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
- 1.1 Introduction
- 1.2 Why do we need databases?
- 1.3 What is a database?
- 1.4 Some properties of a Good Database
- 1.5 Types of Databases
- 1.6 How data is Stored in Relational Databases
- 1.7 How data is stored in NoSQL databases
- 1.8 Companies using MySQL
- Exercise 1
- 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
- 3.1 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
- 4.1 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
- 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
- 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 and Rolling up data
- 6.10 Filtering in Groups
- 7.1 Introduction
- 7.2 Data Dictionary
- 7.3 Questions we need answers of
- 7.4 Analyzing data and creating table structure
- 7.5 Loading data to our MySQL table
- 7.6 Data Analysis – Simple Queries
- 7.7 Data Analysis – Advanced Queries
- 8.1. Introduction
- 8.2. The need for joins
- 8.3. Different type of joins
- 8.4. When to use which join?
- 8.5. Joining tables – Examples
- 8.6. Self Join
- 8.7. Self Join - Examples
- 9.1 Introduction to Indexing
- 9.2. How indexing works (basics)
- 9.3. Primary Keys
- 9.4. Auto Increment
- 10.1 String functions
- 10.2 Date/ Time functions
- 10.3 Aggregate/ Numeric functions
- 11.1 Creating an API user account
- 11.2 Libraries for connecting MySQL to Python: mysql-client and mysql-python
- 11.3 Installation
- 11.4 Connecting to MySQL
- 11.5 Connecting to database and table
- 11.6 Querying the database- INSERT
- 11.7 Querying the database- SEARCH
- 11.8 Querying the database- DELETE
- 11.9 Querying the database- INDEXING
- 11.10 Project-2
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.
Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
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 need to take the modules in a specific order?
We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.
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 1,000
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 can be availed through any of the following channels: