Tableau

Turn your pandas dataframe into a Tableau-style User Interface

PyGWalker: A Python Library for Exploratory Data Analysis with Visualization

PyGWalker can simplify your Jupyter Notebook data analysis and data visualization workflow. By turning your pandas dataframe into a Tableau-style User Interface for visual exploration.

PyGWalker (pronounced like "Pig Walker", just for fun) is named as an abbreviation of "Python binding of Graphic Walker". It integrates Jupyter Notebook (or other jupyter-based notebooks) with Graphic Walker, a different type of open-source alternative to Tableau. It allows data scientists to analyze data and visualize patterns with simple drag-and-drop operations.

Visit Google Colab, Kaggle Code, or Graphic Walker Online Demo to test it out!

PyGWalker will add more support such as R in the future.

Getting Started

Tested Environments

•  Jupyter Notebook
•  Google Colab
•  Kaggle Code
•  Jupyter Lab (WIP: There're still some tiny CSS issues)
•  Databricks Notebook (Since version 0.1.4a0, needs more tests)
•  Jupyter Extension for Visual Studio Code (Since version 0.1.4a1, needs more tests)
•  ...feel free to raise an issue for more environments.

Run in Kaggle	Run in Colab

Setup pygwalker

Before using pygwalker, make sure to install the packages through the command line using pip.

pip install pygwalker

Note

For an early trial, you could install with pip install pygwalker --pre for pre-releases or even pip install git+https://github.com/Kanaries/pygwalker@main to obtain latest features and bug-fixes.

Use pygwalker in Jupyter Notebook

Import pygwalker and pandas to your Jupyter Notebook to get started.

import pandas as pd
import pygwalker as pyg

You can use pygwalker without changing your existing workflow. For example, you can call up Graphic Walker with the dataframe loaded in this way:

df = pd.read_csv('./bike_sharing_dc.csv', parse_dates=['date'])
gwalker = pyg.walk(df)

You can even try it online, simply visiting , Google Colab or Kaggle Code.

That's it. Now you have a Tableau-like user interface to analyze and visualize data by dragging and dropping variables.

Cool things you can do with Graphic Walker:

• You can change the mark type into others to make different charts, for example, a line chart:

• To compare different measures, you can create a concat view by adding more than one measure into rows/columns.

• To make a facet view of several subviews divided by the value in dimension, put dimensions into rows or columns to make a facets view. The rules are similar to Tableau.

• You can save the data exploration result to a local file.

For more detailed instructions, visit the Graphic Walker GitHub page.

Resources

• Check out more resources about Graphic Walker on Graphic Walker GitHub
• We are also working on RATH: an Open Source, Automate exploratory data analysis tool that redefines the workflow of data wrangling, exploration and visualization with AI-powered automation. Check out the Kanaries website and RATH GitHub for more!
• If you encounter any issues and need support, join our Slack or Discord channels.
• Share pygwalker on these social media platforms:

Download Details:

Author: Kanaries
Source Code: https://github.com/Kanaries/pygwalker 
License: Apache-2.0 license

#jupyternotebook #visualization #pandas #dataanalysis #tableau 

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 Visualization and Analysis with Quicksight & Tableau

Learn Business Intelligence and Data Visualization with practical examples using Amazon Quicksight and Tableau. Create visualization charts on Quicksight and Tableau

You will learn to import various types of dataset in a BI tool. Various operations could be performed on this imported dataset such as adding filters and creating new columns or modified data values using calculated fields. We can also remove certain data elements using excluded list and conditional formatting. You will learn about these concepts in step wise manner.

Moreover you would also learn to create data visualization charts that helps you visualize information in compressed form in categorized way. You could find insights from complex data using a combination of charts that can be further dived deeper by adding filters. You would learn to create various types of data charts on both Tableau and Quicksight.

You will learn various concepts with practical examples in Tableau such as:

•        How to import an excel file in Tableau
•        Various chart types
•        Creating Map Visualization
•        Converting measures to dimensions and vice versa
•        Calculating Measure and creating a dual axis chart
•        Creating multiple worksheets
•        Creating a dashboard
•        Using map as a filter
•        Creating custom filters
•        Adding filters to the dashboard
•        Creating an Analytics dashboard with filters
•        Creating a Calculated field
•        Butterfly chart
•        Word Cloud

Moreover you would learn various lessons on AWS Quicksights such as:

•        Getting Started with Quicksight
•        Importing dataset and understanding group and values
•        Creating Treemap and Customizing charts
•        Data Preparation- Editing Dataset before creating Charts
•        Create a Calculated Field using Functions- ceil and concat
•        More calculated fields
•        Creating Filters and Excluded list
•        Map Chart and Conditional Formatting
•        Pivot table

What you’ll learn

•        You could create visualization charts on Quicksight and Tableau
•        You could find meaningful insights from the dataset
•        You could arrange Data charts on a dashboard
•        You could perform various operations such as using filter, calculated fields, etc

Are there any course requirements or prerequisites?

•        No experience required in Data Visualization or Business Intelligence, you will learn everything from beginning.

Who this course is for:

•        Professionals curious to learn Business Intelligence and Data Analysis
•        Anyone looking to Learn AWS Quicksight and Tableau

#tableau #quicksight #aws #businessintelligence #dataanalysis #datavisualization

Learn Tableau from Scratch

Learn Tableau from scratch, create rich visualization and share with others. You'll learn: Install Tableau Public, Tableau concepts, Common Chart types, Calculated Fields, Sorting and Filtering, Crosstabs, Dashboards, and more

This course is designed by considering the fact that you are an absolute beginner, and I will teach you even the most basic concepts in Tableau from scratch so that after completing this course you would be able to create the rich visualizations easily and confidently. In this course, you will get the problem at the start of the section, and then we will together solve this problem and learn all the concepts while solving it.

What you’ll learn

•        Install Tableau Public
•        Understand the core concepts of Tableau - Data Types, Measures and Dimensions, Aggregation and Granularity, Discrete and Continuous fields
•        Common Chart types - Bar Chart, Line Chart, Map Chart
•        Calculated Fields
•        Sorting and Filtering
•        Crosstabs
•        Dashboards

Are there any course requirements or prerequisites?

•        Enthusiasm to learn
•        Access of computer and Internet

Who this course is for:

•        Anyone who wants to learn Tableau from scratch.
•        Already working on Tableau and revisit the core concepts

#tableau 

Which Data Visualization Tool To Choose: Tableau vs QlikView

Tableau vs QlikView

When we talk about data visualization tools, two names definitely pops up in our mind – Tableau and QlikView. But it has always been a dilemma which tool to choose out of the two. There has always been a silent battle between the two tools – Tableau vs QlikView. Both of these tools are in Leaders quadrant of Gartner’s magic quadrant among BI tools. Take a look at the diagram below:

In order to decide which tool to use for Data Visualization, it is very important for you to understand about both of these tools. It’s wrong to just listen to the word of mouth and just go with the majority. You can always be the better judge.

So, I will tell you all you need to know about Tableau and QlikView and you will tell me in the comment section below which tool do you think is the best and why. Fair enough? ;)

And since we are talking about data visualization tools, let’s move ahead in this Tableau vs QlikView blog and understand what is data visualization first. Then only we can observe whether these tools fit the requirements or not.

Tableau vs QlikView: Data Visualization

Big Data is booming!

In order for Big Data to have an enormous impact on the way information is disseminated and analyzed, data visualization plays a tremendous role. There are many organizations that find it real challenging to interpret data, and this is exactly where data visualization comes to the rescue. It is a generic term that describes an organization’s efforts to help them comprehend the significance of data by converting it into visual content. There are many important elements, including patterns and trends that often go unobserved in text-based data, but can be easily noticed with data visualization. It also helps digest information in the form of heat maps and rich graphical representations.

A picture conveys the meaning better than text. Similarly, data visualization provides access to rich visual elements that enhance the quality and ease of comprehension of the information being conveyed. Another vital benefit of data visualization is the stage at which it is employed. At this stage, the analyzed data passes on from the data scientists to business stakeholders. The latter group needs to be presented with the insights in a meaningful way so that they can understand the impact and influence of the insights. This is where data visualization comes in handy.

Therefore, it is very important to understand which is the best tool that will provide us with better solutions.

Let’s move ahead in this Tableau vs QlikView blog and compare these tools with the following parameters:

1. Ease of Use

QlikView : It is easy to use and explore the hidden trends. To search, just type any word in any order into search box for instant and associative results and it will show connections and relationships across your data. It is difficult for user to design their own views due to menu driven properties.

Tableau: Its interface is simple, not filled with too many features at one page and has a drag and drop interface. It does not provide feature to search content across all your data. User can easily create their own views using various objects and it is easy because of well designed GUI interface.

2. Ease of Learning

QlikView: It has actively engaged community and resources to help you learn this software in the best possible manner.

Tableau: It also has actively engaged community and resources. It is a simple drag & drop application which makes it very easy to learn.

3. Cost 

QlikView: Its personal edition is free with limitation of document sharing. Each named user license is $1,350 and $15,000 for a concurrent user. Server license is $35,000/server. Additional $21,000/server for PDF distribution service; $22,500 for SAP NetWeaver connector.
May require RAM upgrades if large numbers of concurrent users.

Tableau: Free Desktop version called “Public” that makes data is available for all to download. Private versions come with fixed fee $999 or $1,999 depending on data access. Tableau Server – anecdotal evidence says $1000/server user, with minimum of 10 users plus maintenance.

4. Connectivity with Other tools/ Language or Database

QlikView: It integrates with a very broad range of data sources like Amazon Vectorwise, EC2, and Redshift, Cloudera Hadoop and Impala, CSV, DatStax, Epicor Scala, EMC Green Plum, Hortonworks Hadoop, HP Vertica, IBM DB2, IBM Netezza, Infor Lawson, Informatica Powercenter, MicroStrategy, MS SQL Server, My SQL, ODBC, Par Accel, Sage 500, Salesforce, SAP, SAP Hana, Teradata, and many more. It can connect with R using API integration. It can connect with Big data.

Tableau: It can integrate with a broader range of data sources including spreadsheets, CSV, SQL databases, Salesforce, Cloudera Hadoop, Firebird, Google Analytics, Google BigQuery, Hortonworks Hadoop, HP Vertica, MS SQL Server, MySQL, OData, Oracle, Pivotal Greenplum, PostgreSQL, Salesforce, Teradata, and Windows Azure Marketplace. It can connect with R that powers the analytical capabilities of the tool. It can also connect with Big data sources.

5. Deployment Process & System Requirement

QlikView: QlikView has its own data warehouse and addition of scripting feature adds more value to it. We can use multilevel layers in QlikView deployment. QlikView is easily deploy-able and configurable, and starts producing stunning reports within minutes of installation. This product does not use cubes; hence loads all the tables and charts in memory to enable interactive queries and creation of reports—a technology not found in other products. It can be developed on both 32 and 64 bit. Its associative technology makes data modeling easier.

Tableau: It does not have its own data warehouse. It can not create layers while connecting with data set. It is more easier to deploy because it requires more structured data.

6. Insight Generation

QlikView: Associative technology makes it more powerful and it helps to read association between variables easily. This feature sometimes help businesses to understand hidden relation between data points.

