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SQL for Data Analytics

Learn how to effectively use SQL for optimal data preparation, performance, and analysis.

(SQL-DA.AJ2) / ISBN : 978-1-64459-485-8
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About This Course

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.

Skills You’ll Get

  • 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

Download Course Outline

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
2

Understanding and Describing Data

  • Introduction
  • Data Analytics and Statistics
  • Types of Statistics
  • Working with Missing Data
  • Statistical Significance Testing
  • SQL and Analytics
  • Summary
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
4

SQL for Data Preparation

  • Introduction
  • Assembling Data
  • Cleaning Data
  • Transforming Data
  • Summary
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
6

Window Functions for Data Analysis

  • Introduction
  • Window Functions
  • Statistics with Window Functions
  • Window Frame
  • Summary
7

Importing and Exporting Data

  • Introduction
  • The COPY Command
  • Using Python with your Database
  • Going Passwordless
  • Summary
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
9

Performant SQL

  • Introduction
  • The Importance of Highly Efficient SQL
  • Database Scanning Methods
  • Killing Queries
  • Functions and Triggers
  • Summary
10

Using SQL to Uncover the Truth: A Case Study

  • Introduction
  • Case Study
  • Summary

1

Understanding and Describing Data

  • Creating a Histogram in Excel
  • Exploring Dealership Sales Data
2

The Basics of SQL for Analytics

  • Running the SELECT Query
  • Creating and Modifying Tables
3

SQL for Data Preparation

  • Generating a List Using the UNION Query
  • Building a Sales Model
4

Aggregate Functions for Data Analysis

  • Analyzing Sales Data Using Aggregate Functions
5

Window Functions for Data Analysis

  • Analyzing Sales Using Window Frames and Window Functions
6

Importing and Exporting Data

  • Reading, Visualizing, and Saving Data in Python
7

Analytics Using Complex Data Types

  • Performing Text Analytics
  • Searching and Analyzing Sales
8

Performant SQL

  • Implementing Hash Indexes
  • Creating Functions with Arguments
  • Creating a Trigger to Track Average Purchases
9

Using SQL to Uncover the Truth: A Case Study

  • Using SQL Techniques to Collect Preliminary Data
  • Analyzing the Difference in the Sales Price Hypothesis
  • Analyzing the Performance of the Email Marketing Campaign

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Find more details about our interactive SQL for Data Analytics training course.

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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.

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.

SQL is considered easier to learn than Python due to its simpler, declarative syntax.

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.

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.

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.

 

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