Course HighlightsCOURSE
SQL for Data Science

SQL for Data Science

Gain practical, hands-on experience working with SQL in a data science environment. Use Jupyter Notebooks to perform SQL access to relational databases.

Develop your skills in this critical language for data science and build your competence in this exciting field.

SQL for Data Science Highlights

  Course duration


  • 4 weeks, online
    4-6 hours/week
  Course Fee


US$ 99 - US$ 199

Course duration


  • 4 weeks, online
    4-6 hours/week
Course Fee


US$ 99 - US$ 199

SQL is widely used by analysts working with databases and relational database systems. Therefore, knowledge of SQL is critical for working in data science.

During this course, you will be introduced to relational database concepts, and you will learn how to apply foundational knowledge of the SQL language in a practical way in a data science environment.

The emphasis of this course is on hands-on, practical learning. You will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter Notebooks using SQL and Python.

Learning about database concepts is a critical step for individuals keen to excel in the world of data science. This course will provide you with this key skill so you can confidently progress your data science career.

This course comprises six purposely designed modules that take you on a carefully defined learning path. If you are thinking about taking the course separately, it is worth noting that it is part of the IBM Data Science Professional Certificate Program and you may want to consider enrolling for the whole program rather than just enrolling for one course at a time.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 4-6 hours per week, you will complete the course in 4-5 weeks. However, as long as the course is completed before the end date, you can work at your own pace.

The materials for each module will become available when you start the particular module. Methods of learning and assessment will include videos, reading material, and online exams questions.

Once you have successfully completed the course, you will earn your IBM Certificate.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

  • Apply your knowledge of data science and machine learning to a real life scenario.
  • Analyze and visualize data using Python.
  • Perform a feature engineering exercise using Python.
  • Build and validate a predictive machine learning model using Python.
  • Create and share actionable insights to real life data problems.

    This course is intended for existing or aspiring data scientists who want practical knowledge of SQL to:

    • Query databases.
    • Execute SQL from Jupyter Notebooks using Python.

      This course is also of great value to data analysts, application developers, and data engineers.

    Some experience with Python will be an asset.

Course Outline

Course Overview
Who Should Take This Course
Learning Objectives
Grading Scheme
Module Introduction
Learning Objectives
Video: Welcome to SQL for Data Science (2:22)
Video: Introduction to Databases (4:28)
Video: How to Create a Database Instance on Cloud (5:51)
Relational Database Concepts (5:34)
Hands-on LAB: Provision a Cloud Hosted Database Instance
Graded Quiz - Databases
Module Introduction
Learning Objectives
Video: Types of SQL Statements (DDL vs. DML) (2:25)
Video: CREATE TABLE Statement (6:19)
Video: ALTER, DROP, and Truncate Tables (4:17)
Reading: Examples to CREATE and DROP tables (5:00)
Hands-on Lab: Create and Load Tables using SQL Scripts (30m)
Video: SELECT Statement (3:40)
Reading: SELECT statement examples (2:00)
Hands-on Lab: Basics of SELECT Statements
Video: INSERT Statement (2:41)
Video: UPDATE and DELETE Statements (3:16)
Hands-on Lab: INSERT, UPDATE, and DELETE
Hands-on LAB: Composing and Running Basic SQL Queries
Graded Quiz - Basic SQL
Module Introduction
Learning Objectives
Video: Using String Patterns, Ranges (4:27)
Video: Sorting Result Sets (2:49)
Video: Grouping Result Sets (3:37)
Hands-on LAB: String Patterns, Sorting & Grouping (1 Hr)
Graded Quiz: String Patterns, Ranges, Sorting and Grouping
Module Introduction
Learning Objectives
Video: Built-in Database Functions (6:16)
Video: Date and Time Built-in Functions (2:45)
Hands-on Lab: Built-in Functions (20:00)
Video: Sub-Queries and Nested Selects (4:53)
(Optional) Hands-on Lab : Sub-queries and Nested SELECTS
Video: Working with Multiple Tables (6:49)
Hands-on Lab: Sub-queries, Multiple Tables (30:00)
Graded Quiz: Functions, Sub-Queries, Multiple Tables
Module Introduction
Learning Objectives
Video: How to Access Databases Using Python (6:02)
Video: Writing Code Using DB-API (5:29)
Video: Connecting to a Database Using ibm_db API (2:10)
LAB 0: Create Database Credentials (5:00)
Hands-on LAB 1: Connecting to a Database Instance (20:00)
Video: Creating Tables, Loading Data and Querying Data (3:54)
Hands-on LAB 2: Creating Tables, Inserting and Querying Data (30:00)
Reading: Introducing SQL Magic (10m)
Hands-on Tutorial: Accessing Databases with SQL Magic (20:00)
Video: Analyzing Data with Python (9:34)
Hands-on LAB 3: Analyzing a Real World Data Set (45:00)
Graded Quiz: Database access from Python
Module Introduction
Learning Objectives
Video: Working with Real World Data Sets (8:48)
Video: Getting Table and Column Details (4:09)
Hands-on Lab 1: Practice Querying Real World Data Sets (45:00)
LAB: Execute the SQL Queries for Final Assignment
Reading: About this Optional Section (2:00)
Learning Objectives
Video: Join Overview (3:13)
Video: Inner Join (3:52)
Video: Left Outer Join (5:12)
Video: Right Outer Join (3:40)
Video: Full Outer Join (2:56)
Hands-on LAB: JOINs (45:00)
Practice Quiz: JOIN operations
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SQL for Data Science


Data science is composed of a variety of elements including statistical analysis, programming tools, algorithms, and machine learning techniques. It includes the application of several methodologies, including statistics, scientific methods, artificial intelligence (AI) and data analysis.

1. Facebook: Facebook is now the world's most popular social networking platform. It has millions of users across the globe and is always undertaking large-scale quantitative research utilizing data science to learn more about social relationships. Face recognition and text analysis are two fundamental applications of deep learning, a cutting-edge data science technology that Facebook employs. Facebook also uses powerful neural networks to classify faces in photos. Plus, it uses their Deep Text engine that was created in-house to classify written words.

2. Amazon: Amazon has always sought to be a consumer platform that constantly improves client satisfaction. By employing data science methodologies, Amazon utilizes predictive shipping technologies to analyze massive quantities of data to predict what products people will buy. It tracks buying habits and stores products in nearby warehouses where possible. Amazon also monitors user behavior, order history, rival prices, availability of the product, and so on. Fraud detection is another issue that all e-commerce platforms face. As a result, Amazon has developed its own techniques and algorithms. Plus Amazon uses workflow data to increase warehouse product packing and packaging line productivity.

Once you’ve completed SQL for Data Science, you will be able to:

  • Create a database in a cloud.
  • Apply foundational knowledge of SQL language.
  • Analyze data using Python.
  • Sort and group data in result sets and by data type.
  • Use string patterns and ranges to query data.

Data is a valuable asset, and data-driven business processes are helping to boost efficiency and innovation. As a result, the need for data scientists with extraordinary talents and strong skills is growing, and firms are willing to offer excellent remuneration packages to attract the best candidates. The following are some well-known data science service providers:

  • Oracle
  • Amazon
  • JP Morgan Chase
  • Teradata
  • Accenture