữ liệu để đưa ra dự đoán và xác định các mẫu. Excel đặc biệt hữu ích cho phân tích hồi quy, dự báo và phân tích kịch bản.

Nếu bạn nghiêm túc muốn trở thành một nhà phân tích dữ liệu, thì điều cần thiết là bạn phải thành thạo Excel. May mắn thay, có rất nhiều tài nguyên trực tuyến có sẵn để giúp bạn tìm hiểu. Hãy xem ExcelIsFun , Excel Chandoo , Tutorials Point, Ashutosh Kumar và MyOnlineTrainingHub để biết hướng dẫn trên Youtube. Ngoài ra, các khóa học sau đây sẽ hướng dẫn bạn cách tận dụng tối đa Excel.

1. Giới thiệu về Phân tích dữ liệu bằng Excel của Coursera
2. Phân tích dữ liệu với Python cho người dùng Excel trên kênh YouTube của freeCodeCamp
3. Kỹ năng Excel cho Chuyên ngành Kinh doanh của Coursera
4. Phân tích và trực quan hóa dữ liệu với Excel bằng EdX

Hãy nhớ rằng, Excel chỉ là một công cụ trong bộ công cụ phân tích dữ liệu của bạn. Nhưng đó là một điều quan trọng mà bạn sẽ sử dụng hàng ngày với tư cách là nhà phân tích dữ liệu. Bằng cách thành thạo Excel, bạn sẽ được trang bị đầy đủ để xử lý mọi tác vụ liên quan đến dữ liệu theo cách của bạn.

Bây giờ, hãy chuyển sang kỹ năng tiếp theo và cũng là một trong những kỹ năng quan trọng nhất mà bạn sẽ cần với tư cách là nhà phân tích dữ liệu.

Bước 5: Làm chủ SQL để trích xuất dữ liệu

SQL (Ngôn ngữ truy vấn có cấu trúc) là một công cụ quan trọng trong phân tích dữ liệu. Là một nhà phân tích dữ liệu, một trong những trách nhiệm chính của bạn là trích xuất dữ liệu từ cơ sở dữ liệu và SQL là ngôn ngữ được sử dụng để làm việc đó.

SQL không chỉ chạy các truy vấn cơ bản như SELECT, FROM và WHERE. Đó là một ngôn ngữ phức tạp cho phép bạn thao tác và chuyển đổi dữ liệu theo vô số cách. SQL được sử dụng để nối dữ liệu từ nhiều bảng, lọc và tổng hợp dữ liệu cũng như tạo bảng và dạng xem mới.

Để trở thành một nhà phân tích dữ liệu hiệu quả, điều cần thiết là phải thành thạo SQL. Bạn sẽ cảm thấy thoải mái với việc viết truy vấn, tạo bảng và hiểu cách tối ưu hóa truy vấn của mình để đạt được hiệu suất.

May mắn thay, có rất nhiều tài nguyên có sẵn để giúp bạn học SQL. Một số nơi tuyệt vời để bắt đầu là Khan Academy SQL , W3Schools , SQLZoo , SQLbolt , Luke Barousse , Alex the Analyst , Microsoft Power Tools và cuối cùng là một số trò chơi SQL như đảo SQL và SQL Murder.

Ngoài ra, có rất nhiều khóa học và sách trực tuyến đề cập chuyên sâu về SQL. Dưới đây là một số để giúp bạn bắt đầu:

1. Tìm hiểu SQL và cơ sở dữ liệu - Khóa học đầy đủ cho người mới bắt đầu
2. Giáo trình Cơ sở dữ liệu quan hệ từ freeCodeCamp
3. Tổng hợp các khóa học cơ sở dữ liệu quan hệ miễn phí

Bằng cách thành thạo SQL, bạn sẽ có thể trích xuất những hiểu biết có giá trị từ cơ sở dữ liệu và thao tác dữ liệu theo cách cung cấp những hiểu biết có ý nghĩa về kinh doanh.

Bước 6: Tìm hiểu Python để phân tích dữ liệu

Tôi biết có rất nhiều suy đoán về việc liệu một nhà phân tích dữ liệu có cần Python hay không – một số nói rằng họ cần Python trong khi một số nói rằng họ không.

Đối với tôi, tôi muốn nói rằng điều đó phụ thuộc vào công ty bạn đang làm việc – nhưng thật tuyệt khi có lợi thế khi học Python vì đây là một trong những ngôn ngữ lập trình được sử dụng rộng rãi nhất trong thế giới phân tích dữ liệu. Python được biết đến với tính đơn giản, dễ đọc và linh hoạt, khiến nó trở thành lựa chọn phổ biến của các nhà phân tích dữ liệu.

Python có rất nhiều thư viện và công cụ có thể giúp phân tích dữ liệu dễ dàng hơn, chẳng hạn như Pandas để thao tác và phân tích dữ liệu, NumPy để tính toán khoa học và Matplotlib để trực quan hóa dữ liệu. Nó cũng có khả năng tự động hóa các tác vụ , giúp việc phân tích dữ liệu trở nên hiệu quả và hiệu quả hơn.

Học Python để phân tích dữ liệu là một khoản đầu tư tuyệt vời trong sự nghiệp của bạn với tư cách là nhà phân tích dữ liệu. Nó không chỉ cho phép bạn làm việc với các thư viện mạnh mẽ mà còn mở ra nhiều cơ hội để làm việc với các tập dữ liệu lớn hơn và các phân tích phức tạp hơn.

Có rất nhiều tài nguyên có sẵn để giúp bạn học Python, từ các khóa học trực tuyến miễn phí đến các chương trình và sách giáo khoa trực tuyến có trả phí. Một số tài nguyên bao gồm freeCodeCamp , DataCamp, CodeBasis , Lập trình với Mosh và Microsoft Learn.

Bằng cách học Python, bạn sẽ có thể thực hiện phân tích dữ liệu phức tạp hơn, tự động hóa các tác vụ và làm việc với nhiều bộ dữ liệu hơn, giúp bạn trở thành tài sản quý giá trong bất kỳ tổ chức tập trung vào dữ liệu nào.

Bước 7: Làm chủ một công cụ trực quan hóa dữ liệu

Là một nhà phân tích dữ liệu, điều cần thiết là có thể truyền đạt những phát hiện của bạn một cách rõ ràng và ngắn gọn. Một cách để làm điều này là thông qua trực quan hóa dữ liệu. Các công cụ trực quan hóa dữ liệu như PowerBI và Tableau có thể giúp bạn tạo biểu đồ, đồ thị và bảng điều khiển tương tác giúp người khác dễ dàng hiểu được phát hiện của bạn. Chúng ta sẽ nói về họ nhiều hơn trong một phút.

Đây là khóa học Tableau về Khoa học dữ liệu và Trực quan hóa dữ liệu mà bạn có thể xem.

Mặc dù SQL rất tốt cho việc truy vấn và thao tác dữ liệu, nhưng nó không thể làm cho dữ liệu của bạn trở nên sống động hoàn toàn. Đây là lúc công cụ trực quan hóa dữ liệu ra đời. Những công cụ này cho phép bạn chuyển đổi dữ liệu của mình thành hình ảnh trực quan sâu sắc và dễ hiểu để có thể chia sẻ với các bên liên quan.

Bạn có thể tìm hiểu kiến ​​thức cơ bản về trực quan hóa dữ liệu trong khóa học miễn phí chuyên sâu này trên kênh YouTube của freeCodeCamp.

PowerBI là một lựa chọn tuyệt vời để trực quan hóa dữ liệu vì nó dễ học và tích hợp với các sản phẩm khác của Microsoft. Điều này làm cho nó trở thành một công cụ lý tưởng cho các tổ chức sử dụng Microsoft Office. Tableau cũng là một lựa chọn phổ biến và có cộng đồng người dùng mạnh mẽ cũng như nhiều tính năng.

Học một công cụ trực quan hóa dữ liệu như PowerBI hoặc Tableau sẽ cho phép bạn tạo các hình ảnh trực quan hấp dẫn giúp bạn hiểu rõ hơn về dữ liệu của mình và truyền đạt những phát hiện của mình cho người khác. Có rất nhiều khóa học và hướng dẫn trực tuyến có sẵn để giúp bạn tìm hiểu các công cụ này, chẳng hạn như khóa đào tạo chính thức về Microsoft PowerBI và các khóa đào tạo riêng của Tableau .

Bằng cách thành thạo một công cụ trực quan hóa dữ liệu, bạn sẽ có thể tạo các hình ảnh trực quan tương tác và hấp dẫn giúp bạn hiểu rõ hơn về dữ liệu của mình và truyền đạt những phát hiện của mình cho người khác, giúp bạn trở thành tài sản vô giá đối với bất kỳ tổ chức tập trung vào dữ liệu nào.

Bạn cũng có thể đi sâu vào các công cụ viz dữ liệu phổ biến khác như D3.js - đây là một khóa học về nó để giúp bạn bắt đầu .

Bước 8: Kết nối với các nhà phát triển và phân tích dữ liệu khác

Mạng là một phần thiết yếu của bất kỳ ngành nghề nào và phân tích dữ liệu cũng không ngoại lệ. Bằng cách kết nối với các nhà phân tích và nhà phát triển dữ liệu khác, bạn có thể học hỏi kinh nghiệm của họ, hiểu rõ hơn về các xu hướng và công nghệ mới nhất của ngành cũng như có khả năng tìm được cơ hội việc làm.

Dưới đây là một số cách để kết nối với những người khác trong lĩnh vực này:

1. Tham dự các sự kiện trong ngành: Tìm kiếm các hội nghị, cuộc gặp gỡ và các sự kiện khác liên quan đến phân tích dữ liệu và tham dự chúng. Đây là một cách tuyệt vời để gặp gỡ những người khác trong lĩnh vực này và tìm hiểu về những phát triển và công nghệ mới.
2. Tham gia cộng đồng trực tuyến: Có nhiều cộng đồng trực tuyến dành cho nhà phân tích và phát triển dữ liệu, chẳng hạn như diễn đàn, nhóm LinkedIn và nhóm truyền thông xã hội. Tham gia các cộng đồng này và tham gia thảo luận để kết nối với những người khác trong lĩnh vực này.
3. Tiếp cận với những người khác: Đừng ngại tiếp cận với các nhà phân tích và nhà phát triển dữ liệu khác, cho dù thông qua phương tiện truyền thông xã hội, email hay gặp trực tiếp. Giới thiệu bản thân, xin lời khuyên và xây dựng mối quan hệ.

Hãy nhớ rằng, mạng là một con đường hai chiều. Hãy sẵn sàng cung cấp sự giúp đỡ và lời khuyên cho những người khác trong lĩnh vực này. Bằng cách xây dựng một mạng lưới quan hệ mạnh mẽ trong lĩnh vực phân tích dữ liệu, bạn có thể nâng cao cơ hội nghề nghiệp của mình và luôn cập nhật các xu hướng và công nghệ mới nhất của ngành.

Bước 9: Đừng Quên "Kỹ Năng Mềm"

Một kỹ năng cuối cùng mà tôi nghĩ cần phải trau dồi trước khi bạn có thể trở thành một DA tuyệt vời là kỹ năng mềm liên quan đến khả năng giao tiếp, giải quyết vấn đề, v.v.

Sau tất cả những gì đã nói và làm, hãy thực hành, thực hành và xây dựng các dự án.

Phần kết luận.

Trở thành một nhà phân tích dữ liệu đòi hỏi sự cống hiến, làm việc chăm chỉ và đam mê phân tích dữ liệu. Thực hiện theo các bước được nêu trong lộ trình này sẽ giúp bạn có được những kỹ năng và kiến ​​thức cần thiết để trở thành một nhà phân tích dữ liệu thành công.

Từ việc hiểu vai trò của một nhà phân tích dữ liệu, đến việc thành thạo SQL và Python, đến kết nối mạng với các nhà phát triển khác, mỗi bước đều rất quan trọng để đạt được thành công trong lĩnh vực này.

Hãy nhớ luôn tò mò, không ngừng học hỏi và luôn sẵn sàng thích ứng với các công nghệ và phương pháp mới. Với quyết tâm và sự kiên trì, bạn có thể đạt được mục tiêu trở thành nhà phân tích dữ liệu thành thạo và mở ra vô số cơ hội nghề nghiệp thú vị.