Tableau: Story telling feature helps you to create presentation, using your available data points.

7. Visualization Objects

QlikView: It has good options available to visualize information. It is loaded with various objects. We can play with properties of these objects easily to customize it. We can also create custom charts like waterfall, boxplot, geo-spatial charts by customizing properties. While inserting object, it has layout and formatting options similar to theme of the document. Here, we need to work on formatting options to make it more visually appealing.

Tableau: It has good visualization objects with better formatting options. It has very good visualization for geo-spatial visualizations. It has numerous options of visualizing your data. The visualizations are always in the best quality.

8. Mobility

QlikView: QlikView is not dependent on a device, we can easily access it from anywhere. Decision making becomes much faster compared to traditional methods.

Tableau: It is also available on all devices and can be accessed over internet. You can use Tableau on your laptop, tablet or phone.

9. Security

QlikView: It has various security options like security for Script, Document, Section Access and user authentication. Direct access to the QlikView Document using QlikView Desktop is always governed by Windows NTFS File Security. Access to the web-based QlikView Enterprise Management Console is restricted to Windows Users who are a member of a particular local Windows Group.

Tableau: Tableau has good security feature and it is highly handled by Tableau server. Tableau is a modern enterprise analytics platform that enables self-service analytics at scale through governance. Security is the first and most critical part of a data and content governance strategy. Tableau Server provides the comprehensive features and deep integration to address all aspects of enterprise security. Tableau helps organizations promote trusted data sources for all users, so the right data is used to make the right decisions quickly.

10. Scalability

You may have heard Tableau offers better scalability than QlikView, or that QlikView can scale faster than Tableau. The truth is, both vendors can handle a huge amount of data. In fact, the majority of organizations are not producing that much of data which either solution can’t handle.

11. Architecture

Now, let us take a look at the architecture of both the tools. This will help you identify the flexibility of both the tools.

Tableau vs QlikView: Tableau Architecture

Tableau Architecture is mostly focused on three stages : Integrating, Analyzing & Visualizing.

Tableau vs QlikView: QlikView Architecture

QlikView Architecture is based on three parts: Front End, Back End and Resources.

12. Basic Charting

Let’s see how does each of these tool make visualizations:

Tableau:

QlikView:

This is how a QlikView Dashboard looks like:

The most important thing you should know that QlikView is used for data discovery rather than just visualizing the data. On the contrary, Tableau is a very powerful tool for visualization only. Now, I have mentioned the parameters and compared both the tools. To be very clear, you can use Tableau & QlikView for different purposes.

This brings us to the end of Tableau vs QlikView blog. I am sure with the above comparison you will be able to identify which tool suits you the best for your needs. I will be coming up with blogs on QlikView very soon and if you want to learn about Tableau, you can check out this blog.                     

If you wish to master Tableau or QlikView, Edureka has a curated course on Tableau Certification or QlikView Training & Certification which covers various concepts of data visualization in depth. It comes with 24*7 support to guide you throughout your learning period. New batches are starting soon.

Got a question for us? Please mention it in the comments section and we will get back to you at the earliest.

Original article source at: https://www.edureka.co/

#datavisualization #tableau #qlik #view 

What is Tableau?

What is Tableau? Visualizing Data Using Tableau

Data Visualization with Tableau

This blog is the first of the Tableau Tutorial blog series by edureka. Tableau Certification is one of the most sought-after skills in Data Visualization. In this part of the blog-series “What is Tableau?”, I will explain to you what exactly is tableau and how to work on it, thereby showcasing you the real power of data visualization using Tableau.

1. Power of Data Visualization: Anscombe’s Quartet

2. But Why Tableau?

3. Establishing Connection with Tableau

4. Tableau Desktop UI

5. Understanding Tableau’s UI and its Terminologies

6. Creating Visualization in Tableau

Power Of Data Visualization: Anscombe's Quartet

This example of Anscombe’s Quartet will show you the Power of Data Visualization.

Now you would ask “ what is Anscombe’s quartet? ” 

Well, Anscombe’s quartet comprises four datasets that have nearly identical simple descriptive statistics, yet appear very different when graphed. Each dataset consists of eleven (x,y) points.

In the below image, you will notice that the data points in each dataset is very similar, but when you create a graph for the same, each dataset comes up with its own visualization.

But Why Tableau?

Access to Multiple Data Connection

Tableau easily connects to nearly any data source, be it corporate Data Warehouse, Microsoft Excel or web-based data

Live Analysis

ShowMe

Maps

Establishing Connection

Use Tableau and get connected to different data sources from files and servers. You can work on various file formats like CSV, JSON, TXT or even get your data imported from servers like Tableau Server, MySQL, Amazon Redshift & many more.

Tableau Desktop UI

Since we have established connection with Tableau, now its turn to understand Tableau’s UI and see how fields from the Measures and Dimension (under the Data Pane) play an important role in creating visualization. You will also learn about Page Shelf, Filter Shelf and Marks Card.

Understanding Tableau's UI and its Terminologies

Measure

Measures are the fields which can be measured, aggregated or used for different calculation purpose. It helps you to answer your business-related questions. Generally, a field containing a numeric value is placed under the measure.

Ex: Discount, Profit, Sales, etc

Dimension

The dimension consists of those values which cannot be aggregated. It is used for categorizing facts.

Ex: Category, Country, City, etc

Page Shelf

Page Shelf allows you to analyze your data based on the individual values contained within a field. It creates a set of pages, with a different view on each page.

Filter Shelf

Filter Shelf allows you to add or remove data from your view. You can add Dimensions and Measures from the data pane to filter shelf to filter your data. Once filtered the fields will be displayed on the Filter Shelf.

Row/Column Shelf

Use Row and Column Shelf to add dimension and measure to complete the visualization. Row is treated as the X-Axis and Column as Y-Axis.

Marks Card Shelf

Mark Shelf is a region in Tableau where you can drag your fields to set mark properties.It helps you in enhancing visualization by setting colour, size, label, detail, path or shapes.

Creating Visualization with Tableau

I guess by now you are well versed with Tableau’s Terminologies. Lets move ahead and explore some of the visualization which you can easily create in Tableau. Following is an interactive visualization where you can explore the different types of graphs including: Bar Chart, Heat Map, Scatter Plot, Packed Bubble and few others.

I hope you got a fair idea of “What is Tableau?” from this blog. Still hungry for more knowledge? Don’t worry the next blog on Tableau Tutorial will give you better understanding of the tool. I will showcase many real-life scenarios in that blog.

If you are interested in getting certified in Tableau, hit the below button to explore more about Data Visualization Using Tableau Training and Certification by Edureka. You can also subscribe to our YouTube Channel to get more free content from Edureka.

Original article source at: https://www.edureka.co/

#tableau #data 

Learn Data Visualization using Tableau

Tableau Tutorial – Learn Data Visualization Using Tableau

When you talk about Data Visualization, the first word that comes to your mind is – Tableau. I am guessing that’s why you might be here reading this Tableau tutorial because you have heard about the advantages of Tableau. The demand for Tableau certification training is at peak because of its increasing adoption in the market.

This is my first blog in this Tableau Tutorial blog series which will explain how to get started with Tableau. Hope you will enjoy learning about Tableau with this Tableau Tutorial blog.

 Well you came to the right place. In this Tableau Tutorial, you will be learning the following topics:

• Importance Of Data Visualization
• Data Visualization Tools
• Why Tableau?
• What Is Tableau?
• Tableau Product Family
• Understanding More About Tableau
• Visualizations
• Use Case – Datamatics

Let us first start this Tableau tutorial by understanding the concepts behind Data Visualization.

Importance Of Data Visualization

Data Visualization is one of the most important part of data analysis. It has always been important to present the data in an understandable and visually appealing format. Data visualization is one of the skills that Data Scientists have to master in order to communicate better with the end users.

Data is the word here.

Let me first give you an idea of the kind and amount of data that we are dealing with. According to SiliconAngle, there was 2.5 zetabytes of stored data world over in the year 2012 and it is set to hit more than 50 zetabytes mark by the end of 2017. To put things in perspective, this data has largely been produced by websites and cross platform transactions. Add to it the fact that there would be a total of 20 billion “smart” devices connected to the internet by the end of 2020 and the numbers can be baffling!

Data visualization allows data scientists to converse with their end users. The outcome of data analysis is not immediately comprehensible to the people who do not directly deal with data. Data visualization bridges that gap and makes people appreciate the possibility of data analysis.

Let us consider the following example to understand this:

The following image shows the x and y co-ordinates of different points to be plotted on a graph. The numbers look almost the same, don’t they? Maybe the lines will look similar after we plot each set on the graph.

So, now take a look at the image below when we plot these points in our graph:

You see how different they look like when you actually visualize it. It wasn’t possible for us to figure out the nature of each line until we visualized it.

Tableau Tutorial: Introduction To Data Visualization Tools

Here are the top 5 data visualization tools that are being used extensively in BI:

1. Tableau
2. Qlikview
3. Domo
4. Microsoft power BI
5. Excel

Let us know a little bit about all of them.

Tableau:

Basic version of Tableau data visualization tool is free which can perform regular tasks such as:

•   Sales data analysis
•   User density monitoring
•   Consumer segmenting
•   Tracking budgeting expense
•   Categorizing and sub-categorizing data

Excel:

You can actually do some pretty complex things with Excel, from ‘heat maps’ of cells to scatter plots. As an entry-level tool, it can be a good way of quickly exploring data, or creating visualizations for internal use, but the limited default set of colors, lines and styles make it difficult to create graphics that would be usable in a professional publication or website.

Microsoft Power BI:

Microsoft Power BI is a cloud-based business intelligence and analytics service that provides a full overview of your most critical data.

Connecting to all of your data sources, Power BI simplifies data evaluation and sharing with scalable dashboards, interactive reports, embedded visuals and more.

Domo:

Domo is designed to be available for all business users, regardless of technical expertise, to help them make better business decisions.

Domo recently launched Business Cloud, the world’s first open, self-service platform to run an entire organization. Business Cloud brings together the data, the people and the insights users need to find answers to critical business questions and make faster, better-informed decisions to improve performance.

Qlikview:

The QlikView business discovery platform is one of a few visual analytics tools offered by Qlik. QlikView can’t create the same elegant visualizations that the other tools offer, but the software’s dynamic model means that you can quickly analyze your data in multiple dimensions. In addition, QlikView is able to work off of data in memory instead of off your disk, allowing for real-time operational BI environments (like monitoring financial transactions).

QlikView is able to work with a wide variety of data sources, including SAP, Oracle, Salesforce.com and other legacy data files like Excel spreadsheets. What’s more, QlikView can combine these disparate data sources into a single visualization or dashboard.

But now the big question is which tool should you go for? Well, I say go with Tableau. Take a look below to know why.

Why Tableau?

Below are some of the pros or features of Tableau which will spellbind you to start using it right away!

Tableau Features:

1. Apt visualizations:

Tableau connects to many different data sources and can visualize larger data sets than Power BI can. Once in Tableau, a dashboard shows the basics of the users’ data. The user can then drill down into data sets by downloading a worksheet. From there, they can apply various visualizations to the data.

