SQL for Data Analytics, Third Edition
(SQL-DA.AJ2)/ISBN:978-1-64459-485-8
Unlock the power of SQL in the world of Data Analytics with our SQL for Data Analytics, third edition course. Whether you're a beginner or looking to enhance your data analysis skills, this course is designed to provide you with practical knowledge and hands-on experience in leveraging SQL for effective data analysis. By the end of this course, you'll be equipped with the skills to leverage SQL for efficient data analysis.
Lessons
10+ Lessons | 110+ Exercises | 25+ Quizzes | 34+ Flashcards | 34+ Glossary of terms
TestPrep
52+ Pre Assessment Questions | 42+ Post Assessment Questions |
Hand on lab
17+ LiveLab | 00+ Minutes
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Here's what you will learn
Download Course OutlineLessons 1: Preface
- 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
Lessons 2: Understanding and Describing Data
- Introduction
- Data Analytics and Statistics
- Types of Statistics
- Working with Missing Data
- Statistical Significance Testing
- SQL and Analytics
- Summary
Lessons 3: The Basics of SQL for Analytics
- 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
Lessons 4: SQL for Data Preparation
- Introduction
- Assembling Data
- Cleaning Data
- Transforming Data
- Summary
Lessons 5: Aggregate Functions for Data Analysis
- 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
Lessons 6: Window Functions for Data Analysis
- Introduction
- Window Functions
- Statistics with Window Functions
- Window Frame
- Summary
Lessons 7: Importing and Exporting Data
- Introduction
- The COPY Command
- Using Python with your Database
- Going Passwordless
- Summary
Lessons 8: Analytics Using Complex Data Types
- 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
Lessons 9: Performant SQL
- Introduction
- The Importance of Highly Efficient SQL
- Database Scanning Methods
- Killing Queries
- Functions and Triggers
- Summary
Lessons 10: Using SQL to Uncover the Truth: A Case Study
- Introduction
- Case Study
- Summary
Hands-on LAB Activities
Understanding and Describing Data
- Exploring Dealership Sales Data
- Creating a Histogram in Excel
The Basics of SQL for Analytics
- Running the SELECT Query
- Creating and Modifying Tables
SQL for Data Preparation
- Generating a List Using the UNION Query
- Building a Sales Model
Aggregate Functions for Data Analysis
- Using Aggregate Function for Analyzing Sales Data
Window Functions for Data Analysis
- Using Window Frames and Window Functions
Importing and Exporting Data
- Reading, Visualizing, and Saving Data in Python
Analytics Using Complex Data Types
- Searching and Analyzing Sales
- Performing Text Analytics
Performant SQL
- Implementing Hash Indexes
- Creating Functions with Arguments
- Creating a Trigger to Track Average Purchases
Using SQL to Uncover the Truth: A Case Study
- Analyzing the Performance of the Email Marketing Campaign
- Analyzing the Difference in the Sales Price Hypothesis
- Using SQL Techniques to Collect Preliminary Data