Nguồn: https://www.freecodecamp.org

#dataanalytics

แผนงานสู่การเป็น Data Analytics

แผนงานที่สมบูรณ์สู่การเป็น Data Analytics โดยไม่ต้องมีประสบการณ์มาก่อน การปฏิบัติตามขั้นตอนในแผนงานนี้จะช่วยให้คุณได้รับทักษะและความรู้ที่จำเป็นในการเป็นนักวิเคราะห์ข้อมูลที่ประสบความสำเร็จ ตั้งแต่การทำความเข้าใจบทบาทของนักวิเคราะห์ข้อมูล ไปจนถึงการเรียนรู้ SQL และ Python ไปจนถึงการสร้างเครือข่ายกับนักพัฒนารายอื่น แต่ละขั้นตอนมีความสำคัญต่อการบรรลุความสำเร็จในด้านนี้

สวัสดีและขอต้อนรับสู่โลกแห่งการวิเคราะห์ข้อมูล! หากคุณกำลังพิจารณาอาชีพในสาขานี้ แสดงว่าคุณกำลังมีบริษัทที่ดี การวิเคราะห์ข้อมูลเป็นสาขาที่กำลังเติบโตและน่าตื่นเต้น ซึ่งมีความสำคัญมากขึ้นเรื่อยๆ ในโลกปัจจุบันที่ขับเคลื่อนด้วยข้อมูล

ยอมรับเถอะว่าทุกวันนี้เรากำลังจมอยู่ในข้อมูล ตั้งแต่โพสต์บนโซเชียลมีเดียไปจนถึงธุรกรรมทางการเงินไปจนถึงเวชระเบียน ไม่มีปัญหาการขาดแคลนข้อมูลให้ค้นหา

นั่นคือที่มาของนักวิเคราะห์ข้อมูล พวกเขาคือผู้ที่ช่วยให้เราเข้าใจข้อมูลทั้งหมดนั้นและเปลี่ยนให้เป็นข้อมูลเชิงลึกที่มีค่า

และข้อมูลเชิงลึกเหล่านั้นสามารถเปลี่ยนแปลงเกมได้ พวกเขาสามารถช่วยให้ธุรกิจปรับปรุงผลิตภัณฑ์และบริการของพวกเขา รัฐบาลทำการตัดสินใจนโยบายที่มีข้อมูลมากขึ้น และบุคคลมีทางเลือกที่ดีขึ้นในชีวิตส่วนตัวและอาชีพของพวกเขา

แต่ไม่ใช่แค่ผลกระทบเท่านั้น การวิเคราะห์ข้อมูลสามารถสร้างรายได้ค่อนข้างมาก จากการศึกษาเมื่อเร็วๆ นี้ เงินเดือนเฉลี่ยของนักวิเคราะห์ข้อมูลในสหรัฐฯ อยู่ที่ประมาณ 70,000 ดอลลาร์สหรัฐฯ ต่อปี และตัวเลขดังกล่าวสามารถไต่สูงขึ้นไปอีกได้หากมีประสบการณ์และความเชี่ยวชาญ

แน่นอน เช่นเดียวกับอาชีพอื่นๆ การวิเคราะห์ข้อมูลก็มีความท้าทาย มีชุดข้อมูลที่ทำให้ปวดหัวเป็นครั้งคราว ภัยคุกคามที่มีอยู่ในปัจจุบันของกลุ่มอาการแอบอ้าง และการถกเถียงไม่รู้จบเกี่ยวกับภาษาโปรแกรมที่ดีที่สุดหรือเครื่องมือสร้างภาพข้อมูล แต่เดี๋ยวก่อน หากคุณพร้อมสำหรับความท้าทาย นี่อาจเป็นสนามสำหรับคุณ

ดังนั้น หากคุณกำลังพยายามที่จะเป็นนักวิเคราะห์ข้อมูล บทความนี้เหมาะสำหรับคุณ หวังว่าจะช่วยคุณประหยัดเวลาและความพยายามได้มาก และคุณไม่ต้องเสียเวลาเรียนรู้สิ่งต่างๆ ที่ไม่เกี่ยวข้องมากมายเหมือนที่ฉันเคยทำ

สารบัญ:

• ขั้นตอนที่ 1: ทำความรู้จักกับบทบาทของนักวิเคราะห์ข้อมูล
• ขั้นตอนที่ 2: สำรวจข้อกำหนดของงานสำหรับบทบาทนักวิเคราะห์ข้อมูล
• ขั้นตอนที่ 3: ทำความคุ้นเคยกับคณิตศาสตร์และสถิติ
• ขั้นตอนที่ 4: เชี่ยวชาญ Excel สำหรับการวิเคราะห์ข้อมูล
• ขั้นตอนที่ 5: Master SQL สำหรับการดึงข้อมูล
• ขั้นตอนที่ 6: เรียนรู้ Python สำหรับการวิเคราะห์ข้อมูล
• ขั้นตอนที่ 7: ฝึกฝนเครื่องมือสร้างภาพข้อมูลให้เชี่ยวชาญ
• ขั้นตอนที่ 8: สร้างเครือข่ายกับนักวิเคราะห์ข้อมูลและนักพัฒนารายอื่น
• ขั้นตอนที่ 9: อย่าลืมเรื่อง "Soft Skills"
• บทสรุป

ขั้นตอนที่ 1: ทำความรู้จักกับบทบาทของนักวิเคราะห์ข้อมูล

คุณกำลังพิจารณาอาชีพในฐานะนักวิเคราะห์ข้อมูลหรือไม่? มันอัศจรรย์มาก! ลองใช้เวลาสักครู่เพื่อทำความเข้าใจสาระสำคัญของมันก่อนที่จะก้าวไปข้างหน้า

คุณคงเห็นแล้วว่างานของนักวิเคราะห์ข้อมูลเกี่ยวข้องมากกว่าแค่การขบตัวเลขและการสร้างแผนภูมิ (แม้ว่าสิ่งเหล่านั้นจะค่อนข้างดีด้วยซ้ำ) มันเกี่ยวข้องกับการใช้ประโยชน์จากข้อมูลเพื่อค้นหาข้อมูลเชิงลึกและแก้ไขปัญหา ซึ่งหมายถึงการตั้งคำถามที่ถูกต้อง จัดระเบียบและประเมินข้อมูล และอธิบายข้อสรุปของคุณกับผู้อื่น

ทักษะและกิจกรรมหลักบางอย่างที่นักวิเคราะห์ข้อมูลมักจะทำ ได้แก่:

• รวบรวมและวิเคราะห์ชุดข้อมูลขนาดใหญ่เพื่อระบุรูปแบบ แนวโน้ม และข้อมูลเชิงลึกที่สามารถแจ้งการตัดสินใจทางธุรกิจ
• การใช้เครื่องมือและเทคนิคทางสถิติเพื่อดึงข้อมูลเชิงลึกจากข้อมูล
• การพัฒนาและนำระบบการรวบรวมข้อมูลและกลยุทธ์อื่น ๆ มาใช้เพื่อเพิ่มประสิทธิภาพและคุณภาพของข้อมูล
• ทำงานร่วมกับทีมอื่น ๆ เพื่อระบุความต้องการทางธุรกิจและพัฒนาโซลูชันข้อมูลที่ตอบสนองความต้องการเหล่านั้น
• สื่อสารข้อค้นพบและข้อมูลเชิงลึกแก่ผู้มีส่วนได้ส่วนเสียด้วยวิธีที่ชัดเจนและนำไปปฏิบัติได้

ดังนั้น หากคุณไม่ใช่อัจฉริยะทางคณิตศาสตร์หรือเชี่ยวชาญด้านคอมพิวเตอร์ ก็ไม่ต้องกังวล สถิติ วิทยาการคอมพิวเตอร์ และธุรกิจเป็นเพียงไม่กี่สาขาวิชาที่รวมอยู่ในหัวข้อสหสาขาวิชาชีพของการวิเคราะห์ข้อมูล ทุกคนที่สนุกกับการเรียนรู้ การแก้ปัญหา และสร้างความแตกต่างสามารถสนุกกับมันได้จริงๆ

แล้วคุณจะหาข้อมูลเพิ่มเติมเกี่ยวกับสิ่งที่นักวิเคราะห์ข้อมูลทำได้อย่างไร คุณสามารถเริ่มต้นด้วยการดูแหล่งข้อมูลฟรีมากมายที่เข้าถึงได้ทางออนไลน์ ตัวอย่างเช่นนี่คือบทความที่กล่าวถึงสิ่งที่นักวิเคราะห์ข้อมูลทำจริง และ นี่คือบทหนึ่งที่กล่าวถึงบทบาท ของนักวิเคราะห์ข้อมูลเทียบกับบทบาทของวิทยาการข้อมูล

บล็อก พอดแคสต์ และช่อง YouTube จำนวนมากนำเสนอเนื้อหาที่สนุกสนานและให้ข้อมูลเกี่ยวกับการวิเคราะห์ข้อมูล ฉันจะทิ้งช่อง YouTube บางช่องที่ช่วยฉันในช่วงหลายปีที่ผ่านมาด้านล่าง

เพื่อให้เข้าใจถึงทักษะและคุณสมบัติที่จำเป็น คุณยังสามารถสร้างเครือข่ายกับนักวิเคราะห์ข้อมูลคนอื่นๆ ไปประชุมหรือสัมมนา และศึกษารายละเอียดงาน

อย่าลืมพิจารณาว่าการจัดการกับข้อมูลเป็นสิ่งที่คุณชอบหรือไม่ คุณสนุกกับการค้นหารูปแบบและไขปริศนาหรือไม่? คุณต้องการเปลี่ยนแปลงโลกให้ดีขึ้นหรือไม่? การวิเคราะห์ข้อมูลอาจเป็นทางเลือกอาชีพที่เหมาะสำหรับคุณหากคำตอบคือใช่

ขั้นตอนแรกในเส้นทางของคุณคือการทำความเข้าใจว่านักวิเคราะห์ข้อมูลทำอะไร สนุกกับตัวเอง ใช้เวลาของคุณ และอย่าลังเลที่จะถามคำถาม

ขั้นตอนที่ 2: สำรวจข้อกำหนดของงานสำหรับบทบาทนักวิเคราะห์ข้อมูล

เมื่อคุณมีความเข้าใจที่ดีขึ้นเกี่ยวกับบทบาทของนักวิเคราะห์ข้อมูลแล้ว ก็ถึงเวลาที่จะเริ่มมองหาสิ่งที่นายจ้างกำลังมองหา ท้ายที่สุด คุณต้องแน่ใจว่าทักษะและความรู้ของคุณตรงกับความต้องการในตลาดงาน

แต่ก่อนที่คุณจะตื่นตระหนกกับการไม่มีประสบการณ์เพียงพอ โปรดจำไว้ว่าทุกบริษัทมีความแตกต่างกัน บางคนอาจให้ความสำคัญกับทักษะการเขียนโปรแกรม ในขณะที่บางคนอาจให้ความสำคัญกับการสื่อสารและความเฉียบแหลมทางธุรกิจ นั่นเป็นเหตุผลที่การทำวิจัยของคุณเป็นสิ่งสำคัญและค้นหาว่าทักษะและคุณสมบัติเฉพาะใดที่เป็นที่ต้องการมากที่สุดในอุตสาหกรรมหรือบริษัทที่คุณต้องการ

แล้วคุณจะไปหาข้อมูลนี้ได้อย่างไร? จุดเริ่มต้นที่ดีอย่างหนึ่งคือการดูรายการงานและคำอธิบายบนกระดานงานเช่น LinkedIn, Indeed หรือ Glassdoor สิ่งนี้สามารถช่วยให้คุณเข้าใจถึงข้อกำหนดและคุณสมบัติที่สำคัญสำหรับบทบาทนักวิเคราะห์ข้อมูลที่แตกต่างกัน

ตัวอย่างของรายการงานที่อาจขอรวมถึง:

• ความเชี่ยวชาญใน SQL และประสบการณ์การทำงานกับชุดข้อมูลขนาดใหญ่
• ความคุ้นเคยกับ Python และเครื่องมือสร้างภาพข้อมูล เช่น Tableau หรือ Power BI
• ทักษะการวิเคราะห์ที่แข็งแกร่งและความสามารถในการดึงข้อมูลเชิงลึกจากข้อมูลที่ซับซ้อน
• มีประสบการณ์เกี่ยวกับการวิเคราะห์ทางสถิติและเทคนิคการสร้างแบบจำลอง
• ทักษะการสื่อสารที่ยอดเยี่ยมและความสามารถในการอธิบายการค้นพบที่ซับซ้อนแก่ผู้ชมทั้งด้านเทคนิคและไม่ใช่ด้านเทคนิค

แต่อย่าหยุดเพียงแค่นั้น! คุณยังสามารถติดต่อผู้ที่ทำงานในสายงานหรือผู้ที่มีตำแหน่งงานที่คุณสนใจ ถามพวกเขาเกี่ยวกับประสบการณ์ของพวกเขาและทักษะใดที่พวกเขาคิดว่าสำคัญที่สุดสำหรับความสำเร็จในบทบาทของพวกเขา คุณอาจต้องการพิจารณานัดสัมภาษณ์ข้อมูลเพื่อเรียนรู้เพิ่มเติมเกี่ยวกับสาขาและขอคำแนะนำในการเริ่มต้น

และเมื่อพูดถึงการเริ่มต้น สิ่งสำคัญคือต้องจำไว้ว่าไม่มีสิ่งใดทดแทนประสบการณ์จริงได้ แม้ว่าการใช้เวลาทั้งหมดของคุณดูบทช่วยสอนอาจดึงดูดใจ แต่คุณจะเรียนรู้ได้รวดเร็วและมีประสิทธิภาพมากขึ้นด้วยการสร้างสิ่งต่างๆ และทำงานในโครงการวิเคราะห์ข้อมูลจริง

ดังนั้น ใช้เวลาในการสำรวจความต้องการของงาน แต่อย่าลืมสร้างทักษะและรับประสบการณ์จริงอยู่เสมอ ด้วยความพยายามเพียงเล็กน้อยและความอยากรู้อยากเห็นอย่างมาก คุณก็พร้อมที่จะเป็นนักวิเคราะห์ข้อมูลที่ประสบความสำเร็จได้

ขั้นตอนที่ 3: ทำความคุ้นเคยกับคณิตศาสตร์และสถิติ

โอเค ฉันรู้ว่าคุณคิดอะไรอยู่ คณิตศาสตร์และสถิติ ? อ๊ะ!