In Tableau, you select the data and switch between visualizations on the fly. It’s easier to jump between visualizations in Tableau.

Tableau visualizes data from the start, allowing you to see the significance right away. Tableau differentiates correlations using color, size, labels and shapes, giving you context as you drill down and explore on a granular level.

2. Depth of discovery:

The features of Tableau gives users ways to answer questions as they investigate data visualizations. The solution can show basic trends as predictions, use “what if” queries to adjust data hypothetically, and visualize components of data dynamically for comparisons.

3. Implementation:

Tableau provides a variety of implementation and consulting services. For enterprise-level deployment, there’s a four-step process spanning weeks, and for smaller-scale deployments, there are quick-start options that can complete setup in a matter of hours.

Tableau provides a variety of implementation and consulting services. For enterprise-level deployment, there’s a four-step process:

• Phase 1 – This phase involves IT planning, architecture consulting, pre-install checkup, server installation and verification, and validation of security configuration.
• Phase 2 – Phase 2 involves working with data and data migration, including data modeling, data mining, data extraction, data sources and business workflow.
• Phase 3 – In Phase 3, there’s a two-day classroom training covering Tableau Fundamentals, hands-on advanced coaching, and building and formatting visualizations.
• Phase 4 – This final phase helps companies expand Tableau usage across their business. It includes implementation workshops where topics such as evaluating action plans and defining measurable outcomes are discussed.

4. Automation functionality:

Tableau Certification Training Course

• Instructor-led Sessions
• Real-life Case Studies
• Assessments
• Lifetime Access

Explore Curriculum

Tableau is a little more intuitive with creating processes and calculations. For example, when creating calculations in a tabular format, the formula can be typed once, stored as a field and applied to all rows referencing that source. This makes it easier to create and apply recurring processes. Tableau’s flexibility also allows users to create custom formulas that aren’t available in most of the tools.

5. Data source connectors:

Tableau offers hundreds of native connectors to easily pull, cleanse and correlate data from practically any source without having to create custom code.

Tableau extracts large data sets from sources for quick, ad-hoc analysis using two different methods: Live Connection and In-memory. Both adapt to your local database and, based on the size and capacity, sync data quickly by extracting the relevant data to a query. It also has a general Open Database Connectivity (ODBC) connection for any connections that don’t have a native connector provided. This is the reason, you can see an increasing demand graph for Tableau certification training.

Tableau Product Family

1. Tableau Desktop:

It is a self service business analytics and data visualization that anyone can use. It translates pictures of data into optimized queries. With tableau desktop, you can directly connect to data from your data warehouse for live upto date data analysis. You can also perform queries without writing a single line of code. Import all your data into Tableau’s data engine from multiple sources & integrate altogether by combining multiple views in a interactive dashboard.

2. Tableau Server:

It is more of a enterprise level Tableau software. You can publish dashboards with Tableau Desktop and share them throughout the organization with web-based Tableau server. It leverages fast databases through live connections.

3. Tableau Online:

This is a hosted version of Tableau server which helps makes business intelligence faster and easier than before. You can publish Tableau dashboards with Tableau Desktop and share them with colleagues.

4. Tableau Reader:

It’s a free desktop application that enables you to open and view visualizations that are built in Tableau Desktop. You can filter, drill down data but you cannot edit or perform any kind of interactions.

5. Tableau Public:

This is a free Tableau software which you can use to make visualizations with but you need to save your workbook or worksheets in the Tableau Server which can be viewed by anyone.

Understanding Tableau

Let us start by taking a look at the datatypes that Tableau supports. Refer to the diagram below which shows all the compatible data types of Tableau.

The above diagram shows you the data types that Tableau supports with  respective examples.

Now, the data types that we are dealing with can also be categorized broadly into two categories and they are:

• Measures
• Dimensions

Refer to the diagram below to understand the differences between Dimensions and Measures.

In order to make it more understandable to you, A dimension is used to add more detail to describe your data.

How To Use Tableau?

You just need to follow the below 3-step mantra to use Tableau:

1. Connect to data
2. Play around with the UI
3. Create visualizations

1. Connect to data

The first thing to do in Tableau is to connect to your data. There are mainly two types of connections-

Connecting to your local file or connecting to a server.

Tableau can connect to any local file or database such as-

• Excel
• Text File
• Access
• Statistical File, or
• Other Database file.

Local connection gives the maximum speed of data processing.

Tableau can connect to your data server too. It can connect to almost any type of data server. Below are some of the most popular databases that Tableau can connect:

• Tableau Server
• Google Analytics
• Google BigQuery
• Hortonworks Hadoop Hive
• MapR Hadoop Hive
• IBM DB2
• IBM BigInsights
• IBM Netezza
• Microsoft SQL Server
• Microsoft Analysis Services
• Oracle
• Oracle Essbase
• MySQL 
• PostgreSQL
• SAP

While working on Tableau, data can have Live Connection where any change in the source data will be automatically updated in Tableau. On the other hand, data can be Extracted to Tableau repository so that any change made here will not affect the original source data.

Data Joins

You can also integrate different data-sets together to link up and produce better insights. There are different ways to join data-sets. Refer to the diagram below to understand them all.

In the above diagram shows the four data-set join options available in Tableau.

2. Play around with the UI

This is how the user interface looks like:

UI- Show me the data

This is the pane with which you can create visualizations. You can create different visualization in order to represent your dataset.  The diagram below shows the ‘show me’ data pane:

Some visualizations might not be available at times because of incompatible dataset.

Below are the most popular visualizations used widely in Tableau:

Tree Map

Heat Map

Now let’s explore few more options available in our UI.

Menu

The menu bar in Tableau consists of various options to edit your visualization. Let me take you through them one by one. 

File menu

This Menu is used to create new Tableau workbook and open existing workbooks from both local system and Tableau server. The important features in this menu are:

• Workbook Locale to set the language to be used in the report.
• Paste Sheets to paste a sheet into the current workbook which is copied from another workbook.
• Export Packaged Workbook option is used to create a packaged workbook which will be shared with other users.

Data Menu

This Menu is used to create new data source to fetch the data for analysis and visualization. It also allows you to replace or upgrade existing data source.

The important features in this menu are as follows:

• New Data Source to see all the types of connections available and choose from it.
• Refresh All Extracts to refresh the data form source.
• Edit Relationships option is used to define the fields in more than one data source for linking.

Worksheet menu

This Menu is used to create new worksheet along with various display features like showing the title and captions etc.

The important features in this menu are as follows:

• Show summary to see the summary of the data used in the worksheet like count etc..
• Tooltip to show the tooltip when hovering above various data fields.
• Run Update option is used to update the worksheet data or filters used.

Dashboard menu

This Menu is used to create a new dashboard along with various display features like showing the title and exporting the image etc..

The important features in this menu are as follows:

• Format is used to set the layout in terms of colours and sections of the dashboard.
• Actions to link the dashboard sheets to external URLS or other sheets.
• Export Image option is used to export an image of the Dashboard.

Story Menu

This Menu is used to create a new story which has many sheets or dashboards with related data.

The important features in this menu are as follows:

• Format is used to set the layout in terms of colours and sections of the story.
• Run Update to update the story with latest data form source.
• Export Image option is used to export an image of the story.

Analysis Menu

This Menu is used for analyzing the data present in the sheet. Tableau provides many out of box features like calculating the percentage and doing a forecast etc..

The important features in this menu are as follows:

• Forecast to show a forecast based on available data.
• Trend Lines to show the trend line for s series of data.
• Create calculated Field option is used to create additional fields based on certain calculation on existing fields.

Map Menu

This Menu is used for building map views in Tableau. You can assign geographic roles to fields in your data.

The important features in this menu are as follows:

• Map Layers to hide and show map layers, such as street names and country borders, and add data layers.
• Geocoding to create new geographic roles and assign them to the geographic fields in your data.

Format Menu

This Menu is used for applying the various formatting options to enhance the look and feel of the dashboards created. It provides features like borders, colours, alignment of text etc..

The important features in this menu are as follows:

• Borders to apply borders to fields displayed in the report.
• Title and caption to assign a Title and caption to the reports.
• Cell Size to customize the size of the cells displaying the data.
• Workbook Theme to apply theme to the entire workbook.

Server Menu

Server Menu is used to login to the Tableau server if you have access and publish your results to be used by others. It is also used to access the workbooks published by others.

The important features in this menu are as follows:

• Publish Workbook to publish the workbook in the server to be used by others.
• Publish Data source to publish the source data used in the workbook.
• Create User filters to create filters on the worksheet to be applied by various users when the access the report.

3. Create Visualizations

Above in this Tableau tutorial, you have seen the different The following table tells you how to choose the right visualization for your dataset out of many available options.

Now, let us have a look at a case study to understand how Tableau can help in solving real-life business problems.

Tableau Use Case – Datamatics

Datamatics is the subsidiary of world’s largest bank based on market capitalization, dealing in securities and stocks investments. It provides services in all major areas of investment like equity, IPO, derivatives, mutual funds, insurance etc.

Business need: 

Being a part of an extremely dynamic industry, tracking the slightest market development is of highest priority for the client. They needed a solution that could enable them to react quickly to the varying market trends. A solution that would be able to generate hassle-free, ad-hoc & secured reports that could provide accurate data visualization.

Challenges:

Unable to react quickly to market developments and regulatory requirements The existing system had a high turnaround time which delayed the overall decision making process.

Restricted and inflexible data visualization reports resulted in poor interpretation of data The system compromised on security aspects and had high IT support dependency Lack of effective versatility to do ad-hoc analyses from multiple viewpoints.

Solution: 

Datamatics selected Tableau, a leading Data Visualization BI tool in the market. As per requirements gathered, various Dashboards and Reports were designed for various levels. Refer to the diagram below to understand how Datamatics made use of Tableau:

Tableau was connected with the existing data bases of the organization. Unique and relevant visual dashboards & reports were developed for people at different levels across the organization as well as to cater the day-to-day needs of different departments.

Results:

• Time taken to generate analytical reports/MIS reduced to 1-2days with zero loss of data.
• Efficient integration of excel and other flat files based data with structured data to create deep & versatile analytical insights.
• Easy and optimized data visualization options to slice & dice the reports for more meaningful and comprehensive viewing.
• Enhanced security features with role based access.
• Maximum level of ease to distribute & share the reports even amongst extended team members.
• Dependency on the IT team or external vendors for report generation was minimized.
• Easy installation and integration of the solution with the existing system reduced.
• Total Cost of Ownership for the client Boosted the identification of cross-selling and up-selling opportunities.

This is how Tableau helped Datamatics and has been helping many other companies worldwide.

I hope you have enjoyed reading this blog. I will be coming up with more blogs in the coming days on Tableau. You will be learning how to make proper insights, dashboards, contributing viz and many more.

You can go ahead and check out this blog too in order to learn more.

If you wish to master Tableau, Edureka has a curated course on Tableau Training Course which covers various concepts of data visualization in depth, including conditional formatting, scripting, linking charts, dashboard integration, Tableau integration with R and more. It comes with 24*7 support to guide you throughout your learning period. New batches are starting soon.

Got a question for us? Please mention it in the comments section and we will get back to you at the earliest.

