SQL-DS.AE1
SQL for Data Scientists
Learn SQL for data science and acquire skills for analyzing data and building machine learning-ready datasets.
- Practice in 33 Hands-On Labs — nothing to install
- 15 Interactive Lessons and 89 topics mapped to the official exam objectives
- 131 Practice Test Questions
Beginner Self-paced · 1 year access 4.4/5 (7 Reviews)
33 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
15Interactive Lessons
89Topics
33LiveLab
131Practice Test Questions
99Flashcards
99Glossary of terms
01 / Skills you'll get
What you will be able to do
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.
- 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
Target Career Roles
- Data analysts
- data scientists
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
15 Interactive Lessons · 89 topics01 Introduction 3 topics +
- Who This Course Is For?
- Why You Should Learn SQL if You Want to Be a Data Scientist?
- Conventions
02 Data Sources 8 topics +
- 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
03 The SELECT Statement 10 topics · 4 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
04 The WHERE Clause 7 topics · 5 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
05 CASE Statements 6 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
06 SQL JOINs 4 topics · 3 LiveLab +
- Database Relationships and SQL JOINs
- A Common Pitfall when Filtering Joined Data
- JOINs with More than Two Tables
- Exercises Using the Included Database
3 LiveLab in this lesson — see the labs panel →
07 Aggregating Results for Analysis 9 topics · 6 LiveLab +
- 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
6 LiveLab in this lesson — see the labs panel →
08 Window Functions and Subqueries 6 topics · 4 LiveLab +
- ROW NUMBER
- RANK and DENSE RANK
- NTILE
- Aggregate Window Functions
- LAG and LEAD
- Exercises Using the Included Database
4 LiveLab in this lesson — see the labs panel →
09 Date and Time Functions 7 topics +
- 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 7 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
11 Building SQL Datasets for Analytical Reporting 4 topics · 2 LiveLab +
- Thinking Through Analytical Dataset Requirements
- Using Custom Analytical Datasets in SQL: CTEs and Views
- Taking SQL Reporting Further
- Exercises
2 LiveLab in this lesson — see the labs panel →
12 More Advanced Query Structures 5 topics · 2 LiveLab +
- UNIONs
- Self-Join to Determine To-Date Maximum
- Counting New vs. Returning Customers by Week
- Summary
- Exercises
2 LiveLab in this lesson — see the labs panel →
13 Creating Machine Learning Datasets Using SQL 4 topics · 1 LiveLab +
- Datasets for Time Series Models
- Datasets for Binary Classification
- Taking Things to the Next Level
- Exercises
1 LiveLab in this lesson — see the labs panel →
14 Analytical Dataset Development Examples 3 topics · 1 LiveLab +
- 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?
1 LiveLab in this lesson — see the labs panel →
15 Storing and Modifying Data 6 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
33 LiveLabs- Retrieving Data from Employee Department
- Listing Materials
- Analysing Total amount Paid By Customers'
- Concatenating the First and Last Names
- 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
- Grading Employees Punctuality
- Checking the Availability of Items Used in Production
- Getting Detailed View for Analyzing Population
- Updating Post Office Databases
- Getting Employees History
- 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
- Finding the Most Populated Territory
- Analyzing the Demography of Most Populated Territory
- Retrieving Pay Frequency
- Correcting the Entries of Database
- Analyzing Country Codes
- Creating a Personalized Alias of a Query
- Creating a View
- Analyzing Gender Ratio Inside a Company
- Finding Overlap Records
- Retrieving Full Names
- Finding Last Names
- Using the DROP Command
- Updating a Record
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What is the SQL for Data Scientists course? +
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.
Do I need prior knowledge of SQL to take this course? +
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.
How can SQL be beneficial for data scientists? +
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.
Is this course suitable for beginners in data science? +
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.
What types of databases will I learn about in this course? +
The course covers relational databases and dimensional data warehouses, providing hands-on experience with connecting to and querying data from various sources.
Where can I practice SQL for data science?+
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.
Is SQL used in data science?+
Yes, SQL is widely used in data science.
Keep going
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Practice writing complex SQL queries, exploring and analyzing data efficiently, and building datasets read for machine learning.
- 1 year of full access
- 33 LiveLab included
- Certificate of completion