แต่ฟังฉันก่อนที่คุณจะวิ่งไปที่เนินเขา เพื่อให้นักวิเคราะห์ข้อมูลสามารถเข้าใจข้อมูลและรับข้อมูลเชิงลึกอันมีค่าจากข้อมูลได้ การมีความเข้าใจพื้นฐานของแนวคิดเหล่านี้เป็นสิ่งสำคัญ

ดังนั้นคุณควรคุ้นเคยกับแนวคิดและสูตรทางสถิติพื้นฐานอะไรบ้าง

ในการเริ่มต้น มีการวัดแนวโน้มเข้าสู่ส่วนกลางที่เรียกว่าค่าเฉลี่ย ค่ามัธยฐาน และฐานนิยม ซึ่งสามารถให้แนวคิดเกี่ยวกับค่าทั่วไปในชุดข้อมูลได้ คุณควรรู้วิธีคำนวณ

ค่าเบี่ยงเบนมาตรฐานเป็นตัววัดว่าข้อมูลกระจายจากค่าเฉลี่ยมากน้อยเพียงใด และคุณน่าจะคำนวณได้

และเพื่อที่จะค้นหาความสัมพันธ์ระหว่างตัวแปรและสร้างการคาดคะเนตามความสัมพันธ์เหล่านั้น คุณควรทำความคุ้นเคยกับความสัมพันธ์และการถดถอยด้วย

แต่ไม่ใช่แค่เรื่องตัวเลขเท่านั้น คุณยังต้องมีความเข้าใจพื้นฐานเกี่ยวกับพีชคณิตเชิงเส้น ซึ่งใช้ในแนวทางการวิเคราะห์ข้อมูลมากมาย นี่คือหลักสูตรเชิงลึก (และตำราเรียน)เพื่อให้คุณเริ่มต้นได้

คุณอาจต้องใช้เมทริกซ์เพื่อแก้ไขและจัดการข้อมูล หรือคุณอาจต้องใช้การถดถอยเชิงเส้นเพื่อคาดการณ์มูลค่าในอนาคตตามแนวโน้มในอดีต

หากคุณไม่มีพื้นฐานทางคณิตศาสตร์ที่มั่นคง สิ่งนี้อาจดูยาก แต่ไม่ต้องกังวล! มีแหล่งข้อมูลมากมายที่จะช่วยคุณในการเรียนรู้ของคุณ

ตัวอย่างเช่น Khan Academy มีบทเรียนและแบบฝึกหัดในวิชาคณิตศาสตร์และสถิติ หากคุณชอบหนังสือ "วิทยาศาสตร์ข้อมูลสำหรับผู้เริ่มต้น" โดย Andrew Park เป็นแหล่งข้อมูลที่ยอดเยี่ยมที่ครอบคลุมทั้งหลักการทางสถิติและคณิตศาสตร์ในลักษณะที่เข้าถึงได้

freeCodeCamp กำลังพัฒนาหลักสูตรคณิตศาสตร์ซึ่งคุณสามารถอ่านได้ที่นี่

และนี่คือคำแนะนำเกี่ยวกับสถิติที่คุณจำเป็นต้องรู้เพื่อเข้าสู่วิทยาศาสตร์ข้อมูลและติดตามสาขาต่างๆ เช่น แมชชีนเลิร์นนิง

กุญแจสำคัญคือการเริ่มต้นเล็ก ๆ และสร้างความรู้ของคุณอย่างค่อยเป็นค่อยไป อย่ากลัวที่จะถามคำถามหรือขอความช่วยเหลือเมื่อคุณต้องการ ด้วยการฝึกฝนเพียงเล็กน้อยและความพากเพียร คุณจะพบว่าคณิตศาสตร์และสถิติเป็นเรื่องสนุกจริงๆ (ไม่จริง!)

ขั้นตอนที่ 4: เชี่ยวชาญ Excel สำหรับการวิเคราะห์ข้อมูล

Excel เป็นเครื่องมือสำคัญในคลังแสงของนักวิเคราะห์ข้อมูล มันถูกใช้โดยแทบทุกองค์กรที่นั่น และการเรียนรู้มันจะช่วยให้คุณทำความสะอาด จัดการ และวิเคราะห์ข้อมูลได้อย่างง่ายดาย

ด้วย Excel คุณสามารถสร้างสูตรและฟังก์ชันเพื่อทำการคำนวณ เปลี่ยนตารางและแผนภูมิเพื่อแสดงข้อมูลเป็นภาพ และใช้เครื่องมือวิเคราะห์ข้อมูลเพื่อคาดการณ์และระบุรูปแบบได้ Excel มีประโยชน์อย่างยิ่งสำหรับการวิเคราะห์การถดถอย การพยากรณ์ และการวิเคราะห์สถานการณ์

หากคุณจริงจังกับการเป็นนักวิเคราะห์ข้อมูล จำเป็นอย่างยิ่งที่จะต้องเชี่ยวชาญ Excel โชคดีที่มีแหล่งข้อมูลออนไลน์มากมายที่จะช่วยให้คุณเรียนรู้ได้ ลองดูExcelIsFun , Excel Chandoo , Tutorials Point, Ashutosh KumarและMyOnlineTrainingHubสำหรับบทช่วยสอนบน Youtube นอกจากนี้ หลักสูตรต่อไปนี้จะแนะนำคุณเกี่ยวกับวิธีการใช้ประโยชน์สูงสุดจาก Excel

1. รู้เบื้องต้นเกี่ยวกับการวิเคราะห์ข้อมูลโดยใช้ Excelโดย Coursera
2. การวิเคราะห์ข้อมูลด้วย Python สำหรับผู้ใช้ Excelในช่อง YouTube ของ freeCodeCamp
3. ทักษะ Excel สำหรับความเชี่ยวชาญทางธุรกิจโดย Coursera
4. การวิเคราะห์และการแสดงข้อมูลด้วย Excelโดย EdX

โปรดจำไว้ว่า Excel เป็นเพียงเครื่องมือหนึ่งในชุดเครื่องมือวิเคราะห์ข้อมูลของคุณ แต่เป็นสิ่งสำคัญที่คุณต้องใช้ทุกวันในฐานะนักวิเคราะห์ข้อมูล เมื่อเชี่ยวชาญ Excel คุณจะมีความพร้อมในการจัดการงานที่เกี่ยวข้องกับข้อมูลใดๆ ที่เข้ามา

ตอนนี้ มาดูทักษะถัดไปและทักษะที่สำคัญที่สุดอย่างหนึ่งที่คุณต้องมีในฐานะนักวิเคราะห์ข้อมูล

ขั้นตอนที่ 5: Master SQL สำหรับการดึงข้อมูล

SQL (Structured Query Language) เป็นเครื่องมือสำคัญในการวิเคราะห์ข้อมูล ในฐานะนักวิเคราะห์ข้อมูล ความรับผิดชอบหลักประการหนึ่งของคุณคือการดึงข้อมูลจากฐานข้อมูล และ SQL เป็นภาษาที่ใช้ในการทำเช่นนั้น

SQL เป็นมากกว่าการเรียกใช้คำสั่งพื้นฐาน เช่น SELECT, FROM และ WHERE เป็นภาษาที่ซับซ้อนซึ่งช่วยให้คุณสามารถจัดการและแปลงข้อมูลได้หลายวิธี SQL ใช้สำหรับรวมข้อมูลจากหลายตาราง กรองและรวมข้อมูล และสร้างตารางและมุมมองใหม่

ในการเป็นนักวิเคราะห์ข้อมูลที่มีประสิทธิภาพ จำเป็นอย่างยิ่งที่จะต้องเชี่ยวชาญ SQL คุณควรจะคุ้นเคยกับการเขียนแบบสอบถาม สร้างตาราง และเข้าใจวิธีการปรับแต่งแบบสอบถามของคุณเพื่อประสิทธิภาพ

โชคดีที่มีแหล่งข้อมูลมากมายที่จะช่วยให้คุณเรียนรู้ SQL จุดเริ่มต้นที่ดีคือKhan Academy SQL , W3Schools , SQLZoo , SQLbolt , Luke Barousse , Alex the Analyst , Microsoft Power Toolsและสุดท้ายคือเกม SQL บางเกม เช่น SQL Island และ SQL Murder

นอกจากนี้ยังมีหลักสูตรและหนังสือออนไลน์มากมายที่ครอบคลุม SQL เชิงลึก ต่อไปนี้เป็นข้อมูลบางส่วนที่จะช่วยให้คุณเริ่มต้นได้:

1. เรียนรู้ SQL และฐานข้อมูล - หลักสูตรเต็มรูปแบบสำหรับผู้เริ่มต้น
2. หลักสูตรฐานข้อมูลเชิงสัมพันธ์จาก freeCodeCamp
3. คอลเลกชันของหลักสูตรฐานข้อมูลเชิงสัมพันธ์ฟรี

ด้วยการเรียนรู้ SQL คุณจะสามารถดึงข้อมูลเชิงลึกอันมีค่าจากฐานข้อมูลและจัดการข้อมูลในรูปแบบที่ให้ข้อมูลเชิงลึกทางธุรกิจที่มีความหมาย

ขั้นตอนที่ 6: เรียนรู้ Python สำหรับการวิเคราะห์ข้อมูล

ฉันรู้ว่ามีการคาดเดามากมายว่านักวิเคราะห์ข้อมูลต้องการ Python หรือไม่ – บางคนบอกว่าต้องการในขณะที่บางคนบอกว่าไม่ต้องการ

สำหรับฉัน ฉันอยากจะบอกว่ามันขึ้นอยู่กับบริษัทที่คุณทำงานด้วย แต่มันก็ดีที่จะได้เปรียบด้วยการเรียนรู้ Python เพราะมันเป็นหนึ่งในภาษาโปรแกรมที่ใช้กันแพร่หลายมากที่สุดในโลกของการวิเคราะห์ข้อมูล Python เป็นที่รู้จักในด้านความเรียบง่าย อ่านง่าย และใช้งานได้หลากหลาย ทำให้เป็นตัวเลือกยอดนิยมสำหรับนักวิเคราะห์ข้อมูล

Python มีไลบรารีและเครื่องมือมากมายที่สามารถทำให้การวิเคราะห์ข้อมูลง่ายขึ้น เช่น Pandas สำหรับการจัดการและวิเคราะห์ข้อมูล NumPy สำหรับการคำนวณทางวิทยาศาสตร์ และ Matplotlib สำหรับการแสดงข้อมูล นอกจากนี้ยังมีความสามารถในการทำงานอัตโนมัติทำให้การวิเคราะห์ข้อมูลมีประสิทธิภาพและประสิทธิผลมากขึ้น

การเรียนรู้ Python สำหรับการวิเคราะห์ข้อมูลเป็นการลงทุนที่ยอดเยี่ยมในอาชีพของคุณในฐานะนักวิเคราะห์ข้อมูล ไม่เพียงแต่จะช่วยให้คุณทำงานกับไลบรารีที่ทรงพลังเท่านั้น แต่ยังเปิดโอกาสอีกมากมายในการทำงานกับชุดข้อมูลขนาดใหญ่และการวิเคราะห์ที่ซับซ้อนยิ่งขึ้น

มีแหล่งข้อมูลมากมายที่จะช่วยให้คุณเรียนรู้ Python ตั้งแต่หลักสูตรออนไลน์ฟรีไปจนถึงโปรแกรมและหนังสือเรียนออนไลน์ที่ต้องชำระเงิน แหล่งข้อมูลบางส่วน ได้แก่freeCodeCamp , DataCamp, CodeBasis , การเขียนโปรแกรมด้วย MoshและMicrosoft Learn

ด้วยการเรียนรู้ Python คุณจะสามารถทำการวิเคราะห์ข้อมูลที่ซับซ้อนมากขึ้น ทำงานอัตโนมัติ และทำงานกับชุดข้อมูลที่หลากหลายมากขึ้น ทำให้คุณกลายเป็นทรัพย์สินที่มีค่าในองค์กรที่เน้นข้อมูล

ขั้นตอนที่ 7: ฝึกฝนเครื่องมือสร้างภาพข้อมูลให้เชี่ยวชาญ

ในฐานะนักวิเคราะห์ข้อมูล สิ่งสำคัญคือต้องสามารถสื่อสารสิ่งที่คุณค้นพบในลักษณะที่ชัดเจนและรัดกุม วิธีหนึ่งในการทำเช่นนี้คือการแสดงข้อมูลเป็นภาพ เครื่องมือการแสดงภาพข้อมูล เช่นPowerBIและ Tableau สามารถช่วยคุณสร้างแผนภูมิแบบโต้ตอบ กราฟ และแดชบอร์ดที่ทำให้ผู้อื่นเข้าใจสิ่งที่คุณค้นพบได้ง่าย เราจะพูดถึงพวกเขาอีกสักครู่

นี่คือหลักสูตร Tableau สำหรับ Data Science และ Data Visualizationที่คุณสามารถตรวจสอบได้

แม้ว่า SQL นั้นยอดเยี่ยมสำหรับการสืบค้นและจัดการข้อมูล แต่ก็ไม่สามารถทำให้ข้อมูลของคุณมีชีวิตชีวาได้อย่างเต็มที่ นี่คือที่มาของเครื่องมือการแสดงข้อมูล เครื่องมือเหล่านี้ช่วยให้คุณสามารถแปลงข้อมูลของคุณให้เป็นการแสดงภาพข้อมูลเชิงลึกและเข้าใจง่ายที่สามารถแชร์กับผู้มีส่วนได้ส่วนเสียได้

คุณสามารถเรียนรู้พื้นฐานการแสดงภาพข้อมูลได้ในหลักสูตรฟรีเชิงลึกในช่อง YouTube ของ freeCodeCamp

PowerBI เป็นตัวเลือกที่ยอดเยี่ยมสำหรับการแสดงภาพข้อมูล เนื่องจากง่ายต่อการเรียนรู้และรวมเข้ากับผลิตภัณฑ์อื่นๆ ของ Microsoft ทำให้เป็นเครื่องมือที่เหมาะสำหรับองค์กรที่ใช้ Microsoft Office Tableau ยังเป็นตัวเลือกยอดนิยมและมีชุมชนผู้ใช้ที่แข็งแกร่งและคุณสมบัติที่หลากหลาย

การเรียนรู้เครื่องมือการแสดงข้อมูลอย่างเช่น PowerBI หรือ Tableau จะช่วยให้คุณสร้างการแสดงข้อมูลที่น่าสนใจ ซึ่งช่วยให้คุณเข้าใจข้อมูลของคุณได้ดีขึ้นและสื่อสารสิ่งที่คุณค้นพบกับผู้อื่น มีหลักสูตรและบทช่วยสอนออนไลน์มากมายที่จะช่วยให้คุณเรียนรู้เครื่องมือเหล่านี้ เช่น การฝึกอบรมอย่างเป็นทางการของMicrosoft PowerBIและหลักสูตรการฝึกอบรมของ Tableau

คุณจะสามารถสร้างการแสดงข้อมูลแบบอินเทอร์แอกทีฟและมีส่วนร่วม ซึ่งจะช่วยให้คุณเข้าใจข้อมูลของคุณได้ดีขึ้นและสื่อสารสิ่งที่คุณค้นพบกับผู้อื่น ทำให้คุณเป็นทรัพย์สินที่มีค่าสำหรับองค์กรที่เน้นข้อมูล

คุณยังสามารถดำดิ่งสู่เครื่องมือ ข้อมูลยอดนิยมอื่นๆ เช่น D3.js นี่คือหลักสูตรที่จะช่วยคุณเริ่มต้น

ขั้นตอนที่ 8: สร้างเครือข่ายกับนักวิเคราะห์ข้อมูลและนักพัฒนารายอื่น

ระบบเครือข่ายเป็นส่วนสำคัญของทุกอาชีพ และการวิเคราะห์ข้อมูลก็ไม่มีข้อยกเว้น ด้วยการสร้างเครือข่ายกับนักวิเคราะห์ข้อมูลและนักพัฒนา คุณสามารถเรียนรู้จากประสบการณ์ของพวกเขา รับข้อมูลเชิงลึกเกี่ยวกับแนวโน้มอุตสาหกรรมและเทคโนโลยีล่าสุด และโอกาสในการทำงาน