Original article source at: https://www.edureka.co/

#data #visualization #datavisualization #tableau 

Top 12 Data Visualization Tools You Must Know

Top 12 Data Visualization tools to choose from: Tableau, Google Charts, Zoho Analytics, Microsoft Power BI, Qlik Sense, Plotly, Domo, Infogram, D3.js, Databox, Datawrapper, Highcharts

Data visualization is the process of interpreting or representing data graphically. Generally, it is the graphical or visual representation of data using charts, tables, histograms, and other info-graphics.

Data plays a major role in any type of organization. Data visualization helps us understand certain trends and insights, and make important decisions based on them.

Through visual representation, you can show complex loads of data in a very simplistic manner.

What Are Data Visualization Tools?

Imagine going through a record of one million users and grouping them based on their age range. That would be a very tedious task.

Data visualization tools extract information from such records and present them to you or to anyone who needs the data in a visual way.

In the next section, you'll see some of the best data visualization tools to choose from.

Top Data Visualization Tools To Choose From

In this section, we'll go over the features and availability/pricing plans of various data visualization tools. There are not listed in any specific order.

1. Tableau
2. Google Charts
3. Zoho Analytics
4. Microsoft Power BI
5. Qlik Sense
6. Plotly
7. Domo
8. Infogram
9. D3.js
10. Databox
11. Datawrapper
12. Highcharts

1. Tableau

Tableau landing page

You can use Tableau to access, visualize, and analyze data. It also has a drag and drop feature for a more interactive interface.

Features

• Real-time data analytics.
• Offline support.
• Database and cloud integration.
• AI and ML powered analytics.
• Supports integrations for data scientists.
• Dedicated environment for teams to collaborate and share their work.
• Slack integration for notifications on data alerts, AI predictions, and analytics for team collaborations.
• Great tool for non-technical users, as you don't have to write code for basic usage.

Availability/Pricing

Tableau is not a free tool. Although free trails are available, Tableau offers three plans you can choose from — Tableau Creator, Tableau Explorer, and Tableau Viewer at $70, $42, and $15 respectively per month billed annually.

2. Google Charts

Google Charts landing page

Google Charts is a very efficient tool you can use to visualize or display data on a website. It is commonly used with JavaScript.

The documentation guides show how you can create different chart types like bar charts, bubble charts, calendar charts, histograms, maps, pie charts, and so much more.

Features

• Built for developers. Knowledge of JavaScript is required.
• Wide range of pre-built chart types to choose from.
• No plugins required.
• Real-time data update.
• Cross-browser compatibility.
• Cross-platform portability to iOS and Android.
• Chart types are easily customizable.
• Can be connected to dashboard to display dynamic charts to users.

Availability/Pricing

Google Charts is a completely free product.

3. Zoho Analytics

Zoho Analytics landing page

Zoho Analytics transforms raw data into insights and dashboards. You can connect different sources files, cloud databases, custom apps, and popular business apps to Zoho.

This product is great for businesses because it lets you prepare, analyze, and visualize data.

Features

• Automated insights and predictive analytics with the help of AI and ML.
• Supports low-code and no-code integrations.
• Supports integration with many popular apps like Twitter, YouTube, Mailchimp, Shopify, and many others.
• Collaboration feature.
• Flexible deployment options.
• Data storytelling feature for presentations using slideshows, web portals, and an embedded AI assistant.

Availability/Pricing

Zoho Analytics offers the following cloud plans:

• Basic => $22/month billed yearly.
• Standard => $50/month billed yearly.
• Premium => $125/month billed yearly.
• Enterprise => $495/month billed yearly.

The cloud plan has a free 15-day free trial with no credit card required.

The on-premise plan for users who handle all the deployment and hosting themselves offers these options:

Local server:

• Personal => Free forever.
• Professional => $30/month billed annually.
• Minimum of 5 users.

AWS:

• Personal => Free forever.
• Professional => $0.25/hour of AWS usage.
• Minimum of 5 users.

Azure:

• Personal => Free forever.
• Professional => $0.4/hour Azure infrastructure fee.
• Minimum of 5 users.

Docker:

• Personal => Free forever.
• Professional => $30/month billed annually.
• Minimum of 5 users.

4. Microsoft Power BI

Microsoft Power BI landing page

Microsoft Power BI lets you connect to, model, and visualize your data.

Features

• AI generated answers based on your data.
• Integration to popular apps and features.
• Real-time analytics.
• Secure data analytics.

Availability/Pricing

Microsoft Power Bi has two main plans – Power BI Pro and Power BI Premium at $13.70 and $27.50 per user/month, respectively.

The Power BI Premium plan has a per capacity feature for much larger organizations. This feature starts at $6,858.10 per capacity/month.

5. Qlik Sense

Qlik Sense landing page

Qlik Sense, like the other tools we've listed, has some cool features that makes it a good option for data visualization.

Features

• AI generated insights.
• Automated data preparation.
• Real-time data analytics.
• Interactive dashboards.
• Fully customizable analytics.

Availability/Pricing

Qlik Sense Business plan starts at $30 per user/month billed annually. Their Enterprise and client-managed plans can be assessed by contacting their sales team.

6. Plotly

Plotly landing page

Plotly is a low-code tool for building data visualization apps using the Python programming language.

Features

• Knowledge of Python is required.
• This product is open source and has an active community.
• Can be used offline.
• Open source graphing library for creating line plots, area charts, bar charts, histograms, and more.

Availability/Pricing

Plotly is free and open source.

It also has the Dash Enterprise feature which comes with automated delivery of reports, alerts, and an app manager. This feature is not free.

7. Domo

Domo landing page

Features

• Real-time data analytics.
• Interactive dashboards.
• Data sharing.
• Data apps built for businesses.

Availability/Pricing

According to Domo:

Pricing is based on several components related to your usage of the platform, including data storage, data refresh rates, volume of data queries, and the number of users...

While it's not known exactly what their pricing plans are, you can reach out to them through their pricing page to find out more.

8. Infogram

Infogram landing page

Inforgram is a great tool for creating infographics, reports, dashboards, slides, social media posts, email headers, and more easily.

Features

• Interactive charts.
• Real-time team collaboration.
• Access to project version history.
• Great for creating and tracking social media content.
• Custom tracking links.

Availability/Pricing

Infogram has the following plans:

• Basic => Free forever.
• Pro => $19/month.
• Business => $67/month.
• Team => 149/month.
• Enterprise => Contact the Infogram team through their pricing page.

9. D3.js

D3.js landing page

D3.js is a JavaScript library for manipulating and visualizing data on the web using HTML, SVG, and CSS.

Features

• Knowledge of HTML, CSS, SVG, and JavaScript is required.
• Gallery of different code examples for creating infographics.
• Good documentation for learning how to use the library.

Availability/Pricing

D3.js is free and open source.

10. Databox

Databox landing page

Databox is primarily a dashboard tool. It can be used to track and visualize data from any source.

Features

• Automated reports.
• Alerts and notifications.
• Supports integration popular apps and platforms.
• Customizable templates.

Availability/Pricing

Databox has a free-forever plan that comes with 3 data source connections, all standard features and over 60 integrations.

Here are the other plans:

• Starter => $72/month.
• Professional => 135/month.
• Performer => $231/month.

11. Datawrapper

datawrapper landing page

Datawrapper is a good tool for creating visualization for content like articles, reports, and publications.

Features

• No code or design skills required.
• Live visualization update from data source.
• Responsive on different devices.
• PNG, SVG, PDF export.
• Print-ready graphics.
• Customizable charts.
• Team collaboration through shared folder, Slack, and Teams integration.
• Wide range of visualizations to choose from.

Availability/Pricing

Datawrapper has the following plans:

• Free plan.
• Custom => $599/month.
• Enterprise => Contact the Datawrapper team through their pricing page.

12. Highcharts

Highcharts landing page

Highcharts makes it easy for web and mobile developers to create charts.

Built on JavaScript and TypeScript, Highcharts' libraries work with any back-end database, and can be used for Javascript, Angular, React, VueJS, iOS, R, .NET, Python, Java, Android projects.

Features

• Supports popular tech stacks.
• Easily customizable.
• Documentation and learning resources.

Availability/Pricing

Highcharts has the JS, Stock, Maps, and Gantt plans with the following pricing features:

• Web => $152 per seat annually.
• SaaS => $300 per seat annually.
• SaaS+ => $750 per seat annually.
• OEM => Contact the Highcharts team.

There is also a Highcharts Editor plan that points to a code repository on GitHub. This is currently in Beta.

Summary

If you cant visualize and represent your data graphically, it becomes very difficult to understand as the data grows.

There are so many data visualization tools you can choose from. Some require coding experience while others don't. There are also free and premium tools.

Choosing the right one all comes down to the features you are looking for and how much you're willing to spend if it's not free.

This article should help you narrow it down by listing some of the popular data visualization tools, their features, and pricing plans.

Thank you for reading!

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

#datavisualization #tableau #powerbi #qliksense #plotly #domo #infogram #d3js #databox #datawrapper #highcharts

Compare The Differences Between Power BI Vs Tableau

In this article, let's learn about the difference between Power BI vs Tableau. Microsoft’s Power BI is a solution for business analytics that allows you to analyse and visualise data, draw conclusions from it, and share it with different organisational divisions. While tableau manages the data flow and converting data into usable information. This blog will help you to understand the difference between Power BI vs Tableau.

What is Tableau?

Tableau is a powerful visual analytics tool that is primarily used in the business intelligence industry. This tool empowers organizations to use data to analyze huge volumes of data and solve problems. By making people and organizations more data-driven, Tableau acts as a secure and intuitive analytics platform.  

What is Power BI?

Power BI is an interactive and scalable data visualization software used in data visualization and business intelligence. It has a technology-driven suite that consists of apps, software, connector, and services. Developed by Microsoft, Power BI finds its application in enterprises for creating and publishing business intelligence reports.  

History of Power BI and Tableau

Power BI came into existence in 2010 when Ron George designed it under Project Crescent. It was then available for public download on 11th July, 2011. Later, it was renamed Power BI by Microsoft in September 2013. 

Tableau was founded in 2013 in California as a result of a project of computer science at Stanford with an aim to make data more accessible through the feature of visualization. Later, it was acquired by Salesforce in 2019 for $15.7 billion.

Cost of Power BI and Tableau

Power BI’s premium version costs around $4,995 per capacity per month with storage resources. It also has a free version available for use for anyone. Their pro plan is priced at $9.99 per month.

Tableau is more expensive. Their pro plan starts at $70 per month per user. 

Features of Tableau

• Intuitive dashboard – People with all data literacy levels can easily use Tableau. Its intuitive dashboard and simple UX help users to gain useful insights. 
• Real-time analytics – Users can access crucial data in real-time using Tableau. 
• Ease of integration – Users can integrate Tableau with different data sources, technologies, and formats using its data connectors.
• Sharing and collaboration – Tableau makes it easy for employees to share and collaborate with project reports within the organization.
• Accessible on mobile – The dashboards of Tableau are compatible on mobile and tablets also.
• Support – Tableau’s community support and forums help equip employees with useful training options.  

Features of Power BI

• Interactive desktop – Power BI’s interactive desktop tool allows users to access data and create reports quickly regardless of their data skill levels.
• Custom visualization – Aside from its default standard, organizations can also get access to its custom library of visualization to suit their unique needs. 

