About Structured Query Language (SQL) for Data Science

SQL is a must have skill for every data science professional. This course will start from basics of databases and structured query language (SQL) and teach you every thing you would need in any data science profession including Writing and executing efficient Queries, Joining multiple tables and appending and manipulating tables.


Key takeaways from Structured Query Language (SQL) for Data Science course:

  • Start using MySQL – the most popular Database Management System
  • Learn how to perform database tasks in SQL
  • How to insert, update, and delete records from your database
  • How to create reports and perform basic analysis using SQL’s aggregate functions
  • How to perform efficient joins to fetch your data from multiple tables




Course curriculum

  • 1
    Module #1: Introduction to Databases
  • 2
    Module #2: Installing MySQL/ MariaDB
    • 2.1 Introduction
    • 2.2 Architecture: Client and Server
    • 2.3 MySQL Distributions
    • 2.4 Local Installation on Mac
    • 2.5 Local Installation on Linux
    • 2.6 Local Installation on Windows
    • 2.7 Licensing
    • 2.8 Accessing a remote MySQL server
    • 2.9 Graphical user interfaces
    • Exercise 2
  • 3
    Module #3: Getting started
    • 3.1 Introduction
    • 3.2 What exactly is SQL?
    • 3.3 History of SQL
    • 3.4 Connecting to MySQL
    • 3.5 Types of Commands - DDL (Creation/ Deletion/ Updating of Schema
    • 3.6 Types of Commands - DML (Manipulating data in tables)
    • 3.7 Types of Commands - DCL (Managing Access control)
    • 3.8 Exploring databases
    • 3.9 Creating tables
    • 3.10 Inserting data in tables
    • 3.11 SELECT Statement - Introduction
    • 3.12 Datatypes in MySQL
    • 3.13 NULL vs NOT NULL
    • Exercise 3
  • 4
    Module #4: Modifying Databases structures
    • 4.1 Introduction
    • 4.2 Update command – Concept
    • 4.3 Update command – Example
    • 4.4 Delete command – Concept
    • 4.5 Delete command – Example
    • 4.6 Describe command – Concept
    • 4.7 Describe command – Example
    • 4.8 Alter command – Concept and Example
    • Exercise 4
    • Assignment: Share your learning and build your profile
  • 5
    Module #5: Importing and Exporting Data
    • 5.1 Introduction
    • 5.2 Importing data from CSV to MySQL
    • 5.3 Exporting data from MySQL to CSV
    • 5.4 Backing up databases
    • 5.5 Restoring databases
    • Exercise 5
  • 6
    Module #6: Data Analysis
    • 6.1 Introduction
    • 6.2 Counting Rows and Items
    • 6.3 Aggregation Functions – SUM, AVG, STDDEV
    • 6.4 Extreme Values Identification – MIN, MAX
    • 6.5 Slicing data
    • 6.6 Limiting data
    • 6.7 Sorting data
    • 6.8 Filtering Patterns
    • 6.9 Groupings, Rolling up data and Filtering in Groups
    • Exercise 6
  • 7
    Module #7: Real Life Project - Descriptive Analytics of FIFA 19 Players
    • 7.1 Introduction
    • 7.2 Data Eyeballing
    • 7.3 Data Dictionary
    • 7.4 Questions we need answers of
    • 7.5 Analyzing data and creating table structure
    • 7.6 Loading data to our MySQL table
    • 7.7 Data Analysis – Simple Queries
    • 7.8 Data Analysis – Advanced Queries
    • FIFA19 Players dataset (cleaned) for this Project
  • 8
    Module #8: Getting Data from Multiple Tables
    • 8.1. Introduction
    • 8.2. The need for joins
    • 8.3. Different type of joins
    • 8.4. The Left Join - Concept
    • 8.5. The Left Join – Practical Example
    • 8.6. The Inner Join
    • 8.7. The Cross Join
    • 8.8. The Right Join
    • 8.9. The Self Join
    • Assignment: Share your learning and build your profile
  • 9
    Module #9: Introduction to Indexing
    • 9.1. Introduction
    • 9.2. Introduction to Indexing
    • 9.3. How indexing works (basics)
    • 9.4. Relationships
    • 9.5. Types of Relationships
    • 9.6. Table Constraints – PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT
  • 10
    Module #10: MySQL built-in functions
    • 10.1 String functions - CONCAT
    • 10.2 String functions – Case Conversion
    • 10.3 String functions – Trimming Strings
    • 10.4 String functions – Extracting Substrings
    • 10.5 Date/ Time functions – Current date and time
    • 10.6 Date/ Time functions – Extracting date and time from field
    • 10.7 Date/ Time functions – Formatting date and time as Strings
    • 10.8 Numeric functions
  • 11
    Module #11: Manipulate MySQL from Python
    • 11.1 Introduction
    • 11.2 Setting up a virtual environment
    • 11.3 Installing the required packages
    • 11.4 Connecting to MySQL
    • 11.5 Connecting to database table and pulling data
    • 11.6 Querying the database- INSERT
    • 11.7 Querying the database- DELETE
    • 11.8 Querying the database- SEARCH
    • 11.9 Querying the database- INDEXING
    • 11.10 Notes and Resources
    • Assignment: Share your learning and build your profile