ต่อไปนี้เป็นวิธีสร้างเครือข่ายกับผู้อื่นในภาคสนาม:

1. เข้าร่วมกิจกรรมทางอุตสาหกรรม: มองหาการประชุม การพบปะ และกิจกรรมอื่นๆ ที่เกี่ยวข้องกับการวิเคราะห์ข้อมูลและเข้าร่วม นี่เป็นวิธีที่ยอดเยี่ยมในการพบปะผู้อื่นในสายงานและเรียนรู้เกี่ยวกับการพัฒนาและเทคโนโลยีใหม่ๆ
2. เข้าร่วมชุมชนออนไลน์: มีชุมชนออนไลน์มากมายสำหรับนักวิเคราะห์ข้อมูลและนักพัฒนา เช่น ฟอรัม กลุ่ม LinkedIn และกลุ่มโซเชียลมีเดีย เข้าร่วมชุมชนเหล่านี้และมีส่วนร่วมในการสนทนาเพื่อเชื่อมต่อกับผู้อื่นในภาคสนาม
3. เข้าถึงผู้อื่น: อย่ากลัวที่จะติดต่อกับนักวิเคราะห์ข้อมูลและนักพัฒนาคนอื่นๆ ไม่ว่าจะผ่านโซเชียลมีเดีย อีเมล หรือต่อหน้า แนะนำตัว ขอคำแนะนำ และสร้างความสัมพันธ์

โปรดจำไว้ว่าเครือข่ายเป็นถนนสองทาง ยินดีที่จะให้ความช่วยเหลือและคำแนะนำแก่ผู้อื่นในสาขานี้เช่นกัน ด้วยการสร้างเครือข่ายผู้ติดต่อที่แข็งแกร่งในฟิลด์การวิเคราะห์ข้อมูล คุณจะสามารถเพิ่มโอกาสในการทำงานและติดตามเทรนด์และเทคโนโลยีล่าสุดของอุตสาหกรรมได้

ขั้นตอนที่ 9: อย่าลืมเรื่อง "Soft Skills"

ทักษะสุดท้ายอย่างหนึ่งที่ฉันคิดว่าจำเป็นต้องฝึกฝนก่อนที่คุณจะเป็น DA ที่ดีได้คือทักษะด้านอารมณ์ของคุณ ซึ่งเกี่ยวข้องกับความสามารถในการสื่อสาร การแก้ปัญหา ฯลฯ

หลังจากพูดและทำเสร็จแล้ว ฝึกฝนและฝึกฝนและสร้างโครงการ

บทสรุป.

การเป็นนักวิเคราะห์ข้อมูลต้องอาศัยความทุ่มเท การทำงานอย่างหนัก และความหลงใหลในการวิเคราะห์ข้อมูล การทำตามขั้นตอนที่ระบุไว้ในแผนงานนี้จะช่วยให้คุณได้รับทักษะและความรู้ที่จำเป็นในการเป็นนักวิเคราะห์ข้อมูลที่ประสบความสำเร็จ

ตั้งแต่การทำความเข้าใจบทบาทของนักวิเคราะห์ข้อมูล ไปจนถึงการเรียนรู้ SQL และ Python ไปจนถึงการสร้างเครือข่ายกับนักพัฒนารายอื่น แต่ละขั้นตอนมีความสำคัญต่อการบรรลุความสำเร็จในด้านนี้

อย่าลืมอยากรู้อยากเห็น อย่าหยุดเรียนรู้ และเต็มใจที่จะปรับตัวให้เข้ากับเทคโนโลยีและวิธีการใหม่ๆ อยู่เสมอ ด้วยความมุ่งมั่นและความอุตสาหะ คุณสามารถบรรลุเป้าหมายในการเป็นนักวิเคราะห์ข้อมูลที่เชี่ยวชาญและปลดล็อกโลกแห่งโอกาสในการทำงานที่น่าตื่นเต้น

ที่มา: https://www.freecodecamp.org

#dataanalytics

Roadmap per diventare un Data Analytics

La roadmap completa per diventare un Data Analytics senza alcuna esperienza precedente. Seguire i passaggi delineati in questa tabella di marcia ti aiuterà ad acquisire le competenze e le conoscenze necessarie per diventare un analista di dati di successo. Dalla comprensione del ruolo di un analista di dati, alla padronanza di SQL e Python, al networking con altri sviluppatori, ogni passaggio è fondamentale per raggiungere il successo in questo campo.

Ciao e benvenuto nel mondo dell'analisi dei dati! Se stai considerando una carriera in questo campo, sei in buona compagnia. L'analisi dei dati è un campo in crescita ed entusiasmante che sta diventando sempre più importante nel mondo odierno basato sui dati.

Ammettiamolo, stiamo tutti annegando nei dati in questi giorni. Dai post sui social media alle transazioni finanziarie alle cartelle cliniche, non mancano le informazioni da esaminare.

È qui che entrano in gioco gli analisti di dati. Sono quelli che ci aiutano a dare un senso a tutti quei dati e a trasformarli in informazioni preziose.

E queste intuizioni possono cambiare il gioco. Possono aiutare le aziende a migliorare i loro prodotti e servizi, i governi a prendere decisioni politiche più informate e gli individui a fare scelte migliori nella loro vita personale e professionale.

Ma non si tratta solo dell'impatto. L'analisi dei dati può anche essere molto redditizia. Secondo studi recenti, lo stipendio medio di un analista di dati negli Stati Uniti è di circa $ 70.000 all'anno e tale cifra può salire ancora di più con l'esperienza e la competenza.

Ovviamente, come ogni professione, l'analisi dei dati ha le sue sfide. C'è l'occasionale set di dati che induce mal di testa, la minaccia sempre presente della sindrome dell'impostore e gli infiniti dibattiti sul miglior linguaggio di programmazione o strumento di visualizzazione dei dati. Ma hey, se sei pronto per una sfida, questo potrebbe essere il campo che fa per te.

Quindi, se stai cercando di diventare un analista di dati, questo articolo fa per te. Si spera che ti faccia risparmiare un sacco di tempo e fatica e non devi sprecare il tuo tempo imparando un sacco di cose irrilevanti come ho fatto una volta.

Sommario:

• Passaggio 1: conoscere il ruolo di un analista di dati
• Passaggio 2: esplorare i requisiti di lavoro per i ruoli di analista di dati
• Passaggio 3: prendi confidenza con la matematica e le statistiche
• Passaggio 4: Master Excel per l'analisi dei dati
• Passaggio 5: Master SQL per l'estrazione dei dati
• Passaggio 6: impara Python per l'analisi dei dati
• Passaggio 7: padroneggia uno strumento di visualizzazione dei dati
• Passaggio 8: fare rete con altri analisti e sviluppatori di dati
• Passaggio 9: non dimenticare le "competenze trasversali"
• Conclusione

Passaggio 1: conoscere il ruolo di un analista di dati

Stai considerando una carriera come analista di dati? È fantastico! Prendiamoci un momento per coglierne l'essenza prima di andare avanti.

Vedete, il lavoro di un analista di dati implica qualcosa di più del semplice sgranocchiare cifre e creare grafici (anche se anche queste cose possono essere piuttosto interessanti). Implica lo sfruttamento dei dati per trovare approfondimenti e risolvere i problemi. Ciò significa formulare le domande corrette, organizzare e valutare i dati e spiegare le proprie conclusioni agli altri.

Alcune competenze e attività fondamentali che un analista di dati svolge in genere includono:

• Raccolta e analisi di set di dati di grandi dimensioni per identificare modelli, tendenze e approfondimenti che possono informare le decisioni aziendali.
• Utilizzo di strumenti e tecniche statistici per trarre informazioni dai dati.
• Sviluppo e implementazione di sistemi di raccolta dati e altre strategie che ottimizzano l'efficienza e la qualità dei dati.
• Collaborare con altri team per identificare le esigenze aziendali e sviluppare soluzioni di dati che le risolvano.
• Comunicare risultati e approfondimenti alle parti interessate in modo chiaro e attuabile.

Quindi, se non sei un prodigio della matematica o un mago del computer, non preoccuparti. Statistica, informatica e business sono solo alcune delle numerose discipline che sono incorporate nell'argomento multidisciplinare dell'analisi dei dati. Tutti coloro che amano imparare, risolvere problemi e fare la differenza possono davvero divertirsi.

Quindi, come puoi scoprire di più su ciò che fa un analista di dati? Puoi iniziare esaminando le numerose risorse gratuite accessibili online. Ad esempio, qui un articolo che discute cosa fanno effettivamente gli analisti di dati . Ed eccone uno che discute i ruoli dell'analista di dati rispetto alla scienza dei dati .

Molti blog, podcast e canali YouTube offrono contenuti divertenti e informativi sul tema dell'analisi dei dati. Lascerò cadere alcuni canali YouTube che mi hanno aiutato negli anni seguenti.

Per farti un'idea delle competenze e delle qualità necessarie, puoi anche fare rete con altri analisti di dati, partecipare a riunioni o seminari e studiare le descrizioni dei lavori.

Non dimenticare di considerare se gestire i dati è qualcosa che ti piace. Ti piace trovare schemi e risolvere enigmi? Vuoi cambiare il mondo in meglio? L'analisi dei dati può essere la scelta professionale ideale per te se la risposta è sì.

Il primo passo nel tuo percorso è capire cosa fa un analista di dati. Divertiti, prenditi il ​​tuo tempo e non esitare a fare domande.

Passaggio 2: esplorare i requisiti di lavoro per i ruoli di analista di dati

Ora che hai una migliore comprensione del ruolo di un analista di dati, è il momento di iniziare a esaminare ciò che i datori di lavoro stanno cercando. Dopotutto, vuoi assicurarti che le tue capacità e conoscenze corrispondano a ciò che è richiesto nel mercato del lavoro.

Ma prima di iniziare a farti prendere dal panico per non avere abbastanza esperienza, ricorda che ogni azienda è diversa. Alcuni possono dare la priorità alle capacità di programmazione, mentre altri possono apprezzare la comunicazione e il senso degli affari. Ecco perché è importante fare le tue ricerche e scoprire quali competenze e qualifiche specifiche sono più richieste nel settore o nell'azienda che desideri.

Quindi, come si fa a trovare queste informazioni? Bene, un ottimo punto di partenza è controllare gli annunci di lavoro e le descrizioni su bacheche di lavoro come LinkedIn, Indeed o Glassdoor. Questo può darti un'idea dei requisiti e delle qualifiche chiave per i diversi ruoli di analista di dati.

Alcuni esempi di ciò che gli annunci di lavoro potrebbero richiedere includono:

• Competenza in SQL ed esperienza di lavoro con set di dati di grandi dimensioni
• Familiarità con Python e strumenti di visualizzazione dei dati come Tableau o Power BI
• Forti capacità analitiche e capacità di trarre intuizioni da dati complessi
• Esperienza con analisi statistiche e tecniche di modellazione
• Eccellenti capacità di comunicazione e capacità di spiegare risultati complessi a un pubblico sia tecnico che non tecnico

Ma non fermarti qui! Puoi anche contattare persone che lavorano nel settore o che hanno titoli di lavoro che ti interessano. Chiedi loro della loro esperienza e quali abilità ritengono più importanti per il successo nel loro ruolo. Potresti anche prendere in considerazione l'idea di organizzare colloqui informativi per saperne di più sul campo e ottenere consigli su come iniziare.

E parlando di come iniziare, è importante ricordare che nulla può sostituire l'esperienza pratica. Per quanto allettante possa essere passare tutto il tempo a guardare i tutorial, imparerai molto più rapidamente ed efficacemente costruendo effettivamente cose e lavorando su progetti di analisi dei dati reali.

Quindi prenditi del tempo per esplorare i requisiti del lavoro, ma non dimenticare di continuare a sviluppare le tue capacità e acquisire esperienza pratica. Con un piccolo sforzo e molta curiosità, sarai sulla buona strada per diventare un analista di dati di successo.

Passaggio 3: prendi confidenza con la matematica e le statistiche

Ok, so cosa stai pensando. Matematica e statistica? Accidenti!

Ma ascoltami prima di correre verso le colline. Affinché un analista di dati sia in grado di dare un senso ai dati e ricavarne preziose informazioni, è essenziale avere una comprensione fondamentale di questi concetti.

Quindi quali concetti e formule statistici fondamentali dovresti conoscere?

Ora, per iniziare, ci sono misure di tendenza centrale note come media, mediana e moda, che possono darti un'idea del valore tipico in un set di dati. Dovresti sapere come calcolarli.

La deviazione standard è una misura dell'ampiezza della distribuzione dei dati rispetto alla media e dovresti essere in grado di calcolarla.

E per trovare relazioni tra variabili e generare previsioni basate su tali associazioni, dovresti anche avere familiarità con la correlazione e la regressione.

Ma non si tratta solo di numeri. Avrai anche bisogno di una conoscenza rudimentale dell'algebra lineare, che viene impiegata in molti approcci di analisi dei dati. Ecco un corso approfondito (e un libro di testo) per iniziare.

Potrebbe essere necessario utilizzare matrici per modificare e manipolare i dati oppure potrebbe essere necessario utilizzare la regressione lineare per prevedere i valori futuri in base alle tendenze storiche.

Se non hai un solido background in matematica, questo può sembrare difficile. Ma non preoccuparti! Ci sono numerose risorse disponibili per assisterti nel tuo apprendimento.

Ad esempio, Khan Academy offre lezioni e attività pratiche in matematica e statistica. Se preferisci i libri, "Data Science for Beginners" di Andrew Park è un'eccellente risorsa che copre sia i principi statistici che quelli matematici in modo accessibile.

freeCodeCamp sta sviluppando un curriculum di matematica di cui puoi leggere qui .

Ed ecco una guida sulle statistiche che devi conoscere per entrare nella scienza dei dati e perseguire campi come il Machine Learning.

La chiave è iniziare in piccolo e sviluppare gradualmente le tue conoscenze. Non aver paura di fare domande o cercare aiuto quando ne hai bisogno. Con un po' di pratica e perseveranza, scoprirai presto che la matematica e le statistiche sono in realtà piuttosto divertenti (no, sul serio!).

Passaggio 4: Master Excel per l'analisi dei dati

Excel è uno strumento fondamentale nell'arsenale di un analista di dati. È utilizzato praticamente da tutte le organizzazioni là fuori e padroneggiarlo ti aiuterà a pulire, manipolare e analizzare i dati con facilità.

Con Excel puoi creare formule e funzioni per eseguire calcoli, tabelle pivot e grafici per visualizzare i dati e utilizzare strumenti di analisi dei dati per fare previsioni e identificare modelli. Excel è particolarmente utile per l'analisi di regressione, le previsioni e l'analisi degli scenari.

Se sei seriamente intenzionato a diventare un analista di dati, è essenziale padroneggiare Excel. Fortunatamente, ci sono molte risorse online disponibili per aiutarti a imparare. Dai un'occhiata a ExcelIsFun , Excel Chandoo , Tutorials Point, Ashutosh Kumar e MyOnlineTrainingHub per i tutorial su Youtube. Inoltre, i seguenti corsi ti guideranno su come ottenere il massimo da Excel.

1. Introduzione all'analisi dei dati utilizzando Excel di Coursera
2. Analisi dei dati con Python per gli utenti di Excel sul canale YouTube di freeCodeCamp
3. Competenze Excel per la specializzazione aziendale di Coursera
4. Analisi e visualizzazione dei dati con Excel di EdX

Ricorda, Excel è solo uno strumento nel tuo toolkit di analisi dei dati. Ma è cruciale che utilizzerai quotidianamente come analista di dati. Padroneggiando Excel, sarai ben attrezzato per gestire qualsiasi attività relativa ai dati che ti viene incontro.

Ora passiamo all'abilità successiva e anche a una delle abilità più importanti di cui avrai bisogno come analista di dati.

Passaggio 5: Master SQL per l'estrazione dei dati

SQL (Structured Query Language) è uno strumento fondamentale nell'analisi dei dati. In qualità di analista di dati, una delle tue responsabilità principali è estrarre i dati dai database e SQL è il linguaggio utilizzato per farlo.

SQL è molto più che eseguire query di base come SELECT, FROM e WHERE. È un linguaggio complesso che ti consente di manipolare e trasformare i dati in innumerevoli modi. SQL viene utilizzato per unire i dati da più tabelle, filtrare e aggregare i dati e creare nuove tabelle e viste.

Per essere un analista di dati efficace, è essenziale padroneggiare SQL. Dovresti essere a tuo agio con la scrittura di query, la creazione di tabelle e la comprensione di come ottimizzare le tue query per le prestazioni.

Fortunatamente, ci sono molte risorse disponibili per aiutarti ad imparare SQL. Alcuni ottimi punti di partenza sono Khan Academy SQL , W3Schools, SQLZoo, SQLbolt , Luke Barousse , Alex the Analyst , Microsoft Power Tools e infine alcuni giochi SQL come SQL island e SQL Murder.

Inoltre, sono disponibili molti corsi e libri online che trattano SQL in modo approfondito. Eccone alcuni per iniziare:

1. Impara SQL e database - Corso completo per principianti
2. Curriculum di database relazionale da freeCodeCamp
3. Raccolta di corsi gratuiti sui database relazionali

Padroneggiando SQL, sarai in grado di estrarre informazioni preziose dai database e manipolare i dati in modi che forniscono informazioni aziendali significative.

Passaggio 6: impara Python per l'analisi dei dati

So che ci sono molte speculazioni sul fatto che un analista di dati abbia o meno bisogno di Python: alcuni dicono di sì mentre altri dicono di no.

Per me, vorrei dire che dipende dall'azienda per cui lavori, ma è bello avere un vantaggio imparando Python in quanto è uno dei linguaggi di programmazione più utilizzati nel mondo dell'analisi dei dati. Python è noto per la sua semplicità, leggibilità e versatilità, rendendolo una scelta popolare per gli analisti di dati.

Python ha una vasta gamma di librerie e strumenti che possono semplificare l'analisi dei dati, come Panda per la manipolazione e l'analisi dei dati, NumPy per il calcolo scientifico e Matplotlib per la visualizzazione dei dati. Ha anche la capacità di automatizzare le attività , rendendo l'analisi dei dati più efficiente ed efficace.

Imparare Python per l'analisi dei dati è un grande investimento nella tua carriera di analista di dati. Non solo ti consentirà di lavorare con potenti librerie, ma aprirà anche molte opportunità per lavorare con set di dati più grandi e analisi più complesse.

Ci sono molte risorse disponibili per aiutarti a imparare Python, dai corsi online gratuiti ai programmi e libri di testo online a pagamento. Alcune risorse includono freeCodeCamp , DataCamp, CodeBasis , Programming with Mosh e Microsoft Learn.

Imparando Python, sarai in grado di eseguire analisi dei dati più complesse, automatizzare le attività e lavorare con una gamma più ampia di set di dati, rendendoti una risorsa preziosa in qualsiasi organizzazione incentrata sui dati.

Passaggio 7: padroneggia uno strumento di visualizzazione dei dati

In qualità di analista di dati, è essenziale essere in grado di comunicare i propri risultati in modo chiaro e conciso. Un modo per farlo è attraverso la visualizzazione dei dati. Strumenti di visualizzazione dei dati come PowerBI e Tableau possono aiutarti a creare diagrammi, grafici e dashboard interattivi che consentono agli altri di comprendere facilmente i tuoi risultati. Ne parleremo di più tra un minuto.

Ecco un corso di Tableau per la scienza dei dati e la visualizzazione dei dati a cui puoi dare un'occhiata.

Sebbene SQL sia ottimo per interrogare e manipolare i dati, non può dare piena vita ai tuoi dati. È qui che entra in gioco uno strumento di visualizzazione dei dati. Questi strumenti ti consentono di trasformare i tuoi dati in visualizzazioni approfondite e di facile comprensione che possono essere condivise con le parti interessate.

Puoi imparare le basi della visualizzazione dei dati in questo corso gratuito approfondito sul canale YouTube di freeCodeCamp.

PowerBI è un'ottima scelta per la visualizzazione dei dati in quanto è facile da apprendere e da integrare con altri prodotti Microsoft. Questo lo rende uno strumento ideale per le organizzazioni che utilizzano Microsoft Office. Tableau è anche una scelta popolare e ha una forte comunità di utenti e un'ampia gamma di funzionalità.

L'apprendimento di uno strumento di visualizzazione dei dati come PowerBI o Tableau ti consentirà di creare visualizzazioni accattivanti che ti aiuteranno a comprendere meglio i tuoi dati e a comunicare le tue scoperte agli altri. Sono disponibili molti corsi e tutorial online per aiutarti ad apprendere questi strumenti, come la formazione ufficiale su Microsoft PowerBI e i corsi di formazione di Tableau .

Padroneggiando uno strumento di visualizzazione dei dati, sarai in grado di creare visualizzazioni interattive e coinvolgenti che ti aiuteranno a comprendere meglio i tuoi dati e a comunicare le tue scoperte agli altri, rendendoti una risorsa inestimabile per qualsiasi organizzazione incentrata sui dati.

Puoi anche immergerti in altri popolari strumenti di visualizzazione dei dati come D3.js: ecco un corso su di esso per iniziare .

Passaggio 8: fare rete con altri analisti e sviluppatori di dati

Il networking è una parte essenziale di qualsiasi professione e l'analisi dei dati non fa eccezione. Collegandoti in rete con altri analisti e sviluppatori di dati, puoi imparare dalle loro esperienze, ottenere approfondimenti sulle ultime tendenze e tecnologie del settore e potenzialmente trovare opportunità di lavoro.

Ecco alcuni modi per entrare in rete con altri sul campo:

1. Partecipa agli eventi del settore: cerca conferenze, incontri e altri eventi relativi all'analisi dei dati e partecipa. Questo è un ottimo modo per incontrare altri sul campo e conoscere nuovi sviluppi e tecnologie.
2. Unisciti alle community online: esistono molte community online per analisti e sviluppatori di dati, come forum, gruppi di LinkedIn e gruppi di social media. Unisciti a queste comunità e partecipa alle discussioni per entrare in contatto con gli altri sul campo.
3. Raggiungi gli altri: non aver paura di contattare altri analisti e sviluppatori di dati, tramite social media, e-mail o di persona. Presentati, chiedi consigli e costruisci relazioni.

Ricorda, il networking è una strada a doppio senso. Sii disposto a offrire aiuto e consigli anche ad altri sul campo. Costruendo una solida rete di contatti nel campo dell'analisi dei dati, puoi migliorare le tue opportunità di carriera e rimanere aggiornato sulle ultime tendenze e tecnologie del settore.

Passaggio 9: non dimenticare le "competenze trasversali"

Un'ultima abilità su cui penso debba essere lavorata prima che tu possa essere un ottimo procuratore distrettuale sono le tue abilità trasversali che implicano la tua capacità di comunicare, risolvere problemi ecc.

Dopo tutto è stato detto e fatto, pratica e pratica e costruisci progetti.

Conclusione.

Diventare un analista di dati richiede dedizione, duro lavoro e passione per l'analisi dei dati. Seguire i passaggi delineati in questa tabella di marcia ti aiuterà ad acquisire le competenze e le conoscenze necessarie per diventare un analista di dati di successo.

Dalla comprensione del ruolo di un analista di dati, alla padronanza di SQL e Python, al networking con altri sviluppatori, ogni passaggio è fondamentale per raggiungere il successo in questo campo.

Ricorda di rimanere curioso, di non smettere mai di imparare e di essere sempre disposto ad adattarti alle nuove tecnologie e metodologie. Con determinazione e perseveranza, puoi raggiungere il tuo obiettivo di diventare un abile analista di dati e sbloccare un mondo di entusiasmanti opportunità di carriera.

Fonte: https://www.freecodecamp.org

#dataanalytics

Roadmap to Becoming a Data Analytics

The complete roadmap to becoming a Data Analytics with no previous experience. Following the steps outline in this roadmap will help you gain the necessary skills and knowledge to become a successful data analyst. From understanding the role of a data analyst, to mastering SQL and Python, to networking with other developers, each step is crucial to achieving success in this field.

Hello and welcome to the world of data analysis! If you're considering a career in this field, you're in good company. Data analysis is a growing and exciting field that's becoming increasingly important in today's data-driven world.

Let's face it, we're all drowning in data these days. From social media posts to financial transactions to medical records, there's no shortage of information to sift through.

That's where data analysts come in. They're the ones who help us make sense of all that data and turn it into valuable insights.

And those insights can be game-changing. They can help businesses improve their products and services, governments make more informed policy decisions, and individuals make better choices in their personal and professional lives.

But it's not just about the impact. Data analysis can also be quite lucrative. According to recent studies, the median salary for a data analyst in the US is around $70,000 per year, and that number can climb even higher with experience and expertise.

Of course, like any profession, data analysis has its challenges. There's the occasional headache-inducing data set, the ever-present threat of imposter syndrome, and the endless debates over the best programming language or data visualization tool. But hey, if you're up for a challenge, this could be the field for you.

So if you're trying to be a Data Analyst, this article is for you. Hopefully, it saves you a lot of time and effort and you don't have to waste your time learning a whole lot of irrelevant things like I once did.

Table of contents:

• Step 1: Get to Know the Role of a Data Analyst
• Step 2: Explore Job Requirements for Data Analyst Roles
• Step 3: Get Comfortable with Math and Statistics
• Step 4: Master Excel for Data Analysis
• Step 5: Master SQL for Data Extraction
• Step 6: Learn Python for Data Analysis
• Step 7: Master a Data Visualization Tool
• Step 8: Network with Other Data Analysts and Developers
• Step 9: Don't Forget about "Soft Skills"
• Conclusion

Step 1: Get to Know the Role of a Data Analyst

Are you considering a career as a data analyst? That's fantastic! Let's take a moment to grasp the gist of it before moving forward.

You see, a data analyst's job involves more than just crunching figures and creating charts (although those things can be pretty cool, too). It involves leveraging data to find insights and address issues. This means coming up with the correct questions, organizing and evaluating the data, and explaining your conclusions to others.

Some core skills and activities that a data analyst typically performs include:

• Collecting and analyzing large data sets to identify patterns, trends, and insights that can inform business decisions.
• Using statistical tools and techniques to draw insights from data.
• Developing and implementing data collection systems and other strategies that optimize efficiency and data quality.
• Collaborating with other teams to identify business needs and develop data solutions that address them.
• Communicating findings and insights to stakeholders in a clear and actionable way.

So if you're not a math prodigy or a computer whiz, don't worry. Statistics, computer science, and business are just a few of the numerous disciplines that are incorporated into the multidisciplinary topic of data analysis. Everyone who enjoys learning, solving problems, and making a difference can really enjoy it.

So how can you find out more about what a data analyst does? You can start by looking through the numerous free resources that are accessible online. For example, here an article that discusses what data analysts actually do. And here's one that discusses data analyst vs data science roles.

Many blogs, podcasts, and YouTube channels offer entertaining and informative content on the subject of data analysis. I will be dropping some YouTube channels that have helped me over the years below.

To gain a feel of the skills and qualities needed, you can also network with other data analysts, go to meetings or seminars, and study job descriptions.

Don't forget to consider whether dealing with data is something you enjoy. Do you enjoy finding patterns and solving puzzles? Do you want to change the world for the better? Data analysis may be the ideal career choice for you if the answer is yes.

The first step in your path is to understand what a data analyst does. Enjoy yourself, take your time, and don't be hesitant to ask questions.

Step 2: Explore Job Requirements for Data Analyst Roles

Now that you have a better understanding of the role of a data analyst, it's time to start looking at what employers are looking for. After all, you want to make sure that your skills and knowledge match up with what's required in the job market.

But before you start panicking about not having enough experience, remember that every company is different. Some may prioritize programming skills, while others may value communication and business acumen. That's why it's important to do your research and find out what specific skills and qualifications are most in demand in your desired industry or company.

So how do you go about finding this information? Well, one great place to start is by checking out job listings and descriptions on job boards like LinkedIn, Indeed, or Glassdoor. This can give you a good sense of the key requirements and qualifications for different data analyst roles.