If you want to learn how to effectively use Power BI to create stunning data visualizations, then this course on Data Visualization With Power BI is for you! You’ll learn how to connect to data sources, transform and clean your data, and then create visualizations that will help you better understand your data. You’ll also learn how to share your visualizations with others so that they can also benefit from your insights. Enrol now and start learning how to make your data work for you!

• Visibility – Power BI comes with the ability to organize data sets visually, resulting in a better understanding of them and giving organizations an edge over their competitors. 
• Support for data sources – Power BI offers support for multiple data sources to create interactive and appealing visualizations such as Microsoft Excel, SQL, Web files, etc. 
• Stream analytics – It is an important feature of Power BI that enables users to access real-time insights for a rich visualization experience. Users can take advantage of Microsoft tools such as Azure Stream Analytics to set upstream in the dashboard and make timely decisions. 

Tableau products

Some key products in Tableau Product Suite include –

• Tableau Public 
• Tableau Desktop Personal
• Tableau Desktop Professional 
• Tableau Server
• Tableau Reader
• Tableau Online 

Power BI Products

Various products offered by Power BI include –

• Power BI Desktop 
• Power BI Pro
• Power BI Mobile 
• Power BI Embedded
• Power BI Premium 
• Power BI Data Gateway
• Power BI Report Server

Power BI Vs. Tableau

Advantages of Tableau

Tableau offers various advantages making it a knockout data visualization tool such as –

• Easy to use for people with all levels of data literacy 
• Incredibly fast and intuitive design 
• It is easy to learn and understand. For organizations, it involves less cost of training.
• Data Visualization using Tableau: The feature of visualization lets its users customize as per their unique organizational or business needs.
• Tableau is adept at handling bulk amounts of data without lags.
• Tableau offers excellent customer support. Its online community offers various training and learning sessions that offer additional help in solving issues.
• It can do complex table calculations with its easy integration of scripting languages like R and Python.
• It has a storytelling feature for data interpretations.
• It allows users to clean and analyze data for analysis.  

Advantages of Power BI

Power BI offers incredible advantages to users that include –

• Power BI has a free version available for anyone’s use that allows users to access data and create reports instantly.
• It is very easy to use as users do not need advanced data skills to understand it. Being a Microsoft product, its design is based on Microsoft Office 365, which most users are already familiar with.
• Its support stream analytics is one of its most striking features. With the help of real-time analytics, Power BI is favoured by users to make timely decisions.
• It contains a built-in dashboard and standard reports for SaaS solutions.
• You can rely on Power BI to form secure connections in the cloud or on-premises.
• It has various incredible features for data visualization.
• It can easily integrate with R language and Python to use.
• It offers a secure environment for quick deployment.
• Power BI uses artificial intelligence and machine learning to deal with complex data and offer better visualization.
• The Power Query feature offers multiple options to clean the data.
• It does not require manual intervention to refresh the data in the Power BI web service after publishing.
• It offers extensive and flexible data connectivity options. It can connect to various data sources, especially those from Microsoft, to create interactive visualizations. 
• It contains a custom library of visualization apart from its standard library for enhanced features and benefits. 

Disadvantages of Power BI

There are some drawbacks or cons of using Power BI, such as –

• Power BI gets slower when handling large volumes of data and tends to drag.
• The acceptable file size should be lower than 1 GB in Power BI.
• With Power BI, users can only share the dashboard with other users who have the same email domains. 
• It is not possible to mix imported data that has been taken from real-time connections.
• It is not possible to pass dashboard or other entity parameters in Power BI. 

Disadvantages of Tableau

Some drawbacks of using Tableau include –

• Tableau is very expensive when compared to other tools.
• It involves a high cost of training.
• It can be difficult to import custom visualization in Tableau.
• There are no versions of Tableau or even change management.
• Tableau is not ideal for small to mid-sized organizations. It is best for large organizations or businesses that can afford its licensing fee.
• Although Tableau can integrate with over 250 applications, it is difficult to embed reports with other applications.
• It is not backed by artificial intelligence and machine learning.
• It can only connect with Microsoft applications with single sign-on (SSO) such as Dynamics 365, Office 365, Power Apps, etc. 

Conclusion

I hope this blog on Power BI vs Tableau will help to find the difference between each tool. Power BI and Tableau are both powerful data visualization tools that can help you make sense of your data and uncover new insights. Power BI is a more comprehensive business intelligence platform that includes features for data modelling, ETL, and data warehousing. Tableau is primarily a data visualization tool but offers some basic data analysis features. Also, Power BI is more expensive than Tableau, but it offers a complete set of features. Tableau is less expensive and maybe a better choice if you’re primarily interested in data visualization. If you’re still not sure which tool is right for you, the best way to decide is to try them both out and see which one you prefer.

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

#powerbi #tableau 

Power BI vs. Tableau: Difference and Comparison

In this tutorial, you'll learn what both Power BI and Tableau are in detail. I will also create a factual comparison between the two so you can identify which of them you should use for your project.

Tableau and Power BI are both data visualization and business intelligence tools. You can extract data with both tools, visualize the data, analyze it, and turn it into a piece of actionable information.

NB: This article is not a black-and-white comparison of Power BI and Tableau. There are a lot of grey areas between the two and that’s what we are going to look at the most.

What is Tableau?

Tableau became popular in the early 2000s. It is the leading data visualization and business intelligence tool for companies that want to be data-driven.

Tableau can integrate with and get data from a wide variety of sources like Microsoft Excel, Microsoft Access, and Google Analytics. It can even integrate with files like JSON, text, statistical and spatial files.

Tableau has great features such as:

• no code data query
• drag and drop
• real-time analysis
• data filtering
• mobile view
• data connectors
• text editor
• dashboards
• team members collaboration, and tons more.

What is Power BI?

Power BI is a suite of data analysis and visualization tools and services that helps you convert data into visually interactive reports. It was made available to the public in 2011.

Power BI integrates with various data sources such as Excel workbook, SQL server, PostgreSQL, and Microsoft Access. You can then turn this data into any kind of visualization that pleases you. You can also enter your data manually.

That chart could be a pie chart, bar chart, funnel, R and Python Visual, or even a Q & A. Power Bi is a powerful data visualization tool.

The cool features you have access to with Power BI include:

• smooth integration with Microsoft products
• data refreshes
• mobile app
• map creation
• a wide variety of charts
• custom charts with R and Python
• integration with Azure machine learning

Why Use Power BI or Tableau Instead of Excel?

Tableau and Power BI are made for one important thing Excel is not primarily made for – data visualization. I know you can still make charts with Excel, but that functionality is limited in comparison to both Power BI and Tableau.

In addition, Tableau and Power BI are more powerful than Excel when it comes to visuals and dashboards. They also have faster processing times than Excel.

In short, companies and startups that what to be more data-driven should choose Power Bi or Tableau instead of Excel.

Differences between Tableau and Power BI

Final Thoughts

Both Power BI and Tableau perform well in business intelligence, so it is hard to say one is better than the other.

The only conclusion that is relatively easy to draw is that Tableau is more robust than Power BI. This is because Tableau can handle more data and has access to several common and uncommon data sources.

But if you really need to choose one, below are some metrics to consider:

• If you are on a budget, Power Bi might be a better option
• If you have a group of highly skilled data professionals, Tableau is more robust than Power BI, so Tableau is the better option
• If your company is still a startup, you can consider Power BI, but if the company has moved above a small scale and continues to grow, you should choose Tableau.
• If you have a large amount of data to process and you think the data would continue to increase, you should choose Tableau. But if it’s relatively small data to process and you don’t think it will increase so much, Power Bi is a good option.

Thank you for reading.

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

#powerbi #tableau #datavisualization

Tableau Training for Absolute Beginners

Learn about Tableau in this tutorial. This course is designed specifically for freshers and also for those who want to switch career from Excel to Tableau. 

Tableau is a leading data visualization tool used for data analysis and business intelligence. Gartner's Magic Quadrant classified Tableau as a leader for analytics and business intelligence.

Advantages of Tableau

•        Data visualization.
•        Quickly Create Interactive visualizations.
•        Ease of Implementation.
•        Tableau can handle large amounts of data.
•        Use of other scripting languages in Tableau.
•        Mobile Support and Responsive Dashboard.
•        Tableau Company Strategy.
•        Scheduling or notification of reports.

PROBLEMS YOU'VE EXPERIENCED WITH EXCEL IN THE PAST

•        Excel becomes slow or crashes when you have lots of data, formatting and Charts inside a workbook.
•        Mistakenly working on the wrong file you saved.
•        It’s so annoying to email multiple Excel files to the same people everyday.
•        It takes hours to create a dashboard with multiple charts and formula functions.
•        Some time Excel files are used to store the large amount data which is a very wrong decision because to update the data everyday on that file is a headache because it takes so much time to get open and then to get save.

Section 1: Booster Base

•    Lecture 1: Start
•    Lecture 2: Introduction
•    Lecture 3: Download
•    Lecture 4: Install
•    Lecture 5: Connect with Database
•    Lecture 6: Tableau Live & Extract
•    Lecture 7: View tables
•    Lecture 8: Data Types
•    Lecture 9: Tableau Work Sheet Interface
•    Lecture 10: Data Types Symbol
•    Lecture 11: Adding new Sheet
•    Lecture 12: Product Category & Sales
•    Lecture 13: Make Hierarchy
•    Lecture 14: Grouping
•    Lecture 15: Date Filter
•    Lecture 16: Keeponly and Exclude
•    Lecture 17: Granularity
•    Lecture 18: Table into chart form
•    Lecture 19: Important

Section 2: Charts

•    Lecture 22: Introduction to Charts
•    Lecture 23: Charts
•    Lecture 24: Bar Chart
•    Lecture 25: Line Chart
•    Lecture 26: Line Chart size measure
•    Lecture 27: Final

What you’ll learn

•        Basics of Tableau
•        Familiar with tableau
•        Tableau Interface
•        Its a Demo Course only

Are there any course requirements or prerequisites?

•        No prior experience is require

Who this course is for:

•        For Absolute beginners
•        Its only a demo course

#tableau #datavisualization 

Data Visualization using Tableau

Visual Analytics with Tableau | Visualization in Tableau  | Tableau Tutorial 

Learn how to make such simple visualizations in Tableau to understand our data well. Data Visualization using Tableau will allow one to gain an edge over the other analysts and let you present the data in a much better and insightful manner. It would be easier for the learners to immediately implement it in their workplace and create a real-time dashboard for their management using one of the most sought-after tools

Tableau for Beginners – Data Visualisation made easy

Have a look at the visualization below, which was created by a famous Swedish statistician, Hans Rosling. He compiled roughly 200 years of World Development Data and presented it in a very simple manner: 

This above is an excellent example of Data Visualisation, which rather than focussing on what the numbers are, focuses on telling their story. You can find the interactive version of this visual here.

There are multiple Softwares that are available now at instant access which assists in such easy visualizations and one tool that we are going to cover in this article is Tableau.

Sample Dashboard in Tableau

What can you make out from the picture below?

This Dashboard, made on Tableau, visualizes the Sales and Profit Analysis of a Supermarket.

