SQL-DA.AJ2
SQL for Data Analytics
Learn how to effectively use SQL for optimal data preparation, performance, and analysis.
- Practice in 17 Hands-On Labs — nothing to install
- 10 Interactive Lessons and 66 topics mapped to the official exam objectives
- 180 Practice Test Questions
Beginner Self-paced · 1 year access
17 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
10Interactive Lessons
66Topics
17LiveLab
180Practice Test Questions
33Flashcards
33Glossary of terms
01 / Skills you'll get
What you will be able to do
Try Free →
No credit card required
This SQL for Data Analytics course assists you in navigating through the data pools confidently. You’ll learn to prepare, clean, and analyze data effectively, turning raw information into valuable insights. By brushing up your skills in aggregate functions, window functions, and data preparation techniques, you’ll be well-equipped to tackle real-world data challenges.
- Analyze data effectively using SQL
- Prepare and clean datasets to ensure accurate and reliable information
- Utilize aggregate functions to summarize and draw insights from large datasets
- Apply window functions for advanced analytical queries and comparisons
- Transform raw data into structured formats for better interpretation
- Improve performance and efficiency in data retrieval by optimizing SQL queries
- Implement best practices for working with relational databases and ensuring data integrity
- Explore complex data types, including JSON and arrays, for comprehensive analysis
- Visualize data using SQL to support informed decision making
Course Highlights
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10 Structured Lessons Comprehensive coverage of core course objectives
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17 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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180 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
10 Interactive Lessons · 66 topics01 Preface 11 topics +
- About the Course
- Audience
- About the Lessons
- Conventions
- Setting up Your Environment
- Installing Git
- Loading the Sample Datasets – Windows
- Loading the Sample Datasets – Linux
- Loading the Sample Datasets – macOS
- Running SQL files
- Accessing the Code Files
02 Understanding and Describing Data 7 topics · 2 LiveLab +
- Introduction
- Data Analytics and Statistics
- Types of Statistics
- Working with Missing Data
- Statistical Significance Testing
- SQL and Analytics
- Summary
2 LiveLab in this lesson — see the labs panel →
03 The Basics of SQL for Analytics 11 topics · 2 LiveLab +
- Introduction
- The World of Data
- Relational Databases and SQL
- PostgreSQL Relational Database Management System (RDBMS)
- Creating Tables
- Basic Data Types of SQL
- Data Structures: JSON and Arrays
- Column Constraints
- Updating Tables
- SQL and Analytics
- Summary
2 LiveLab in this lesson — see the labs panel →
04 SQL for Data Preparation 5 topics · 2 LiveLab +
- Introduction
- Assembling Data
- Cleaning Data
- Transforming Data
- Summary
2 LiveLab in this lesson — see the labs panel →
05 Aggregate Functions for Data Analysis 6 topics · 1 LiveLab +
- Introduction
- Aggregate Functions
- Aggregate Functions with the GROUP BY Clause
- Aggregate Functions with the HAVING Clause
- Using Aggregates to Clean Data and Examine Data Quality
- Summary
1 LiveLab in this lesson — see the labs panel →
06 Window Functions for Data Analysis 5 topics · 1 LiveLab +
- Introduction
- Window Functions
- Statistics with Window Functions
- Window Frame
- Summary
1 LiveLab in this lesson — see the labs panel →
07 Importing and Exporting Data 5 topics · 1 LiveLab +
- Introduction
- The COPY Command
- Using Python with your Database
- Going Passwordless
- Summary
1 LiveLab in this lesson — see the labs panel →
08 Analytics Using Complex Data Types 7 topics · 2 LiveLab +
- Introduction
- Date and Time Data types for Analysis
- Performing Geospatial Analysis in PostgreSQL
- Using Array Data types in PostgreSQL
- Using JSON Data types in PostgreSQL
- Text Analytics Using PostgreSQL
- Summary
2 LiveLab in this lesson — see the labs panel →
09 Performant SQL 6 topics · 3 LiveLab +
- Introduction
- The Importance of Highly Efficient SQL
- Database Scanning Methods
- Killing Queries
- Functions and Triggers
- Summary
3 LiveLab in this lesson — see the labs panel →
10 Using SQL to Uncover the Truth: A Case Study 3 topics · 3 LiveLab +
- Introduction
- Case Study
- Summary
3 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
17 LiveLabs- Creating a Histogram in Excel
- Exploring Dealership Sales Data
- Running the SELECT Query
- Creating and Modifying Tables
- Generating a List Using the UNION Query
- Building a Sales Model
- Analyzing Sales Data Using Aggregate Functions
- Analyzing Sales Using Window Frames and Window Functions
- Reading, Visualizing, and Saving Data in Python
- Performing Text Analytics
- Searching and Analyzing Sales
- Implementing Hash Indexes
- Creating Functions with Arguments
- Creating a Trigger to Track Average Purchases
- Using SQL Techniques to Collect Preliminary Data
- Analyzing the Difference in the Sales Price Hypothesis
- Analyzing the Performance of the Email Marketing Campaign
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
How is SQL used in data analytics? +
SQL (Structured Query Language) is used in data analytics to query, manipulate, and manage data stored in relational databases. It allows analysts to retrieve specific data, perform calculations, and generate reports, making it essential for extracting insights and making data-driven decisions.
Which SQL is better for data analysis? +
The choice of SQL largely depends on the specific use case and the database system in use. PostgreSQL is often favored for its advanced features and compliance with SQL standards.
Is SQL harder than Python? +
SQL is considered easier to learn than Python due to its simpler, declarative syntax.
Do I need prior SQL knowledge to take this course? +
No, you don’t need prior SQL knowledge to take this course. It is designed for beginners and professionals alike, starting with the basics and gradually progressing to more advanced topics.
What are the benefits of learning SQL for data analytics? +
Learning SQL for data analytics allows you to work with large datasets, perform data preparation, clean and transform data, and run complex queries to extract meaningful information. SQL is a critical skill for anyone working with data, helping you to make data-driven decisions, improve business intelligence, and improve overall analytical capabilities.
Is SQL in Data Analytics a useful and high-paying skill?+
Yes, SQL is a precious skill in the job market. It is crucial for roles like data analyst, data engineer, and business intelligence analyst. Once you’ve refined your skills, you can reach for high-paying job opportunities, especially in data-centric fields.
Learn SQL for Data Analysis
Gain practical skills in SQL and transform and analyze data to achieve success in a data-driven world.
- 1 year of full access
- 17 LiveLab included
- Certificate of completion
No credit card required