Some examples of what job listings might ask for include:

• Proficiency in SQL and experience working with large datasets
• Familiarity with Python and data visualization tools like Tableau or Power BI
• Strong analytical skills and the ability to draw insights from complex data
• Experience with statistical analysis and modeling techniques
• Excellent communication skills and the ability to explain complex findings to both technical and non-technical audiences

But don't stop there! You can also reach out to people who work in the field or who have job titles that interest you. Ask them about their experience and what skills they think are most important for success in their role. You might even want to consider setting up informational interviews to learn more about the field and get advice on how to get started.

And speaking of getting started, it's important to remember that there's no substitute for hands-on experience. As tempting as it may be to spend all your time watching tutorials, you'll learn much more quickly and effectively by actually building things and working on real data analysis projects.

So take some time to explore job requirements, but don't forget to keep building your skills and gaining practical experience. With a little effort and a lot of curiosity, you'll be well on your way to becoming a successful data analyst.

Step 3: Get Comfortable with Math and Statistics

Okay, I know what you're thinking. Math and statistics? Yikes!

But hear me out before you run for the hills. For a data analyst to be able to make sense of data and derive valuable insights from it, having a fundamental understanding of these concepts is essential.

So what fundamental statistical concepts and formulas should you be familiar with?

Now, to start, there are measures of central tendency known as mean, median, and mode, which can give you an idea of the typical value in a dataset. You should know how to calculate them.

The standard deviation is a measure of how widely distributed the data are from the average, and you should be able to calculate it.

And in order to find relationships between variables and generate predictions based on those associations, you should also be familiar with correlation and regression.

But it's not just about numbers. You'll also need a rudimentary understanding of linear algebra, which is employed in many data analysis approaches. Here's an in-depth course (and textbook) to get you started.

You may need to employ matrices to modify and manipulate data, or you may need to use linear regression to forecast future values based on historical trends.

If you don't have a solid math background, this may seem difficult. But don't be concerned! There are numerous resources available to assist you in your learning.

For example, Khan Academy offers lessons and practice tasks in math and statistics. If you prefer books, "Data Science for Beginners" by Andrew Park is an excellent resource that covers both statistical and mathematical principles in an accessible manner.

freeCodeCamp is developing a math curriculum which you can read about here.

And here's a guide on the statistics you need to know to get into data science and pursue fields like Machine Learning.

The key is to start small and build up your knowledge gradually. Don't be afraid to ask questions or seek help when you need it. With a little practice and persistence, you'll soon find that math and statistics are actually kind of fun (no, seriously!).

Step 4: Master Excel for Data Analysis

Excel is a vital tool in a data analyst's arsenal. It's used by virtually every organization out there, and mastering it will help you clean, manipulate, and analyze data with ease.

With Excel, you can create formulas and functions to perform calculations, pivot tables and charts to visualize data, and use data analysis tools to make predictions and identify patterns. Excel is particularly useful for regression analysis, forecasting, and scenario analysis.

If you're serious about becoming a data analyst, it's essential to master Excel. Fortunately, there are plenty of online resources available to help you learn. Check out ExcelIsFun, Excel Chandoo, Tutorials Point, Ashutosh Kumar , and MyOnlineTrainingHub for tutorials on Youtube. Also, the following courses will guide you on how to get the most out of Excel.

1. Introduction to Data Analysis Using Excel by Coursera
2. Data Analysis with Python for Excel Users on freeCodeCamp's YouTube channel
3. Excel Skills for Business Specialization by Coursera
4. Analyzing and Visualizing Data with Excel by EdX

Remember, Excel is just one tool in your data analysis toolkit. But it's a crucial one that you'll use daily as a data analyst. By mastering Excel, you'll be well-equipped to handle any data-related task that comes your way.

Now let's move on to the next skill and also one of the most important skills you'll need as a data analyst.

Step 5: Master SQL for Data Extraction

SQL (Structured Query Language) is a critical tool in data analysis. As a data analyst, one of your primary responsibilities is to extract data from databases, and SQL is the language used to do so.

SQL is more than just running basic queries like SELECT, FROM, and WHERE. It's a complex language that allows you to manipulate and transform data in countless ways. SQL is used for joining data from multiple tables, filtering and aggregating data, and creating new tables and views.

To be an effective data analyst, it's essential to master SQL. You should be comfortable with writing queries, creating tables, and understanding how to optimize your queries for performance.

Fortunately, there are many resources available to help you learn SQL. Some great places to start are Khan Academy SQL, W3Schools, SQLZoo, SQLbolt, Luke Barousse, Alex the Analyst, Microsoft Power Tools, and finally some SQL games like SQL island and SQL Murder.

Additionally, there are many online courses and books available that cover SQL in-depth. Here are a few to get you started:

1. Learn SQL and Databases – Full Course for Beginners
2. Relational Database curriculum from freeCodeCamp
3. Collection of free relational database courses

By mastering SQL, you'll be able to extract valuable insights from databases and manipulate data in ways that provide meaningful business insights.

Step 6: Learn Python for Data Analysis

I know there's a lot of speculation as to whether or not a data analyst needs Python – some say they do while some say they don't.

For me, I'd like to say it depends on the company you're working for – but it's nice to have an edge by learning Python as it's one of the most widely used programming languages in the world of data analysis. Python is known for its simplicity, readability, and versatility, making it a popular choice for data analysts.

Python has a vast array of libraries and tools that can make data analysis easier, such as Pandas for data manipulation and analysis, NumPy for scientific computing, and Matplotlib for data visualization. It also has the ability to automate tasks, making data analysis more efficient and effective.

Learning Python for data analysis is a great investment in your career as a data analyst. Not only will it allow you to work with powerful libraries, but it will also open up many opportunities to work with larger datasets and more complex analyses.

There are many resources available to help you learn Python, from free online courses to paid online programs and textbooks. Some resources include freeCodeCamp, DataCamp, CodeBasis, Programming with Mosh and Microsoft Learn.

By learning Python, you'll be able to perform more complex data analysis, automate tasks, and work with a broader range of datasets, making you a valuable asset in any data-focused organization.

Step 7: Master a Data Visualization Tool

As a data analyst, it's essential to be able to communicate your findings in a clear and concise manner. One way to do this is through data visualization. Data visualization tools like PowerBI and Tableau can help you create interactive charts, graphs, and dashboards that make it easy for others to understand your findings. We'll talk about them more in a minute.

Here's a Tableau for Data Science and Data Visualization course you can check out.

While SQL is great for querying and manipulating data, it can't fully bring your data to life. This is where a data visualization tool comes in. These tools allow you to transform your data into insightful and easy-to-understand visualizations that can be shared with stakeholders.

You can learn data visualization basics in this in-depth free course on freeCodeCamp's YouTube channel.

PowerBI is a great choice for data visualization as it is easy to learn and integrate with other Microsoft products. This makes it an ideal tool for organizations that use Microsoft Office. Tableau is also a popular choice and has a strong community of users and a wide range of features.

Learning a data visualization tool like PowerBI or Tableau will enable you to create compelling visualizations that help you better understand your data and communicate your findings to others. There are many online courses and tutorials available to help you learn these tools, such as the official Microsoft PowerBI training and Tableau's own training courses.

By mastering a data visualization tool, you'll be able to create interactive and engaging visualizations that will help you better understand your data and communicate your findings to others, making you an invaluable asset to any data-focused organization.

You can also dive into other popular data viz tools like D3.js - here's a course on it to get you started.

Step 8: Network with Other Data Analysts and Developers

Networking is an essential part of any profession, and data analytics is no exception. By networking with other data analysts and developers, you can learn from their experiences, get insights on the latest industry trends and technologies, and potentially find job opportunities.

Here are a few ways to network with others in the field:

1. Attend industry events: Look for conferences, meetups, and other events related to data analytics and attend them. This is a great way to meet others in the field and learn about new developments and technologies.
2. Join online communities: There are many online communities for data analysts and developers, such as forums, LinkedIn groups, and social media groups. Join these communities and participate in discussions to connect with others in the field.
3. Reach out to others: Don't be afraid to reach out to other data analysts and developers, whether through social media, email, or in person. Introduce yourself, ask for advice, and build relationships.

Remember, networking is a two-way street. Be willing to offer help and advice to others in the field as well. By building a strong network of contacts in the data analytics field, you can enhance your career opportunities and stay up to date on the latest industry trends and technologies.

Step 9: Don't Forget about "Soft Skills"

One final skill that I think needs to be worked on before you can be a great DA is your soft skills which involves your ability to communicate, solve problems etc

After all is said and done, practice and practice and build projects.

Conclusion.

Becoming a data analyst requires dedication, hard work, and a passion for data analysis. Following the steps outlined in this roadmap will help you gain the necessary skills and knowledge to become a successful data analyst.

From understanding the role of a data analyst, to mastering SQL and Python, to networking with other developers, each step is crucial to achieving success in this field.

Remember to stay curious, never stop learning, and always be willing to adapt to new technologies and methodologies. With determination and persistence, you can achieve your goal of becoming a proficient data analyst and unlock a world of exciting career opportunities.

Source: https://www.freecodecamp.org

#dataanalytics

Data Analysis with Python, Tableau, PowerBI and Excel

Learn how to become a Data Analyst with Python, Tableau, PowerBI and Excel. You will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. 

How To Become A Data Analyst | Data Analysis with Python Tableau PowerBI Excel 

Data analytics has been rapidly growing, with companies looking to generate insights and drive their business with the help of data. In this Data Analytics Full Course video, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. This tutorial will guide you with powerful data analysis tools like Tableau, PowerBT, Python, and Excel. You will then understand a case study and analyze data using Python and R. In addition, we will see the top 10 data analysis tools and understand the difference between a data scientist and a data analyst. Finally, we'll see the top interview questions to help you crack a data analyst interview.

#dataanalytics #tableau #excel #powerbi #python 

Data Analytics with Excel - Full Course in 8 Hours

In this Data Analytics with Excel full course, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications.

Data Analytics With Excel Full Course | Data Analytics Full Course | Data Analytics

Dataset Link - https://drive.google.com/drive/folders/1U8CF0Xx3NivXdfrQvXJ_462tEb26oPuX 

Why become Data Analyst?

By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in today's business environment. In fact, 2.5 quintillion bytes of data are created each day. Data analysts' value is growing with an ever-increasing skill gap in data analytics, creating new job and career advancement opportunities.

The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to increase by 15 percent to 2.72 million jobs by 2020, with the most significant demand for data analysts in finance, insurance, and information technology. Data analysts earned an average pay of $67,377 in 2019, according to Glassdoor.

#dataanalytics #excel 

Data Intelligence vs Data Analytics

Introduction to Data Analytics and Intelligence

Customers are the most expensive in business, but customers have huge exposure on their hands in reality. If they are unsatisfied, they stop taking service. So, customer churn needs to be reduced. Data analysis could help to reduce customer churn. Data governance, Limited resources, and lack of data control are other challenges. Data intelligence and analytics help to resolve them.

Data intelligence always plays a vital role in artificial intelligence and machine learning. Machine learning tools help to perform analysis on a larger dataset. Data intelligence helps to make better decisions for the future organization.

Data intelligence is related to big data management, data mining, human-computer interaction, and machine learning. E-commerce applications and social media applications are built on data intelligence. Intelligence has an essential role in cybersecurity, health, finance, and insurance data.

Data Analysis perform on the dataset to predict so that organizations can move forward. To reduce customer churn rate, fraud detection, and even to develop products, organizations take the help of data analytics.

What is Data Intelligence?

Data intelligence refers to analytics tools and techniques that help to understand data better. Mainly data analysis performs under data intelligence helps to make a better decision. AI and machine learning models are also used in the current scenario for better analysis. Data intelligence tells about what happened and why it happened? When happen? Where? How?

A few significant components of data intelligence are descriptive, prescriptive, diagnostic, decisive, and predictive data.

What is Data Analytics?

In data analytics, the raw dataset is collected and then processed as that row dataset, and the organization tries to read the trend. The dataset also predicts what may happen in the future. Data analytics can be different types like descriptive, diagnostic, predictive analysis, etc.

The customer dataset contained information like customer name, gender, which service is provided to the customer, price, payment mode, and whether the customer is dependent on someone.

Now how does data analysis respond in different stages and solve the problem?

Descriptive Analysis

In such type of data analysis, we examine what happens in the dataset? Or what is happening? Like, in the case of churn analysis, first examines how many customers are churning? And customer churn rate also. What is the gender distribution of customers? Whether that customer is dependent on someone or not? In such a way, datasets describe to the analyst for further analysis.

Diagnostic Analysis

Diagnostic analysis performs after descriptive analysis to know the reason behind the descriptive analysis. To perform diagnostic analysis, the key question is why? Like from descriptive analysis, one can know, "how many customers stop taking service?" And diagnostic analysis tells about more information. If female customers are more churn than mail customers, then diagnostic analysis suggests why female customers are more churn? According to Fig 3, as the customer is dependent, the price of phone service is high, and the customer is unsatisfied with the company's service.

Prescriptive Analysis

This type of analysis helps predict the outcome and what action needs to be taken. The prescriptive analysis is further divided into optimization and random testing. For example, as per Fig 3, "If a customer is churning," the company should improve its service. Also, the company should reduce the price to reduce the customer churn rate.

Predictive Analysis

Once diagnostic analysis performs, predictive analysis is possible. From the predictive analysis, it's possible to know what will happen in the future? From predictive analysis, one can know which customers would be churning? For example, in Fig3, one asks the model and supples some customer information "Will the customer be a churn or not?" and the model response is no.

Importance of Data Intelligence and Data Analytics

The importance of Data Intelligence and Data Analytics are described below:

Data Intelligence

• Data Intelligence helps companies take decisions in favor of organizations.
• With data intelligence, from historical data, companies know about essential factors that information has a significant impact on a company's revenue growth.
• Data intelligence makes the organization smarter and also helps to make data-driven decisions.
• Data-driven decisions are like - Identifying the ways how can increase profit, Also possible the analyze customer behavior, and team member performance tracking is possible.

Data Analytics

• Data analysis helps in the product development phase. Once the analysis performs, one will be aware of the current market trend and how much customers use the product, such as relevant information. Data analysis helps to build an effective product.
• Data analysis helps increase business proficiency and helps decrease the cost price of a product.

What are the challenges of Data Analytics and Data Intelligence?