At a glance, you can see:

1. The Sales distribution of various categories relative to each other
2. Their respective Profit margins.
3. Each Category’s Sub – Category Product Sales
4. And finally, the Sales growth of Categories over the years

So, in this article, we will learn how to make such simple visualizations in Tableau to understand our data well.

1. Overview of Tableau

1.1 What is Tableau?

Tableau is a Data Visualisation tool that is widely used for Business Intelligence but is not limited to it. It helps create interactive graphs and charts in the form of dashboards and worksheets to gain business insights. And all of this is made possible with gestures as simple as drag and drop!

What Products does Tableau offer?

1.2 What do you need to know before using Tableau?

You don’t need to know much to use Tableau, but still, a basic awareness of all the types of graphs such as bar graph, line charts, histograms, etc is preferred.

Along with that, it will be beneficial if you possessed some basic understanding of database management ( datatypes, joins, drill down, drill up, etc ) too. Even if you don’t, not a reason to worry since I will be covering all such concepts in this and forthcoming articles.

1.3 Installation :

To work on Tableau, you need Tableau, right?

Out of the five above mentioned products, Tableau Desktop, Public and Online offer Data Visual Creation.

Tableau Desktop  

It is available in the following three formats :

1. Free trial for 14 days
2. If you are a student or a teacher, you get free access to the Desktop for a full year.
3. Purchase Tableau

Tableau Public

Tableau Public is purely free of all costs and does not require any license. But it comes with a limitation that all of your data and workbooks are made public to all Tableau users. 

Tableau Online

Tableau Online is the best option for you, if you wish to make your Workbooks on the Cloud and be able to access them from anywhere.

2. Getting Started

Now that you have the suitable product installed and set up, I am pretty sure that your hands must be tingling with anticipation to finally begin visualizing using Tableau! Well let’s not keep you waiting then, go ahead and launch the tool.

2.1 Connect to the Data

You should see a screen similar to the one above. This is where you import your data. As is visible, there are multiple formats that your data can be in. It can be in a flat-file such as Excel, CSV or you can directly load it from data servers too.

You can see that Tableau itself offers some Sample Workbooks, with pre-drawn charts, graphs, and other visuals. I would suggest going through these later for further exploration.

The best way to learn is to get your hands dirty. Let us start with our Data, which can be found here. The data is that of a United States’ Superstore which is deliberating over its expansion. It wishes to know the prospective regions of the country where it could and hence requires your help.

The first thing that you will obviously need to do is import the data onto Tableau. So quickly follow the below steps:

1. Since the data is in an Excel File, click on Excel and choose the Sample – Superstore.xls file to get :

1. You can see three sheets on the screen, but we are only going to be dealing with Orders here, so go ahead and drag the same on Drag sheets here :

Uh oh, the imported data looks a bit different for the first few rows. Don’t worry, the solution lies right ahead.

Data Interpreter

       3. You see the option of Use Data Interpreter?  Click on it to get the following clean view :

All that messy data magically disappeared!

If you open the Excel data file, you will see some metadata in it, i.e. information about data :

Tableau imports the entire data file as is, but anticipating such discrepancies, explicitly provides a solution in the form of a Data Interpreter. If you wish to view the exact changes that it made, click on Review the results, and choose the Orders tab in the opened Excel sheet.

As it will show, it simply removed the erroneous data.

2.2 Data Visualisations

As soon as you had imported your dataset, next to the Data Source tab near the bottom of the screen, you immediately must have seen Go to Worksheet. A Worksheet is where you make all of your graphs, so click on that tab to reach the following screen :

Don’t get overwhelmed by the various elements that you see here, we will cover them all one by one.

Let’s start with Dimensions and Measures :

Moving onto Shelves :

Visualization in Tableau is possible through dragging and dropping Measures and Dimensions onto these different Shelves.

Rows and Columns : Represent the x and y-axis of your graphs / charts.
Filter: Filters help you view a strained version of your data. For example, instead of seeing the combined Sales of all the Categories, you can look at a specific one, such as just Furniture.
Pages: Pages work on the same principle as Filters, with the difference that you can actually see the changes as you shift between the Paged values. Remember that Rosling chart? You can easily make one of your own using Pages.
Marks: The Marks property is used to control the mark types of your data. You may choose to represent your data using different shapes, sizes or text.

And finally, there is Show Me, the brain of Tableau!

When you drag and drop fields onto the visualization area, Tableau makes default graphs for you, as we shall see soon, but you can change these by referring to the Show Me option.

Note: Not every graph can be made with any combination of Dimensions or Measures. Each graph has its own conditions for the number and types of fields that can be used, which we shall discuss next. 

2.3 Various Graphs and Charts

So far we have pretty much covered the requisite theoretical knowledge. Lets finally begin with some visualizations now.

I personally prefer to start from the shallow side of the pool, slowly swimming towards the deeper end. So I would suggest beginning by getting an overview of the Superstore Sales and Profit Statistics. That would include the Net Sales, the Net Profit and the growth of the two measures, to name a few. Here is a gist of what we will be making :

From what can be observed, the net sales are on the rise, but the Profit is creeping up slowly. We can also quite clearly see the peak Sales Months, which could be attributed to various reasons. We can only know more as we explore more.

Before we start, there is one thing that I would like to recommend and that is you name your Worksheets as being done here. Since I will be referencing them back and forth throughout the article, it will be easier for you to follow.

Let’s begin with the simplest visualization, and that is displaying the Net Statistics numbers. Tableau, being as smart as it is, automatically computes such values under Measure Names and Measure Values. Follow these steps to make what is called a Text Table : 

1. Drag Measure Names from Dimensions onto the central empty area so that you see a Text Table.
2. Measure Names will be displayed automatically onto Rows, so drag it from Rows to Columns.
3. Since we don’t really need Measures like the Row ID, Discount, etc, you can drag them off from below the Marks Pane, to get something like this :

Note: Don’t get confused by the different colors of the fields that you see. Just remember one small trick: Blue means Discrete and Green, Continuous.

So we have the net Sales and Profit values, let’s delve a little deeper by getting the Sales and Profit Values over the years. Let’s make another, but a more detailed, Text Table :

1. Drag Order Date from Dimensions and Sales from Measures to Rows.
2. Right-click on the green Sales Pill, and select Discrete, in place of Continuous, since we want the explicit values and not the bar graphs.
3. Finally, drag Profit on the ‘abc’ column to get :
4. Do the same thing for Monthly Sales and Profit Values, but this time changes the format of Order Date, from Year to Month, by right-clicking on Order Date in the Rows, and choosing Month, to get something like this :

We have just covered the numeric part of the Dashboard, but that is not its selling point. It’s the Line Charts. Lets quickly learn how to make one :

1. To create the chart of Sales and Profit Growth, drag Order Date over the Columns, Sales over Rows and then Profit over the formed Sales axis – so that you see an equals sign – to get the following :
2. Repeat the same to find the Peak Sales and Profit Months, but again change the format of Order Date, from Year to Month, and get :

If you were to click on Show Me, you will see the different types of Line Charts that you can make, and if you were to hover over each of them, you will get to see their Dimension and Measure requirements too. In case you ever feel lost, I recommend referring to Show Me.

With the previous visualizations, we had gotten a brief overview of the Superstore. Let’s dig a little deeper now. The next thing that I can think of exploring is the demographic of the Sales and Profit. What are the States that have the highest Sales Revenue, which ones are generating the maximum Profits:

Before discussing the inferences, let’s first create the Pie Chart of Region Sales :

1. Drag Regions onto Rows and Sales onto Columns.
2. Go to Show Me, and select the Pie Chart.
3. And finally, drag Sales over the Label in the Marks Pane to get :

From the visual, it’s pretty evident that the two opposite ends, East and West are leading in the Sales game. Let’s dissect this a bit more.

Note : Whenever you have some geographical data, it is always advisable to plot and see it on a Map to gain better insights.

So, we are now going to make the Map Chart of State Sales Distribution :

1. Since its the States that we wish to analyze, drag States onto the empty area, so that you automatically see a Map, with small Circles. Follow this step by dragging Profits next. You will notice the size of these circles changing to represent the varying values of Profits. This is called a Symbol Map. But we are going to convert this into a Filled one, by going to Show Me, and selecting the Filled Map.
2. Drag Profits again, but this time onto Label in the Marks Pane, to view the Profit Values mapped as well, like so :

California and New York are the top most sellers from the West and East region, but unfortunately, there are other states such as Texas, Colorado which even after having good Sales, have negative Profits! This is certainly not good news for the Superstore. You can perceive a good analysis for the other States as well.

And lastly, here are the steps for making the Scatter Plot of Sales and Profit Analysis :

1. Drag Sales onto Rows, and Profit onto Columns. You will see one tiny circle, which actually represents the Total Sales and Profit Values.
2. To get more information, drag States onto the graph created, so that these circles/bubbles scatter to represent the individual States.
3. To better understand the central tendency of the data, we have also added a Median axis as Reference Line. This can be easily done by right-clicking on the Sales / Profit Axis – > Adding Reference Line and choosing Median over the default Average Reference.
4. Finally, for some more insight, drag States again, but this time onto Label in the Marks Pane, and get:

The findings from the Map chart become more prominent with the following Scatter plot inferences :

• The states in the top right, with high Sales and high Profits, mean good business for the organization.
• States with positive Sales and Profits, but near the two respective axis are the ones where there is some scope of improvement.
• Whereas the states that belong to the 2nd or 3rd quarter are the ones which are not generating much revenue.

One of the great things about Tableau is that it lets you interact with the visuals. Have a look at an example :

When we clicked on the Central Region, it highlighted and showed the Central States of the US, along with their respective Sales and Profit scatter. Here we used the chart as a Filter itself which is a feature of a Dashboard. We shall learn how to make one at a later stage.

There is one pretty important analysis that we have yet to touch, and that is Product Statistics. High Sales could be easily attributed to the high cost of the products being sold. Also, when you are considering expansion, you will want to know the Sales distribution of the Products too:

Here we have visualized not just the Sales but also the Profits.

Its quite surprising to see Categories that have high Sales, generating negative profits, like Technology in November 2015, or Furniture in October 2016 and this is inferred from the first chart, which is also called a Highlight Table. As the name suggests, it highlights the relative proportion of the Measure Values of our data. So let’s learn how to make one :

1. Drag Category and Order Date ( Year ) in Rows.
2. Drag Order Date (Month ) over Columns, and Sales over the empty ‘abc’ fields
3. Select Highlight Table from Show Me, and drag Order Date ( Year ) back to Rows, in case it got re-shuffled.
4. Finally drag Profits over Colour in the Marks Pane, to get :

The Product Sub Category Sales is a Bar Chart, which is also quite easy to make :

1. Just drag Sub – Category over to the Rows
2. Drag Profit onto the Columns.
3. Go to Show Me and choose the Horizontal Bars
4. For some customization, drag Sales over Colour in the Marks Pane to attain this final visualization:

From the above graph, we are getting a good idea of the Net Sales and Profit margins of the various products. Notice that even though Tables’ Sales are quite high on the scale, it’s the only product with the least profit.

