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SQL for Data Scientists

Learn SQL for data science and acquire skills for analyzing data and building machine learning-ready datasets.

(SQL-DS.AE1) / ISBN : 978-1-64459-365-3
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About This Course

The SQL for Data Scientists course teaches you the core SQL skills needed to analyze data, create machine learning-ready datasets, and perform data manipulations. You’ll learn how to use SQL for exploratory data analysis (EDA), querying relational databases, and handling large datasets. The course covers topics like SQL JOINs, subqueries, window functions, filtering data, and aggregations, all tailored for data scientists.

Skills You’ll Get

  • Mastery of basic to advanced SQL queries, including SELECT statements, WHERE clauses, and CASE statements
  • Learn to filter, sort,, and aggregate data using SQL and draw insights from large datasets 
  • Apply SQL JOINs to combine data from multiple tables 
  • Use of window functions like RANK, ROW_NUMBER, and LAG/LEAD for advanced data analysis 
  • Apply SQL to perform EDA on various datasets like time series and categorical data 
  • Create and structure SQL datasets specifically for ML models 
  • Use of subqueries, views, and CTEs (Common Table Expressions) for analytical reporting and dataset development

Download Course Outline

1

Introduction

  • Who This Course Is For?
  • Why You Should Learn SQL if You Want to Be a Data Scientist?
  • Conventions
2

Data Sources

  • Data Sources
  • Tools for Connecting to Data Sources and Editing SQL
  • Relational Databases
  • Dimensional Data Warehouses
  • Asking Questions About the Data Source
  • Introduction to the Farmer's Market Database
  • A Note on Machine Learning Dataset Terminology
  • Exercises
3

The SELECT Statement

  • The SELECT Statement
  • The Fundamental Syntax Structure of a SELECT Query
  • Selecting Columns and Limiting the Number of Rows Returned
  • The ORDER BY Clause: Sorting Results
  • Introduction to Simple Inline Calculations
  • More Inline Calculation Examples: Rounding
  • More Inline Calculation Examples: Concatenating Strings
  • Evaluating Query Output
  • SELECT Statement Summary
  • Exercises Using the Included Database
4

The WHERE Clause

  • The WHERE Clause
  • Filtering SELECT Statement Results
  • Filtering on Multiple Conditions
  • Multi-Column Conditional Filtering
  • More Ways to Filter
  • Filtering Using Subqueries
  • Exercises Using the Included Database
5

CASE Statements

  • CASE Statement Syntax
  • Creating Binary Flags Using CASE
  • Grouping or Binning Continuous Values Using CASE
  • Categorical Encoding Using CASE
  • CASE Statement Summary
  • Exercises Using the Included Database
6

SQL JOINs

  • Database Relationships and SQL JOINs
  • A Common Pitfall when Filtering Joined Data
  • JOINs with More than Two Tables
  • Exercises Using the Included Database
7

Aggregating Results for Analysis

  • GROUP BY Syntax
  • Displaying Group Summaries
  • Performing Calculations Inside Aggregate Functions
  • MIN and MAX
  • COUNT and COUNT DISTINCT
  • Average
  • Filtering with HAVING
  • CASE Statements Inside Aggregate Functions
  • Exercises Using the Included Database
8

Window Functions and Subqueries

  • ROW NUMBER
  • RANK and DENSE RANK
  • NTILE
  • Aggregate Window Functions
  • LAG and LEAD
  • Exercises Using the Included Database
9

Date and Time Functions

  • Setting datetime Field Values
  • EXTRACT and DATE_PART
  • DATE_ADD and DATE_SUB
  • DATEDIFF
  • TIMESTAMPDIFF
  • Date Functions in Aggregate Summaries and Window Functions
  • Exercises
10

Exploratory Data Analysis with SQL

  • Demonstrating Exploratory Data Analysis with SQL
  • Exploring the Products Table
  • Exploring Possible Column Values
  • Exploring Changes Over Time
  • Exploring Multiple Tables Simultaneously
  • Exploring Inventory vs. Sales
  • Exercises
11