There are different types of challenges with Data Analytics. Few of them are -

Meaningful data collection

In current scenarios, data analytics deal with big data. That datasets contain much irrelevant information as well. That's why collecting meaningful Data is a big challenge. In most cases, analysts process the dataset after collection and make changes according to requirements.

Right tool selection

Various tools are available in the market. Like MongoDB, Cassandra, etc., for data storing. On the other hand, Microsoft Power BI, Tableau, Jupyter Notebook, etc., for data visualization. Any of these tools can use for data analysis. But from such options selecting the right tool for analysis is another challenge.

Growing data culture in the organization

Very few are aware of the importance of data culture for an organization's growth. This is also a challenge to clarify the importance of data to others. The top level of the organization understands the significance of data, but most of the time, some organizations fail the understand the importance of data for organizational growth.

Data Visualization

Dataset is meaningless until properly visualized the dataset is. As human brain can keep visual information for a long time. But the time right tools selection, there have many visualization tools mentioned, and now selecting the correct tool for data visualization is another major challenge.

Data Intelligence vs. Data Analytics

Data intelligence is the collection of data. With the help of data intelligence, we can extract meaningful insights from the dataset that helps to make decisions. This task performs by AI and machine learning modules. Even sometimes, this task performs manually.

On the other hand, data analytics performs on the datasets and predicts the future. Based on that prediction, actions take for organization growth.

Another way, Data intelligence collects data and then performs some events and actions and from the collected Data tells what happened and why it happened so. On the other hand, data analytics performs operations and predicts what will happen.

Use Cases of Data Intelligence

Data Intelligence use cases are described below:

Healthcare System

Nowadays, healthcare services move online rapidly. The Healthcare sector is the largest market in India and the most profitable from a business point of view. That's why organizations need to make better decisions, and data intelligence comes into its role to help. Here, the patient's health is the top priority. The organization needs to analyze big data to make decisions for patient health and business growth. AI, machine learning, and deep learning modules help analyze the dataset. Even data intelligence helps to know whether customers satisfy or not by analyzing app data.

Supply Chain Management

To manage the supply chain, a business needs to deal with big data. Here Business Intelligence calculates the business risks, how to reduce loss, and implements a self-automated supply chain management system.

E-Commerce

From the e-commerce website, the organization collects customer feedback. And with the help of Data Intelligence, organizations know whether customers are satisfied with the service or not. If not, then why? And this result helps in business development.

Conclusion

Though the term Data Intelligence and Data Analytics are almost similar, these two terms hold differences. Data Intelligence tells about facts from the Data. One knows about the reason behind the facts. And for data analysis, firstly process the datasets and then predict what may happen in the future. Data analytics is a subsection the Data Intelligence.

• Discover more about Cloud vs Edge vs Fog Computing
• Click to explore Artificial Intelligence Solutions for Edge

Original article source at: https://www.xenonstack.com/

#dataintelligence #dataanalytics 

Learn Data Analytics for Beginners - Full Course

Data Analytics Full Course | Data Analytics For Beginners | Data Analytics Course

Data analytics has been rapidly growing, with companies looking to generate insights and drive their business with the help of data. In this Data Analytics Full Course video, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. You will then understand a case study and perform analysis of data using Python and R. In addition to that, we will see the top 10 data analysis tools and understand the difference between a data scientist and a data analyst. Finally, we’ll see the top interview questions to help you crack a data analyst interview.

• What is Data Analytics
• Data Analytics for Beginners
• Data Analytics Using Phyton
• Top 10 skills to become a Data analyst in 2022
• Top 10 Analysis Tools
• Data Analytics Using Phyton
• R tutorial
• Time Series
• Pivot table in Excel
• Covid 19
• Spotify
• World Happiness Report
• Olympics
• Data Analyst

Dataset Link - https://drive.google.com/drive/folders/1U8CF0Xx3NivXdfrQvXJ_462tEb26oPuX 

#dataanalytics #python #r

Transforming Ways Of interactive Data analytics with Apache Druid

What is Apache Druid?

Apache Druid is a real-time analytics database that is designed for rapid analytics on large datasets. This database is used more often for powering use cases where real-time ingestion, high uptime, and fast query performance is needed.  Druid can be used to analyze billions of rows not only in batch but also in real-time. It offers many integrations with different technologies like Apache Kafka Security, Cloud Storage, S3, Hive, HDFS, DataSketches, Redis, etc. It also follows the immutable past and append-only future. As past events happen once and never change, these are immutable, whereas the only append takes place for new events. It provides users with a fast and deep exploration of large scale transaction data.

Characteristics of Apache Druid

Some of the exciting characteristics of Apache Druid are:

•  
  • Cloud-Native, making easy horizontal scaling
  • Supports SQL for analyzing data
  • REST API enabled for querying or uploading data

What are its use cases?

Some of the common use cases of Druid are:

• Clickstream analytics
• Server metrics storage
• OLAP/Business intelligence
• Digital Marketing/Advertising analytics
• Network Telemetry analytics
• Supply chain analytics
• Application performance metrics

What are its key features?

Druid’s core architecture is made by combining the ideas of different data warehouses, log search systems, and time-series databases.

Columnar Storage Format

It uses column-oriented storage; hence only loads required columns needed for a particular query. It helps in fast scans and aggregations.

Parallel Processing

It can process a query in parallel across the entire cluster. It is also termed as Massively Parallel Processing.

Scalable Distributed System

Druid is mostly deployed in clusters ranging from tens to hundreds that offer ingest rate of millions of records/sec, query latencies of sub-second to a few seconds, and retention of trillions of records.

Real-time or Batch Ingestion

Druid can ingest data either in real-time (Ingested data can be queried immediately) or in batches.

Cloud-Native

It is a fault-tolerant architecture that won’t lose data. Once Druid ingests data, its copy is safely stored in deep storage (Cloud Storage, Amazon S3, Redis, HDFS, many more). Users' data can be easily recovered from this deep storage even if all the Druid’s servers fail. This replication ensures that queries are still possible while the system recovers.

Indexing

Druid uses concise and roaring compressed bitmap indexes to create indexes that help in faster filtering.

Timestamp Partitioning

Every data in Druid must have a timestamp column as the data is always partitioned by time, and every query has a time filter.

Easy Integration with Existing Pipelines

Users can easily stream data natively using Druid from message buses like Kafka, kinesis, and many more. It can also load batch files from the data lakes like HDFS and Amazon S3.

General Architecture of Apache Druid

Druid is mainly composed of the following processes:

• Coordinator – This process manages data availability on the cluster.
• Overlord – This process controls the assignment of data ingestion workloads.
• Broker – This helps handle queries from external clients.
• Historical – This process store data that is queryable.
• Middle manager – This process is responsible for ingesting the data.
• Router – These processes are used to route requests to Brokers, Coordinators, and Overlords. These processes are optional.

The processes described above are organized into 3 types of servers: Master, Query, and Data.

Master

It runs the Coordinator and Overlord. Basically, it manages big data ingestion and availability. Master is responsible for the ingestion of jobs and coordinating the availability of data on the “Data Servers”.

Query

It runs Brokers and Optional Router processes. Basically, it handles queries and external clients by providing the endpoints of applications that users and clients interact with, routing queries to Data servers or other Query servers.

Data

It runs Middle Managers and Historical processes. This helps execute jobs and store the queryable data. Other than these 3 servers and six processes, Druid also requires storage for Metadata and Deep Storage.

Metadata Storage

It is basically used to store the metadata of the system (Audit, Datasource, Schemas, and so on). For experimental purposes, the environment suggested using Apache Derby. Derby is the default metadata store for Druid, but it is not suitable for production. For production purposes, MySQL or PostgreSQL is the best choice. Metadata storage stores the entire metadata, which is very useful for the cluster of Druid to work. Derby is not used for production as it does not support a multi-node cluster with high availability. MySQL as a metadata storage database is used to acquire:

• Long term flexibility
• Scaling on budget
• Good with large datasets
• Good high read speed

PostgreSQL, as a metadata storage database, is used to acquire:

• Complex database designs
• Performing customized procedures
• Diverse indexing technique
• Variety of replication methods
• High read and write speed.

Deep Storage

Apache Druid uses separate storage for any data ingested that makes it fault-tolerant. Some of Deep Storage Technologies are Cloud Storage, Amazon S3, HDFS, Redis, and many more.

A structure that defines the logical view of the entire defines how the data is managed and how the relations among them are associated. Click to explore about our, Types of Databases

Data Ingestion in Druid

Data in Druid is organized into segments that generally have rows up to a few million. Loading data in Druid is known as Ingestion or Indexing. Druid fully supports batch ingestion and streaming ingestion. Some of the technologies supported by Druid is Kinesis, Cloud Storage, Apache Kafka, and local storage. Druid requires some structure to the data it ingests. In general, data should consist of OS timestamp, metrics and dimensions.

Zookeeper for Apache Druid

Apache Druid uses Apache Zookeeper to integrate all the services. Users can use Zookeeper that comes with Druid for experiments, but one has to install Zookeeper for production. It’s cluster can only be as stable by a Zookeeper. Zookeeper is responsible for most of the communications that keep the Druid cluster functioning as Druid nodes are prevented from talking to each other.

Duties of a Zookeeper

Zookeeper is responsible for the following operations:

• Segment “publishing” protocol from Historical
• Coordinator leader election
• Overlord and MiddleManager task management
• Segment load/drop protocol between Coordinator and Historical
• Overlord leader election

How to Keep a Zookeeper Stable?

For maximum Zookeeper stability, the user has to follow the following practices:

• There should be a Zookeeper dedicated to Druid; avoid sharing it with any other products/applications.
• Maintain an odd number of Zookeepers for increased reliability.
• For highly available Zookeeper, 3-5 Zookeeper nodes are recommended. Users can either install Zookeeper on their own system or run 3 or 5 master servers and configure Zookeeper on them appropriately.
• Share Zookeeper’s location with a master server rather than doing so with data or query servers. This is done because query and data are far much work-intensive than the master node (coordinator and overlord).
• To fully achieve high availability, it is recommended to never out Zookeeper behind a load balancer.

If Zookeeper goes down, the cluster will operate. Failing of Zookeeper would neither result in addition to new data segments nor can it effectively react to the loss of one of the nodes. So, the failure of Zookeeper is a degraded state.

The IDP is an excellent solution for meeting the ever-increasing demand for faster development and release cycles with total automation.Click to explore about our, Database-as-a-Service

How to monitoring Apache Druid?

Users can monitor Druid by using the metrics it generates. Druid generates metrics related to queries, coordination and ingestion. These metrics are emitted as a JSON object. It is either emitted to a runtime log file or over HTTP (to service like Kafka). The emission of a metric is disabled by default.

Fields of Metrics Emitted

Metrics emitted by Druid share a common set of fields.

• Timestamp – the time at which metric was created
• Metric – the name given to the metric
• Service – the name of the service that emitted the metric
• Host – the name of the host that emitted the metric
• Value – the numeric value that is associated with the metric emitted

Briefing About Available Metrics

Metric emitted may have dimensions beyond the one listed. To change the emission period of Druid that is 1 minute by default, one can use `druid.monitoring.emissionPeriod` to change the default value. Metrics available are:

• Query Metrics, mainly categorized as Broker, Historical, Real-time, Jetty and Cache
• SQL Metrics (Only if SQL is enabled)
• Ingestion Metrics (Kafka Indexing Service)
• Real-time Metrics (Real-time process, available if Real-time Metrics Monitor is included)
• Indexing Service
• Coordination
• JVM (Available if JVM Monitor module is included)
• Event Receiver Firehose (available if Event Receiver Firehose Monitor module is included)
• Sys (Available if Sys Monitor module is included)
• General Health, mainly Historical

Conclusion

Apache Druid is the best in the market when it comes to analyzing data in clusters and providing brief insight to all the data processed. Plus having Zookeeper by the side, one can ease up their working with it and rule the DataOps market. Also, there are many libraries to interact with it. To Validate the running of services, one can use JPS commands. As Druid nodes are java processes, they would show up when JPS commands '$ jps -m' are used. With that much ease in monitoring Druid and working with such a vast architecture of Druid, it is really the last bite of an ice-cream for a DataOps Engineer.

• Get insight on Platform as a Service (PaaS) in Cloud Computing 
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Original article source at: https://www.xenonstack.com/

#dataanalytics #interactive 

Data Analytics with Excel - Full Course

Data Analytics With Excel Full Course | Data Analytics Full Course | Data Analytics

In this Data Analytics with Excel full course, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications.

Dataset Link - https://drive.google.com/drive/folders/1U8CF0Xx3NivXdfrQvXJ_462tEb26oPuX 

Why become Data Analyst?

By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in today’s business environment. In fact, 2.5 quintillion bytes of data are created each day. Data analysts' value is growing with an ever-increasing skill gap in data analytics, creating new job and career advancement opportunities.

The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to increase by 15 percent to 2.72 million jobs by 2020, with the most significant demand for data analysts in finance, insurance, and information technology. Data analysts earned an average pay of $67,377 in 2019, according to Glassdoor.

Who should take up this course?

Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analyst Master’s Program, including:
1. IT professionals
2. Banking and finance professionals
3. Marketing managers
4. Sales professionals
5. Supply chain network managers
6. Beginners in the data analytics domain
7. Students in UG/ PG programs

#dataanalytics #excel

Learn Data Analyst - Full Course for Beginners

In this Data Analyst full course, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. You will then understand a case study and perform analysis of data using Python and R. In addition to that; we will see the top 10 data analysis tools and understand the difference between a data scientist and a data analyst. Finally, we’ll see the top interview questions that will help you crack a data analyst interview.

Below are the topics covered in this video:

• 00:00:00 Introduction - Data Analyst
• 00:01:56Data Analyst Career  - Data Analyst
• 00:37:15 Top 10 Data Analyst Skills - Data Analyst
• 00:50:33 Top 10 Data Analyst Tools - Data Analyst
• 01:23:10 Data Scientist vs Data Analyst - Data Analyst Full Course 
• 01:39:59 Data Analytics with Python - Data Analyst
• 01:49:44 Microsoft Excel Tutorial - Data Analyst
• 02:18:41 Pivot table in excel - Data Analyst
• 02:46:53 Excel Dashboard - Data Analyst
• 06:58:05 Data Analytics Using R - Data Analyst
• 08:04:33 What is SQL? - Data Analyst
• 09:41:38 Data Analyst Interview Questions - Data Analyst

Why become Data Analyst?