Now, just like before, consider interaction with the visualization:

We are now able to view each Category’s Products’ Sales and Profits, at a low-level granularity of Year and Month!

3. Other Functionalities

Congratulations! You have now covered one of the important aspects of Tableau! But it’s not the end of your learning just yet. Tableau offers some advanced functionalities too, some of which we will cover next :

3.1 Filters

Till now we have only made simple charts, that actually provide cumulative data, which is combined data over the lifetime of the Superstore. To look at Sales of a particular Year, a Month, for a certain Product, or to basically view the distinct aspects of the data, Filters are the way to go.

Let’s head back to the first-ever Chart that we had made, of Peak Sales and Profit Months :

The visual here is an accumulation of all 4 years of data, for all Regions, States, Categories and Sub Categories.

The steps of turning any Dimension into a Filter are the same. Let’s first experiment with the Order Date ( formatted to Year ) :

1. Drag the Dimension to the Filters’ Shelf, to see the following pop up. Here we will be choosing Years :
2. Choose the values that you want to be a part of your Filter :
3.  Right-click on the newly generated Filter, and then choose Show Filter :
4. You can also change the format of your Filter, for example, whether you wish for a Dropdown list, a Slider, a Single Value List, etc :
5. If you feel that some of your filters can be applied to other sheets as well, then rather than repeating the steps, you can simply Apply the Filter to all other relevant Worksheets :

3.2 Drill Down and Drill Up

By now you must have gotten some picture of the way our Data is built. We have Category as the main Field, divided into Sub – Category, which is further distinguished into the various Product IDs and their corresponding Product Names.

This concept of breaking down our data to reach the absolute depth is called Drilling Down :

Similarly, you can drill down from Order Date to Order ID to Ship Date to Ship Mode. This is also referred to as making a Hierarchy of data.

Let’s consider the ProductDrillDown first, which is really a Bar Graph :

1. First, you need to group the Dimensions you want in a single Hierarchy. So, drag Sub – Category from Dimensions on top of Category in the Dimensions itself, and change the Name of the hierarchy to Product.
2. Now drag Product ID and Product Name over this Product Hierarchy 
3. Do the same for Order Hierarchy to get :

       4. To finally plot your data, drag the Product Hierarchy onto Rows and Sales onto Columns, and get:

This was just a simple Bar Graph, but if you hover over the Category axis, you will see a small plus sign. Click on it to get a granulated version of your data. Do the same for the other generated axis as well to get to the absolute depth. 

The Tree Analysis of Product Sales is a TreeMap, which is a great way of representing Drilled Down data, and is quite easy to make :

5. Following the drill down from Step 4, simply go to Show Me and select the TreeMap chart, to get the following :

So far you have analyzed the present scenario, but for expansion consideration, let’s try and analyze the future too.

With the following Dashboard, you can not only see the Trends over the Sales Months, but also a Forecast over the Years too. And both of them tell a different story altogether :

Although the Sales of the Superstore are increasing over the months of a year, the future, in general, looks a bit bleak. The sales seem to become constant for the next 3 years, but fortunately for the Superstore, the Profit is increasing steadily. Let’s get to making the above now.

3.3 Trend Line

Traverse back to the Peak Sales and Profit Month Chart and follow these steps to make a Trend Line of your own :

1. Go to Show Me and choose the Dual Combination chart, to get this chart :

       2. To get the Trend Line, go to Analytics, and simply drag Trend Line over the chart, to get :

3.4 Forecasting

For forecasting, we are going to deal with the Sales and Profit Growth chart. The construction is similar to that of Trend Lines, but with a small change. The steps are :

1. Drag Forecast over the chart.
2. You can also change the time frame of the Forecast, by right-clicking on the Forecast Area and opting for Forecast Options, after which you can make your customizations:

3.5 Clusters

Let’s head back to the Sales and Profit Analysis chart that we had made. Remember the detailed inference that we had generated from it? We are just going to make that a bit more prominent now, using Clusters. To make them :

1. Go to Analytics and choose Clusters.
2. You can format the Cluster formation as per your wishes. Here we are clustering based on the Sum of Sales and Profit, choosing the number of clusters to be 4 :

1. To view the Cluster information, right-click on Clusters in the Marks Pane, and select Describe Clusters, to get this pop up :

4. Dashboard

I am sure by now you must have gotten a pretty good idea of what a Dashboard is, having seen it plenty of times all throughout this article.

If not, well then a Dashboard is simply a means of combining Worksheets together so that they convey some message. Without much further ado, let’s get right to it!  

Consider the State Sales Distribution Map chart and Product Sub Categories.

What if you wanted to know the various Sales margin of each Product within separate States? We had observed that Texas was one of the States with the lowest Profits. By looking at the following Dashboard, you will see that the reason is it’s not managing to generate Profits in majority of the Products :

Now consider the state-wise Sales distribution of a Sub – Category :

The above beautifully shows the distribution of Appliances over the country, where California seems to be the major Profit contributor. 

Making such a Dashboard is actually quite easy. Let’s see how :

1. This time instead of creating a New Worksheet, we are going to create a new Dashboard. Click on the window like icon next to the ‘New Worksheet’ icon in the bottom panel to get the following :  
2. See the multiple Worksheets that we had made till now over on the left? All that we have to do to make a Dashboard is drag these sheets from the pane to the empty area ‘Drop sheets here’.
3. So to make the previously displayed Dashboard, simply drag State Sales Distribution and Product Sub Category Sales. The Dashboard will automatically make space available for both of them.

Note : Even after the creation of the Dashboards, you can still edit your Worksheets, and the same changes shall be reflected here.

If you were to click on the States or the Products after creating your first ever Dashboard, you won’t observe any change. Because for such visuals, we first have to convert the Charts themselves into filters.

4. Simply click on the small Down Arrow on each chart you wish to turn into a Filter, and select Use as Filter:

Note: While making Dashboards, it is preferred to use your charts as Filters, rather than cluttering up the view with custom ones.

5. Story – Bringing it all together

Just like Dashboards were a way to combine the Worksheets, a Story is where you combine all the dashboards, and if need be individual Sheets as well, to convey, as the name suggests – a Story. 

1. Just like before, you simply drag your Worksheets and Dashboards onto the empty space :

So let’s combine all those Dashboards that we had made into what could perhaps make a decent presentation for a beginner. Do ensure to Add a Caption to all of your Dashboards, to convey your message clearly :

If you have ever come across Tableau Stories online, the ones which you could actually interact with, instead of just viewing, that is made possible by publishing your Workbooks onto the Tableau Server.

If you have one set up, then all you need to do, after creating your Stories, is go to Server -> Publish Workbook and enter the Server Name :

6. End Notes

What we have covered so far is pretty much the basics of Tableau. It has various other features which I will be covering in my forthcoming articles. 

As it is said ‘With practice, comes perfection’, it is suggested that you experiment as much as you can with Tableau. 

Below is a sample Dashboard that I would encourage every one of you to try and make. You will not only get to test the skills that you have learned so far but also hopefully acquire more. The dataset used is the same as the one we had been working with so far :

If there are ever any doubts, do leave them as comments

#datavisualization #tableau

Learn About The Most Used Tableau Functions

Learn about the most used string, number, date, logical, and aggregation Tableau functions. 10 Most Used Tableau Functions.

Source: https://www.kdnuggets.com

#tableau 

Tìm Hiểu Về Các Hàm Tableau Được Sử Dụng Nhiều Nhất

Tìm hiểu về các hàm Tableau chuỗi, số, ngày tháng, lôgic và tổng hợp được sử dụng nhiều nhất.

Tableau Functions cung cấp thêm khả năng cho các nhà phát triển trí tuệ kinh doanh để thúc đẩy phân tích phức tạp và thực hiện tính toán toán học. Nó được sử dụng để tăng cường các trường dữ liệu chuỗi, số, ngày tháng và địa lý. 

Chúng ta sẽ tìm hiểu về 10 hàm Tableau được sử dụng nhiều nhất để kiểm tra và phát triển trí thông minh kinh doanh. Các chức năng này sẽ giúp bạn hiểu Tableau không chỉ là một công cụ ưa thích kéo và thả. 

MIN và MAX

Hàm MAX sẽ trả về giá trị cao nhất trong hai đối số. Nó cũng có thể được áp dụng cho các trường dữ liệu, như được hiển thị bên dưới. 

MAX([Sales],[Profit])

Thí dụ:

MAX(10,17) = 17

Đối với MIN thì ngược lại. Hàm sẽ trả về giá trị thấp nhất trong số hai đối số. Các đối số có thể là trường dữ liệu hoặc số nguyên.  

MIN([Sales],[Profit])

Thí dụ:

MIN(14,17) = 14

THAY THẾ

Hàm REPLACE có thể được áp dụng cho các trường và chuỗi dữ liệu chuỗi. Nó yêu cầu ba đối số:

• string : nó có thể là một trường dữ liệu chuỗi hoặc một chuỗi.
• chuỗi con : là từ hoặc bảng chữ cái bạn muốn thay đổi.
• Replace : một chuỗi sẽ thay thế một chuỗi con.

REPLACE(string, substring, replacement)

Thí dụ:

REPLACE("Abid Ali", "Ali", "Awan") = "Abid Awan"

DATEDIFF

Nó được sử dụng để tìm sự khác biệt giữa hai trường ngày. Bạn có thể trích xuất sự khác biệt của tuần, ngày, tháng và năm. 

Hàm yêu cầu 4 đối số:

• date_part : là một đơn vị ngày để trả về sự khác biệt giữa hai ngày.
• date1 và date2 : là các trường ngày tháng
• start_of_week : có thể là Thứ Hai, Chủ Nhật hoặc Thứ Ba. Tất cả phụ thuộc vào yêu cầu của bạn.

DATEDIFF(date_part, date1, date2, [start_of_week])

Thí dụ:

DATEDIFF('week', #2019-10-22#, #2019-10-24#, 'monday')= 1

DATENAME và DATEPART

DATENAME được sử dụng để trả về date_part trong một chuỗi trường dữ liệu ngày. Chúng ta có thể trích xuất ngày, năm, tuần và tháng của một ngày. 

• date_part: là một đơn vị ngày được áp dụng vào ngày
• date: là một trường hoặc chuỗi ngày tháng.
• start_of_week: ngày được coi là ngày đầu tiên trong tuần

DATENAME(date_part, date, [start_of_week])

Thí dụ:

DATENAME('month', #2020-03-25#) = "March"

Thay vì trả về tên của tháng trong chuỗi, DATEPART được sử dụng để trích xuất phần ngày từ một ngày ở dạng số nguyên. Chúng ta có thể sử dụng nó để thực hiện các phép tính phức tạp. 

DATEPART(date_part, date, [start_of_week])

Thí dụ:

DATEPART('month', #2020-03-25#) = 3

Chuyển đổi loại

Đây là hàm được sử dụng nhiều nhất trong Tableau và tôi sử dụng nó để chuyển đổi chuỗi thành ngày tháng, Số nguyên thành chuỗi, chuỗi thành số float và phân tích cú pháp ngày tháng. 

Đây là danh sách các hàm chuyển đổi kiểu:

• DATE (biểu thức)
• DATETIME (biểu thức)
• DATEPARSE (định dạng, chuỗi)
• FLOAT (biểu thức)
• INT (biểu thức)
• STR (biểu thức)

IF và ELSE

Tableau có các hàm điều kiện đơn giản. Bạn có thể thực hiện câu lệnh If else như Python. Chỉ cần đảm bảo rằng bạn thêm “ END” để đóng câu lệnh. 

Tôi sử dụng câu lệnh IF & ELSE để tạo danh mục và vẽ biểu đồ chuỗi thời gian. 

IF <expr> THEN <then> ELSE <else> END

Thí dụ:

If [Profit] > 0 THEN 'Profitable' ELSE 'Loss' END

VÀ và HOẶC

Đối với các hàm logic nâng cao, bạn cũng có thể thêm các lệnh AND và OR để mở rộng biểu thức. 

IF <expr1> AND <expr2> THEN <then> END

Thí dụ:

IF (ATTR([Market]) = "South Asia" AND SUM([Sales]) > [Emerging Threshold] )THEN "Well Performing"

TRƯỜNG HỢP

Tương tự như câu lệnh IF & ELSE , bạn có thể sử dụng CASE để tạo các hàm logic. Bạn có thể áp dụng nó cho một trường dữ liệu và tạo nhiều danh mục dựa trên các biểu thức. 

CASE <expression> WHEN <value1> THEN <return1> WHEN <value2> THEN <return2> ... ELSE <default return> END

Thí dụ:

Tập lệnh bên dưới được sử dụng để chuyển đổi trường chuỗi "Ngôn ngữ" thành số nguyên. Nếu các giá trị bằng tiếng Anh , nó sẽ trả về 1 , cho tiếng Urdu 2 và cho mọi thứ khác là 3 . 

CASE [Language] WHEN 'English' THEN 1 WHEN 'Urdu' THEN 2 ELSE 3 END

TRA CỨU

LOOKUP được sử dụng để tạo các hiệu số trong tập dữ liệu. Tôi chủ yếu sử dụng chức năng này để tạo dự báo và phân tích chuỗi thời gian. 

Nó yêu cầu một trường dữ liệu và tham số offset ở dạng số nguyên. 

LOOKUP(expression, [offset])

Thí dụ:

Bằng cách sử dụng lệnh dưới đây, chúng tôi đã bù đắp Lợi nhuận bằng 2. Bây giờ, chúng tôi có thể thấy giá trị bán hàng từ 2 quý trong tương lai.

LOOKUP(SUM([Profit]), 2)

TabPy

TabPy cho phép người dùng chạy các tập lệnh Python trong Tableau. Bạn có thể cài đặt nó bằng cách sử dụng `pip install tabpy` và chạy máy chủ bằng cách gõ` tabpy` vào terminal. Tìm hiểu thêm về cài đặt Tabpy bằng cách làm theo Hướng dẫn TabyPy .

Bạn có thể dùng:

• SCRIPT_BOOL
• SCRIPT_INT
• SCRIPT_REAL
• SCRIPT_STR

Và mỗi lệnh yêu cầu tập lệnh Python với trình giữ chỗ cho các đối số và danh sách các đối số. 

SCRIPT_REAL(Python Script, argument 1, argument 2, ...)

Thí dụ:

Chúng ta sẽ tạo một hàm tương quan lấy các trường Doanh số và Lợi nhuận và trả về hệ số tương quan. Như bạn có thể thấy, _arg1 và _arg2 là các trình giữ chỗ cho Bán hàng và Lợi nhuận . 

SCRIPT_REAL("import numpy as np 

return np.corrcoef(_arg1,_arg2)[0,1]",

SUM([Sales]),SUM([Profit]))

Tương tự, bạn có thể triển khai hàm Python tới máy chủ TabPy và truy cập nó bằng cách sử dụng cùng một tập lệnh. Đọc Hướng dẫn TabPy: Triển khai các hàm Python và Mô hình dự báo tiên tri để tìm hiểu thêm về cách triển khai các hàm Python. 

Tập lệnh truy vấn TabPy bắt đầu bằng “return tabpy.query (<tên hàm>, danh sách hoặc đối số) ['response']"

SCRIPT_REAL(TabPy Query Script, argument 1, argument 2, ...)

Thí dụ:

Chúng tôi đang truy cập hàm Hệ số tương quan Pearson bằng cách thêm tên hàm ( pcc ), trình giữ chỗ đối số và đối số. 

SCRIPT_REAL("return tabpy.query('pcc',_arg1, _arg2)['response']",

SUM([Sales]),SUM([Profit]))

Nguồn:  https://www.kdnuggets.com

#tableau 

Узнайте о наиболее часто используемых функциях Tableau

Узнайте о наиболее часто используемых строковых, числовых, датовых, логических и статистических функциях Tableau.

Tableau Functions предоставляет разработчикам бизнес-аналитики дополнительные возможности для проведения сложного анализа и выполнения математических расчетов. Он используется для дополнения полей строк, чисел, дат и географических данных. 

Мы узнаем о 10 наиболее часто используемых функциях Tableau для тестирования и разработки бизнес-аналитики. Эти функции помогут вам понять, что Tableau — это больше, чем просто инструмент перетаскивания. 

МИН. и МАКС.

Функция MAX вернет наибольшее значение из двух аргументов. Его также можно применить к полям данных, как показано ниже. 

MAX([Sales],[Profit])

Пример:

MAX(10,17) = 17

Для MIN все наоборот. Функция вернет наименьшее значение среди двух аргументов. Аргументы могут быть полями данных или целыми числами.  

MIN([Sales],[Profit])

Пример:

MIN(14,17) = 14

ЗАМЕНЯТЬ

Функция REPLACE может применяться к строковым полям данных и строкам. Требуется три аргумента:

• string : это может быть строковое поле данных или строка.
• substring : это слово или алфавит, который вы хотите изменить.
• замена : строка, которая заменит подстроку.

REPLACE(string, substring, replacement)

Пример:

REPLACE("Abid Ali", "Ali", "Awan") = "Abid Awan"

DATEDIFF

Он используется для поиска различий между двумя полями даты. Вы можете извлечь разницу недель, дней, месяцев и лет. 

Функция требует 4 аргумента:

• date_part : единица измерения даты для возврата разницы между двумя датами.
• date1 и date2 : поля даты
• start_of_week : это может быть понедельник, воскресенье или вторник. Все зависит от вашего требования.

DATEDIFF(date_part, date1, date2, [start_of_week])

Пример:

DATEDIFF('week', #2019-10-22#, #2019-10-24#, 'monday')= 1

DATENAME и DATEPART

DATENAME используется для возврата date_part в строке полей данных даты. Мы можем извлечь день, год, неделю и месяц даты. 

• date_part: единица измерения даты, применяемая к дате
• date: поле даты или строка.
• start_of_week: день считается первым днем ​​недели

DATENAME(date_part, date, [start_of_week])

Пример:

DATENAME('month', #2020-03-25#) = "March"

Вместо того, чтобы возвращать название месяца в виде строк, DATEPART используется для извлечения части даты из даты в виде целого числа. Мы можем использовать его для выполнения сложных вычислений. 

DATEPART(date_part, date, [start_of_week])

Пример:

DATEPART('month', #2020-03-25#) = 3

Преобразование типа

Это наиболее часто используемая функция в Tableau, и я использую ее для преобразования строк в дату, целых чисел в строки, строк в числа с плавающей запятой и анализа даты. 

Вот список функций преобразования типов:

• ДАТА(выражение)
• ДАТАВРЕМЯ(выражение)
• DATEPARSE(формат, строка)
• ПЛАВАЮЩАЯ(выражение)
• INT(выражение)
• STR(выражение)

ЕСЛИ и ИНАЧЕ

Tableau имеет простые условные функции. Вы можете выполнить оператор If else, например Python. Просто убедитесь, что вы добавили « END» , чтобы закрыть оператор. 

Я использую операторы IF и ELSE для создания категорий и построения графиков временных рядов. 

IF <expr> THEN <then> ELSE <else> END

Пример:

If [Profit] > 0 THEN 'Profitable' ELSE 'Loss' END

И и ИЛИ

Для расширенных логических функций вы также можете добавить команды И и ИЛИ , чтобы расширить выражение. 

IF <expr1> AND <expr2> THEN <then> END

Пример:

IF (ATTR([Market]) = "South Asia" AND SUM([Sales]) > [Emerging Threshold] )THEN "Well Performing"

КЕЙС

Подобно операторам IF и ELSE , вы можете использовать CASE для создания логических функций. Вы можете применить его к полю данных и создать несколько категорий на основе выражений. 

CASE <expression> WHEN <value1> THEN <return1> WHEN <value2> THEN <return2> ... ELSE <default return> END

Пример:

Приведенный ниже скрипт используется для преобразования строкового поля «Язык» в целое число. Если значения указаны на английском языке, будет возвращено 1 , для урду — 2 , а для всего остального — 3 . 

CASE [Language] WHEN 'English' THEN 1 WHEN 'Urdu' THEN 2 ELSE 3 END

ИСКАТЬ

ПРОСМОТР используется для создания смещений в наборе данных. В основном я использую эту функцию для создания прогнозов временных рядов и аналитики. 

Требуется поле данных и параметр смещения в виде целого числа. 

LOOKUP(expression, [offset])

Пример:

Используя приведенную ниже команду, мы компенсировали прибыль на 2. Теперь мы можем видеть объем продаж за 2 квартала в будущем.

LOOKUP(SUM([Profit]), 2)

ТабПи

TabPy позволяет пользователям запускать скрипты Python в Tableau. Вы можете установить его с помощью «pip install tabpy» и запустить сервер, набрав «tabpy» в терминале. Узнайте больше об установке Tabpy, следуя руководству по TabyPy .

Вы можете использовать:

• SCRIPT_BOOL
• SCRIPT_INT
• SCRIPT_REAL
• SCRIPT_STR

И для каждой команды требуются скрипты Python с заполнителями для аргументов и списков аргументов. 

SCRIPT_REAL(Python Script, argument 1, argument 2, ...)

Пример:

Мы собираемся создать функцию корреляции, которая принимает поля « Продажи» и « Прибыль » и возвращает коэффициент корреляции. Как видите, _arg1 и _arg2 являются заполнителями для Sales и Profit . 

SCRIPT_REAL("import numpy as np 

return np.corrcoef(_arg1,_arg2)[0,1]",

SUM([Sales]),SUM([Profit]))

Точно так же вы можете развернуть функцию Python на сервере TabPy и получить к ней доступ с помощью того же скрипта. Прочтите руководство по TabPy: развертывание функций Python и модель прогнозирования Prophet , чтобы узнать больше о развертывании функций Python. 

Скрипт запроса TabPy, который начинается с «return tabpy.query(<имя функции>, списки или аргументы)['response']»

SCRIPT_REAL(TabPy Query Script, argument 1, argument 2, ...)

Пример:

Мы получаем доступ к функции коэффициента корреляции Пирсона, добавляя имя функции ( pcc ​​), заполнители аргументов и аргументы. 

SCRIPT_REAL("return tabpy.query('pcc',_arg1, _arg2)['response']",

SUM([Sales]),SUM([Profit]))

Источник:  https://www.kdnuggets.com

#tableau