Real Life Project - Descriptive Analytics of FIFA 19 Players

In this project we take a real life dataset of FIFA19 players and do descriptive analytics on it. We will go step by step as is done in real life by a data scientist - and we will walk you through the thought process of how data is understood, transformed into a useful format and how we get answers and insights from our data.
Real Life Project - Descriptive Analytics of FIFA 19 Players

Instructor

  • Anand Mishra

    Anand Mishra

    Anand Mishra is Head of Engineering at Analytics Vidhya. He is an entrepreneur, an engineer and a data science professional all rolled into one. He co-founded MudraCircle, the true lending marketplace leveraging machine learning to fulfill SME loans. Before MudraCircle, Anand has worked across several companies like Lendingkart, HTMedia as Head of Data Science, Tickled Media, Infoedge India and Opera Solutions. He brings experience across several domains including E-Commerce, Fashion and Retail. Anand earned his B.Tech and M.Tech in Electrical Engineering at IIT Kanpur. Anand specializes in analytical problem solving, especially machine learning, classification, regression, and decision optimization. His thesis focused on automatically annotating large image collections on the web using a combination of weighted feature-classifier pairs.

Reviews of SQL for Data Science

  • Structured Query Language (SQL) for Data Science Course R...

    Pavan Medam

    AnandMishra Sir Explained "SQL Concepts For Data Analysis" Very Clearly With Good Examples. Solving Exercises at the end of each module will give us confiden...

    Read More

    AnandMishra Sir Explained "SQL Concepts For Data Analysis" Very Clearly With Good Examples. Solving Exercises at the end of each module will give us confidence. Course was very well designed to complete quickly with an understanding

    Read Less
  • Sql

    Akshay Deshpande

    In this course everything is covered from simple basic query to complex and even concepts are clear.

    In this course everything is covered from simple basic query to complex and even concepts are clear.

    Read Less

FAQ

  • Who should take this course?

    This course is meant for people looking to learn SQL for Data Science applications. We will start by understanding the basics of databases, primary key, Foreign key and indexing. Later in the course - we will talk about how to write efficient SQL queries and join to fetch your data.

  • When will the classes be held in this course?

    This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase.

  • How many hours per week should I dedicate to complete the course?

    If you can put between 2 to 3 hours a week, you should be able to finish the course in 4 to 6 weeks.

  • Do I need to install any software before starting the course ?

    You will get information about all installations as part of the course.

  • What is the refund policy?

    The fee for this course is non-refundable.

  • Do I get a certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the course.

  • What is the fee for this course?

    Fee for this course is INR 1,499.

  • How long I can access the course?

    You will be able to access the course material for six months since the start of the course.

Support for Structured Query Language (SQL) for Data Science Course