By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialities due to the value that can be derived from data. Data is more abundant and accessible than ever in today’s business environment. In fact, 2.5 quintillion bytes of data are created each day. With an ever-increasing skill gap in data analytics, the value of data analysts is continuing to grow, creating new job and career advancement opportunities.

#dataanalyst #dataanalytics

Data Analytics vs Data Analysis: What's The Difference?

This tutorial shows what data analytics is, the importance of data analytics, and the types of data analytics. The difference between data analysis and data analytics.

Data analytics is the process of collecting, organizing, and analyzing raw data from different sources. You can then gain insights that'll help organizations make important predictions and decisions.

Data analytics mostly involves studying data trends over a given period, and then extracting useful information from these trends.

Why Is Data Analytics Important?

More precise decision making process: Data analytics helps organizations make more accurate decisions based on the insights gotten from data trends over time.

For example, a company selling different products can figure out what time of the year different products sell higher. This will enable them boost production of such products at the required time.

A better decision making process will eliminate the need for guess work, and minimize losses and avoidable risks.

Improved customer satisfaction: When you're able to serve customers, you retain them and keep business going. Insights gotten from data analytics can help you understand exactly what your customers want and when to act.

Data analytics also enables businesses to identify their target audience easily.

Improved business strategy: Data analytics helps organizations channel their resources towards the most efficient strategies.

Performance evaluation: Data analytics can help organizations evaluate how well or badly they've performed over a specified period. This will enable them make important decisions for the future of the organization.

Although the points listed above seem to be from the business point of view, that's not the only industry where data analytics is important.

You can see data analytics being used in healthcare, education, agriculture, and so on.

Types of Data Analytics

There are mainly four different types of data analytics:

• Descriptive analytics: This type of analytics has to do with what happened with analyzed data over a specified period of time.
• Diagnostic analytics: Diagnostic data analytics shows the "why" in a data trend. This involves having a deeper look into why certain patterns were present in the data.
• Predictive analytics: The goal here is to foretell what is expected to happen in the future based on the outcomes of analyzed data over time.
• Prescriptive analytics: In prescriptive analytics, the results from data analysis is used to make recommendations on what to do next.

What Is the Difference Between Data Analysis and Data Analytics?

You'll come across different definitions of data analytics and data analysis.

Some sources would define data analytics and data analysis as the same. Others would use them interchangeably.

Although, they are closely related, these terms have slightly different meanings. They are similar because they aid in the decision making process.

What Is Data Analysis?

Data analysis is the process of studying what has happened in the past in a dataset. There is no need to extend this definition.

Data analysis studies the why and how of data trends. Yes, it involves data collection, organization, and "analysis".

"How did the users respond to a new feature?".

"Why did the rate of purchase of a product fall during a particular period?".

Data analysts can make use of programming languages when analyzing data or data visualization tools.

What Is Data Analytics?

Data analytics is the process of taking insights gained from the analysis of past data trends, and making predictions or decisions for the future.

In the beginning of the article, we defined data analytics to include both analysis and analytics. This is mainly as a convention.

Analytics is used to proffer solutions or make recommendations.

Summary

There is data everywhere. We create them on a daily basis. But data in its raw form has no real meaning.

In order to understand the behavior of data over time, we have to group the data together, study them, and derive useful insights.

This article explained what data analytics is, the importance of data analytics, and the types of data analytics.

We also explained the difference between data analysis and data analytics.

Thank you for reading!

Original article source at https://www.freecodecamp.org

#dataanalytics #dataanalysis

PyArrow and The Future of Data Analytics

In this tutorial, we will introduce PyArrow and talk bout the transformation that the Arrow format is allowing in the Data Analytics world.

PyArrow provides an in-memory format, a disk format, a network exchange protocol, a dataframe library and a query engine all integrated in a single library. But the Arrow ecosystem doesn't stop there and allows you to work integrating multiple different technologies. It can be a swiss army knife for data engineers and it integrates zero cost with NumPy and Pandas in many cases."

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

#pyarrow #dataanalytics #python

How to Become an Expert in Python Data Analytics

Python Data Analysis Full Course | Python Data Analysis Project | Data Analytics

This video is based on Python Data Analysis Projects for Beginners. The topics covered in this video are essential for mastering Python Data Analysis. This video is curated by industry experts and will help you with the critical skills, tips, and tricks required to become an expert in Python Data Analytics. This Python Data Analytics full course video will cover all the basics to advanced concepts of Python Data Analytics.

What is Python?

Python is a high-level object-oriented programming language developed by Guido van Rossum in 1989 and was first released in 1991. Python is often called a battery-included language due to its comprehensive standard library. A fun fact about Python is that The name Python was actually taken from the popular BBC comedy show of that time, "Monty Python's Flying Circus". Python is widely used these days in data analytics, machine learning, and web development. Python allows you to write programs in fewer lines of code than most programming languages. Python as a programming language is growing rapidly. It's the right time to get trained in Python.

A Beginner’s Guide to Data Analysis in Python

A step by step guide to get started with data analysis in Python

The Role of a Data Analyst

A data analyst uses programming tools to mine large amounts of complex data, and find relevant information from this data.

In short, an analyst is someone who derives meaning from messy data. A data analyst needs to have skills in the following areas, in order to be useful in the workplace:

• Domain Expertise — In order to mine data and come up with insights that are relevant to their workplace, an analyst needs to have domain expertise.
• Programming Skills —As a data analyst, you will need to know the right libraries to use in order to clean data, mine, and gain insights from it.
• Statistics — An analyst might need to use some statistical tools to derive meaning from data.
• Visualization Skills — A data analyst needs to have great data visualization skills, in order to summarize and present data to a third party.
• Storytelling — Finally, an analyst needs to communicate their findings to a stakeholder or client. This means that they will need to create a data story, and have the ability to narrate it.

In this article, I am going to walk you through the end-to-end data analysis process with Python.

If you follow along to this tutorial and code everything out the way I did, you can then use these codes and tools for future data analytic projects.

We will start with downloading and cleaning the dataset, and then move on to the analysis and visualization. Finally, we will tell a story around our data findings.

I will be using a dataset from Kaggle called Pima Indian Diabetes Database, which you can download to perform the analysis.

Pre-Requisites

For this entire analysis, I will be using a Jupyter Notebook. You can use any Python IDE you like.

You will need to install libraries along the way, and I will provide links that will walk you through the installation process.

The Analysis

After downloading the dataset, you will need to read the .csv file as a data frame in Python. You can do this using the Pandas library.

If you do not have it installed, you can do so with a simple “pip install pandas” in your terminal. If you face any difficulty with the installation or simply want to learn more about the Pandas library, you can check out their documentation here.

Read the Data

To read the data frame into Python, you will need to import Pandas first. Then, you can read the file and create a data frame with the following lines of code:

import pandas as pd
df = pd.read_csv('diabetes.csv')

To check the head of the data frame, run:

df.head()

From the screenshot above, you can see 9 different variables related to a patient’s health.

As an analyst, you will need to have a basic understanding of these variables:

• Pregnancies: The number of pregnancies the patient had
• Glucose: The patient’s glucose level
• Blood Pressure
• Skin Thickness: The thickness of the patient’s skin in mm
• Insulin: Insulin level of the patient
• BMI: Body Mass Index of patient
• DiabetesPedigreeFunction: History of diabetes mellitus in relatives
• Age
• Outcome: Whether or not a patient has diabetes

As an analyst, you will need to know the difference between these variable types — Numeric and Categorical.

Numeric variables are variables that are a measure, and have some kind of numeric meaning. All the variables in this dataset except for “outcome” are numeric.

Categorical variables are also called nominal variables, and have two or more categories that can be classified.

The variable “outcome” is categorical — 0 represents the absence of diabetes, and 1 represents the presence of diabetes.

A Quick Note

Before continuing with the analysis, I would like to make a quick note:

Analysts are humans, and we often come with preconceived notions of what we expect to see in the data.

For example, you would expect an older person to be more likely to have diabetes. You would want to see this correlation in the data, which might not always be the case.

Keep an open mind during the analysis process, and do not let your bias effect the decision making.

Pandas Profiling

This is a very useful tool that can be used by analysts. It generates an analysis report on the data frame, and helps you better understand the correlation between variables.

To generate a Pandas Profiling report, run the following lines of code:

import pandas_profiling as pp
pp.ProfileReport(df)

This report will give you some overall statistical information on the dataset, which looks like this:

By just glancing at the dataset statistics, we can see that there are no missing or duplicate cells in our data frame.

The information provided above usually requires us to run a few lines of codes to find, but is generated a lot more easily with Pandas Profiling.

Pandas Profiling also provides more information on each variable. I will show you an example:

This is information generated for the variable called “Pregnancies.”

As an analyst, this report saves a lot of time, as we don’t have to go through each individual variable and run too many lines of code.

From here, we can see that:

• The variable “Pregnancies” has 17 distinct values.
• The minimum number of pregnancies a person has is 0, and the maximum is 17.
• The number of zero values in this column is pretty low (only 14.5%). This means that above 80% of the patients in the dataset are pregnant.

In the report, there is information like this provided for each variable. This helps us a lot in our understanding of the dataset and all the columns in it.

The plot above is a correlation matrix. It helps us gain a better understanding of the correlation between the variables in the dataset.

There is a slight positive correlation between the variables “Age” and “Skin Thickness”, which can be looked into further in the visualization section of the analysis.

Since there are no missing or duplicate rows in the data frame as seen above, we don’t need to do any additional data cleaning.

Data Visualization

Now that we have a basic understanding of each variable, we can try to find the relationship between them.

The simplest and fastest way to do this is by generating visualizations.

In this tutorial, we will be using three libraries to get the job done — Matplotlib, Seaborn, and Plotly.

If you are a complete beginner to Python, I suggest starting out and getting familiar with Matplotlib and Seaborn.

Here is the documentation for Matplotlib, and here is the one for Seaborn. I strongly suggest spending some time reading the documentation, and doing tutorials using these two libraries in order to improve on your visualization skills.

Plotly is a library that allows you to create interactive charts, and requires slightly more familiarity with Python to master. You can find the installation guide and requirements here.

If you follow along to this tutorial exactly, you will be able to make beautiful charts with these three libraries. You can then use my code as a template for any future analysis or visualization tasks in the future.

Visualizing the Outcome Variable

First, run the following lines of code to import Matplotlib, Seaborn, Numpy, and Plotly after installation:

# Visualization Imports
import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette()
get_ipython().run_line_magic('matplotlib', 'inline')
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
import plotly.express as px
import numpy as np

Next, run the following lines of code to create a pie chart visualizing the outcome variable:

dist = df['Outcome'].value_counts()
colors = ['mediumturquoise', 'darkorange']
trace = go.Pie(values=(np.array(dist)),labels=dist.index)
layout = go.Layout(title='Diabetes Outcome')
data = [trace]
fig = go.Figure(trace,layout)
fig.update_traces(marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.show()

This is done with the Plotly library, and you will get an interactive chart that looks like this:

You can play around with the chart and choose to change the colors, labels, and legend.

From the chart above, however, we can see that most patients in the dataset are not diabetic. Less than half of them have an outcome of 1 (have diabetes).

Correlation Matrix with Plotly

Similar to the correlation matrix generated in Pandas Profiling, we can create one using Plotly:

def df_to_plotly(df):
    return {'z': df.values.tolist(),
            'x': df.columns.tolist(),
            'y': df.index.tolist() }
import plotly.graph_objects as go
dfNew = df.corr()
fig = go.Figure(data=go.Heatmap(df_to_plotly(dfNew)))
fig.show()

The codes above will generate a correlation matrix that is similar to the one above:

Again, similar to the matrix generated above, a positive correlation can be observed between the variables:

• Age and Pregnancies
• Glucose and Outcome
• SkinThickness and Insulin

To further understand the correlations between variables, we will create some plots:

Visualize Glucose Levels and Insulin

fig = px.scatter(df, x='Glucose', y='Insulin')
fig.update_traces(marker_color="turquoise",marker_line_color='rgb(8,48,107)',
                  marker_line_width=1.5)
fig.update_layout(title_text='Glucose and Insulin')
fig.show()

Running the codes above should give you a plot that looks like this:

There is a positive correlation between the variables glucose and insulin. This makes sense, because a person with higher glucose levels would be expected to take more insulin.

Visualize Outcome and Age

Now, we will visualize the variables outcome and age. We will create a boxplot to do so, using the code below:

fig = px.box(df, x='Outcome', y='Age')
fig.update_traces(marker_color="midnightblue",marker_line_color='rgb(8,48,107)',
                  marker_line_width=1.5)
fig.update_layout(title_text='Age and Outcome')
fig.show()

The resulting plot will look somewhat like this:

From the plot above, you can see that older people are more likely to have diabetes. The median age for adults with diabetes is around 35, while it is much lower for people without diabetes.

However, there are a lot of outliers.

There are a few elderly people without diabetes (one even over 80 years old), that can be observed in the boxplot.

Visualizing BMI and Outcome

Finally, we will visualize the variables “BMI” and “Outcome”, to see if there is any correlation between the two variables.

To do this, we will use the Seaborn library:

plot = sns.boxplot(x='Outcome',y="BMI",data=df)

The boxplot created here is similar to the one created above using Plotly. However, Plotly is better at creating visualizations that are interactive, and the charts look prettier compared to the ones made in Seaborn.

From the box plot above, we can see that higher BMI correlates with a positive outcome. People with diabetes tend to have higher BMI’s than people without diabetes.

You can make more visualizations like the ones above, by simply changing the variable names and running the same lines of code.

I will leave that as an exercise for you to do, to get a better grasp on your visualization skills with Python.

Data Storytelling

Finally, we can tell a story around the data we have analyzed and visualized. Our findings can be broken down as follows:

People with diabetes are highly likely to be older than people who don’t. They are also more likely to have higher BMI’s, or suffer from obesity. They are also more likely to have higher glucose levels in their blood. People with higher glucose levels also tend to take more insulin, and this positive correlation indicates that patients with diabetes could also have higher insulin levels (this correlation can be checked by creating a scatter plot).

That’s all for this article! I hope you found this tutorial helpful, and can use it as a future reference for projects you need to create. Good luck in your data science journey, and happy learning!

#python #dataanalytics