Building SQL Datasets for Analytical Reporting

  • Thinking Through Analytical Dataset Requirements
  • Using Custom Analytical Datasets in SQL: CTEs and Views
  • Taking SQL Reporting Further
  • Exercises
12

More Advanced Query Structures

  • UNIONs
  • Self-Join to Determine To-Date Maximum
  • Counting New vs. Returning Customers by Week
  • Summary
  • Exercises
13

Creating Machine Learning Datasets Using SQL

  • Datasets for Time Series Models
  • Datasets for Binary Classification
  • Taking Things to the Next Level
  • Exercises
14

Analytical Dataset Development Examples

  • What Factors Correlate with Fresh Produce Sales?
  • How Do Sales Vary by Customer Zip Code, Market Distance, and Demographic Data?
  • How Does Product Price Distribution Affect Market Sales?
15

Storing and Modifying Data

  • Storing SQL Datasets as Tables and Views
  • Adding a Timestamp Column
  • Inserting Rows and Updating Values in Database Tables
  • Using SQL Inside Scripts
  • In Closing
  • Exercises

1

The SELECT Statement

  • Retrieving Data from Employee Department
  • Listing Materials
  • Analysing Total amount Paid By Customers'
  • Concatenating the First and Last Names
2

The WHERE Clause

  • Getting Details of Employees Residing in the US
  • Retrieving details of Sellers Whose Name Starts with Kick
  • Checking the Functionality of TRIM() Function
  • Retrieving Data of Employees Lived in the US and Canada
  • Analyzing the Man Power in a Company
3

CASE Statements

  • Grading Employees Punctuality
  • Checking the Availability of Items Used in Production
4

SQL JOINs

  • Getting Detailed View for Analyzing Population
  • Updating Post Office Databases
  • Getting Employees History
5

Aggregating Results for Analysis

  • Using the GROUP BY Keyword
  • Getting the Sum of Number of Items
  • Adding Unit Price
  • Retrieving the Minimum and Maximum Price of the Commodity
  • Retrieving Unique Places
  • Analyzing the Items on the Basis of Price Category
6

Window Functions and Subqueries

  • Finding the Most Populated Territory
  • Analyzing the Demography of Most Populated Territory
  • Retrieving Pay Frequency
  • Correcting the Entries of Database
7

Exploratory Data Analysis with SQL

  • Analyzing Country Codes
8

Building SQL Datasets for Analytical Reporting

  • Creating a Personalized Alias of a Query
  • Creating a View
9

More Advanced Query Structures

  • Analyzing Gender Ratio Inside a Company
  • Finding Overlap Records
10

Creating Machine Learning Datasets Using SQL

  • Retrieving Full Names
11

Analytical Dataset Development Examples

  • Finding Last Names
12

Storing and Modifying Data

  • Using the DROP Command
  • Updating a Record

Any questions?
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Discover everything you want to know about our SQL for data science online course.

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This course teaches the essential SQL skills required for data science, including querying, manipulating, and analyzing data from relational databases, and creating datasets for machine learning and reporting.

No, prior knowledge of SQL is not necessary. The course is structured to start from basic SQL concepts and progressively build toward more advanced topics.

SQL allows data scientists to efficiently retrieve, manipulate, and analyze large datasets, making it a critical tool for tasks like exploratory data analysis, dataset preparation, and building machine learning models.

Yes, this course is beginner-friendly and is designed to help those new to data science develop foundational SQL skills, while also covering advanced techniques for more experienced users. 

The course covers relational databases and dimensional data warehouses, providing hands-on experience with connecting to and querying data from various sources. 

You can practice SQL for data science on our uCertify platform, which offers hands-on labs, and gamified test preps to prepare you for real-world SQL challenges. 

Yes, SQL is widely used in data science.  

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