SQL Masterclass: SQL For Data Analytics |Simpliv

SQL Masterclass: SQL For Data Analytics |Simpliv

Understand The Basic Concepts all the essential SQL commands, Understanding How to execute your code and analyze the result set, Enhance the performance of your Database.

Description
Why learn SQL?

SQL is the most universal and common used database language.It powers the most commonly used database engines like PostgreSQL, SQL Server, SQLite, and MySQL. Simply put,If you want to access databases then yes, you need to know SQL
It is not really difficult to learn SQL. SQL is not a programming language, it’s a query language. The primary objective where SQL was created was to give the possibility to common people get interested data from database. It is also an English like language so anyone who can use English at a basic level can write SQL query easily
SQL is one of the most sought-after skills by hiring employers
You can earn good money
How much time does it take to learn SQL?

SQL is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn SQL quickly starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to learn SQL quickly.

What are the steps I should follow to learn SQL?

Start learning from the basics of SQL. The first 10 sections of the course cover the basics.
Once done with the basic try your hands on advanced SQL. Next 10 sections cover Advanced topics
Next few sections will cover help you with interviews and Vivas
Practice your learning on the exercise provided in every section
What's the difference between SQL and PostgreSQL?

SQL is a language. Specifically, the "Structured Query Language"

PostgreSQL is one of several database systems, or RDMS (Relational Database Management System). PostgresSQL is one of several RDMS's, others of which are Oracle, Informix, MySQL, and MSQL.

All of these RDMSs use SQL as their language. Each of them have minor variations in the "dialect" of SQL that they use, but it's all still SQL.

Which is better, PostgreSQL or MySQL?

Both are excellent products with unique strengths, and the choice is often a matter of personal preference.

PostgreSQL offers overall features for traditional database applications, while MySQL focuses on faster performance for Web-based applications.

Open source development will bring more features to subsequent releases of both databases.

Who uses these databases?

Here are a few examples of companies that use PostgreSQL: Apple, BioPharm, Etsy, IMDB, Macworld, Debian, Fujitsu, Red Hat, Sun Microsystem, Cisco, Skype.

What's special about this course?

The course is created on the basis of three pillars of learning:

Know (Study)
Do (Practice)
Review (Self feedback)
Know

We have created a set of concise and comprehensive videos to teach you all the SQL related skills you will need in your professional career.

Do

We also provide Exercises to complement the learning from the lecture video. These exercises are carefully designed to further clarify the concepts and help you with implementing the concepts on practical problems faced on-the-job.

Review

Check if you have learnt the concepts by executing your code and analyzing the result set. Ask questions in the discussion board if you face any difficulty.

Bonus Lectures

Apart from this, their is a bonus section which covers important topics from the view of job interviews and Vivas.

The Authors of this course have several years of corporate experience and hence have curated the course material keeping in mind the requirement of SQL in today's corporate world.

Who this course is for:

Working Professionals beginning their Data journey
Anyone curious to master SQL from beginner to Advanced in short span of time
Students about to join their first corporate job
Basic knowledge
Just a PC with any web browser
What will you learn
Knowledge of all the essential SQL commands
Become proficient in SQL tools like GROUP BY, JOINS and Subqueries
Become competent in using sorting and filtering commands in SQL
Master SQL's most popular string, mathematical and date-time functions
Enhance the performance of your Database by using Views and Indexes
Increase your efficiency by learning the best practices while writing SQL queries
Relevant theoretical concepts also covered so that you excel in BI Job interviews and Vivas
Solid understanding of SQL

To continue:

Data Analytics For Beginners

Data Analytics For Beginners

🔥Intellipaat Data Analytics training course: https://intellipaat.com/data-analytics-master-training-course/ In this data analytics for beginners video you wi...

In this data analytics for beginners video you will see introduction to data analytics, what is data analytics, who is a data analyst and role & responsibilities of a data analyst. There is a use case in data analytics as well to get hands on knowledge.

Why Data Analytics is important?

Data analysis is an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analyzing and dissecting data, and presenting the findings in an easily-digestible format.

Why should you opt for a Data Analytics career?

If you want to fast-track your career then you should strongly consider Data Analytics. The reason for this is that it is one of the fastest growing technology. There is a huge demand for Data Analyst. The salaries for Data Analytics is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Data Analytics tutorial is your stepping stone to a successful career!

Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.

In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics

Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.

What Is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

[Source: gfycat.com]

It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.

What is Big Data?

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.

[Source: gfycat.com]

What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.

[Source: ibm.com]

What Does Data Scientist, Big Data Professional & Data Analyst Do?

What does a Data Scientist do?

Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.

Roles of Data Scientist

What do Big Data Professionals do?

The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.

Roles of Big Data Professiona

Big data professionals describe the structure and behavior of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, Kafka etc. based on requirements.

What does a Data Analyst do?

Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.

Roles of Data Analyst

Skill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst

What Is The Salary Prospect?

The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.

A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.

Now, let’s try to understand how can we garner benefits by combining all three of them together.

Let’s take an example of Netflix and see how they join forces in achieving the goal.

First, let’s understand the role of* Big Data Professional* in Netflix example.

Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.

Approach in Netflix

Traditional Data Processing

Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.

Big Data approach to process Netflix data

Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.

2. Improving the streaming experience

How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?

One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.

For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.

By determining all these factors one can improve the streaming experience.

3. Optimize content caching

A set of big data problems also exists on the content delivery side.

The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.

4. Improving content quality

Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.

In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.

By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.

So this is how Data Scientist optimizes the Netflix streaming experience.

Now let’s understand how Data Analytics is used to drive the Netflix success.

Role of Data Analyst in Netflix

The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.

So what does a Data Analyst do?

Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.

5 Prominent Big Data Analytics Tools to Learn in 2020

5 Prominent Big Data Analytics Tools to Learn in 2020

We all knew that Big Data refers to voluminous data gathered from different sources such as mobile phones, social media feeds, IoT devices, databases, servers, and applications, etc. But this data is of no use until and unless it is properly...

We all knew that Big Data refers to voluminous data gathered from different sources such as mobile phones, social media feeds, IoT devices, databases, servers, and applications, etc. But this data is of no use until and unless it is properly manipulated so that it can help to make decisions out of it.

So, to make this data meaningful in a way, certain scientific tools and methodologies have been implemented to extract valuable information from it. The overall process of analyzing data sets about the information with the support of specialized tools and technologies is referred to as Big Data analytics.

Big Data Analytics is used to process a large amount of data sets to uncover hidden patterns, market trends, customer preferences and many other useful information that can be helpful for organizations to make decisions to enhance their business.

With Big data analysis, it is possible to process the data very quickly and efficiently, which was not possible with more traditional business intelligence solutions.

Now in this article, we will focus our discussions towards a few important Big data analytics tools which are trending now in the IT industry. But before that, we want to introduce you to a set of online courses containing different courses related to Big Data concept.

Here is the list of top Big data analytics tools:

1. Apache Hadoop:

Apache Hadoop is a big analytics tool based on java, a free software framework. It facilitates effective storage of huge amount of data in a storage place known as cluster. The special feature of this framework is that it runs in parallel on a cluster and also has the ability to process huge data across all nodes in it.
Features:
• It brings flexibility in data processing
• It allows for faster data processing.

2. HPCC:

HPCC is Big data analytics tool developed by LexisNexis Risk Solutions. It stands for High- Performance Computing Cluster. This technique is more advanced and enterprise-ready. It uses a high-level programming language called Enterprise Control Language (ECL), which is based on C++.

Features:
• It is highly efficient in that it can accomplish Big Data tasks with less code
• It has the ability to automatically optimize code for parallel processing.

3. KNIME:

KNIME stands for Konstanz Information Miner. It is an open-source tool that is used for Enterprise reporting, integration, research, CRM and data mining, etc. It supports many platforms such as Linux, Windows operating systems and many more.
It is considered as a good alternative to SAS.

Features:
• It has rich algorithms set
• It automates a lot of manual work.

4. Datawrapper:

Datawrapper is an open-source platform for data visualization. Its major customers are newsrooms that are spread all over the world. Some of its notable customers are The Times, Fortune, and Twitter, etc.

Features:
• It is a device friendly. It works very well on all types of devices such as mobile, tablet or desktop.
• It has great customization and export options.

5. Lumify:

It is an open-source Big Analytics tool. Its primary features include full-text search, 2D and 3D graph visualization, link analysis between graph entitles, integration with mapping systems, and real-time collaboration through a set of projects or workspaces.

Features:
• It is scalable
• It supports cloud-based environment. Works well with Amazon’s AWS.
Here we have provided 5 prominent tools that are being used in Big Data analytics field. However, you can find a list of many more such tools here.

Wrap up:

Big Data Analytics tools are playing a very important role in the Data Science and Big Data fields. There are a number of Big Data Analytics tools available that are used by different companies. Presently in the IT industry, there is a huge scope for the IT professionals with good knowledge of any of these tools.

Considering this growth, if you are looking to learn Big Data Analytics tools, then visit these online courses that can be of great help to you.

We hope the above discussion helped our readers to know some of the Big Data Analytics tools. We like you to send your thoughts in the comment section below.