SQL for Data Analytics
Overview
Course Objective
• Use SQL to summarise and identify patterns in data
• Apply special SQL clauses and functions to generate descriptive statistics
• Use SQL queries and subqueries to prepare data for analysis
• Perform advanced statistical calculations using the window function
• Analyze special data types in SQL, including geospatial data and time data
• Import and export data using a text _le and PostgreSQL
• Debug queries that won’t run
• Optimize queries to improve their performance for faster results
Who Should Attend
Data Analysts looking to enhance their SQL skills for more effective data analysis
Business Intelligence professionals who want to improve their ability to identify trends and insights in data
Data Scientists who wish to apply SQL more efficiently to prepare and analyze various types of data
Database administrators or developers who want to optimize SQL queries for data analytics purposes
Anyone with a basic understanding of SQL who wants to advance their knowledge and apply SQL in real-world business scenarios
Prerequisites
Participants should have a solid understanding of SQL and relational databases. They should be comfortable with basic SQL operations such as SELECT queries, creating tables, and performing simple data manipulations. Familiarity with SQL syntax and functions will be beneficial to fully grasp the more advanced concepts covered in the course.
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Understanding and Describing Data
• Understanding and Describing Data Introduction
• The World of Data
• Methods of Descriptive Statistics
• Statistical Significance Testing
• Summary
Module 2 - The Basics of SQL for Analytics
• The Basics of SQL for Analytics
• Introduction
• Relational Databases and SQL
• Basic Data Types of SQL
• Reading Tables: The SELECT Query
• Creating Tables
• Updating Tables
• Deleting Data and Tables
• SQL and Analytics
• Summary
Module 3 - SQL for Data Preparation
• SQL for Data Preparation
• Introduction
• Assembling Data
• Transforming Data
• Summary
Module 4 - Aggregate Functions for Data Analysis
• Aggregate Functions for Data Analysis
• Introduction
• Aggregate Functions
• Aggregate Functions with GROUP BY
• The HAVING Clause
• Using Aggregates to Clean Data and Examine Data Quality
• Summary
Module 5 - Window Functions for Data Analysis
• Window Functions for Data Analysis
• Introduction
• Window Functions
• Statistics with Window Functions
• Summary
Module 6 - Importing and Exporting Data
• Importing and Exporting Data
• Introduction
• The COPY Command
• Using R with Our Database
• Using Python with Our Database
• Best Practices for Importing and Exporting Data
• Summary
Module 7 - Server Programming Basics Analytics using complex Data Types
• Introduction
• Date and Time Data Types for Analysis
• Performing Geospatial Analysis in Postgres
• Using Array Data Types in Postgres
• Using JSON Data Types in Postgres
• Text Analytics Using Postgres
• Summary
Module 8 - Performant SQL
• Performant SQL
• Introduction
• Database Scanning Methods
• Performant Joins
• Functions and Triggers
• Summary
Module 9 - Using SQL to Uncover the Truth – a Case Study
• Using SQL to Uncover the Truth – a Case Study
• Introduction
• Case Study
• Summary
FAQs
General Questions:
Q: What is the SQL for Data Analytics course about?
A: This 3-day course is designed to help participants move beyond basic SQL and learn how to analyze data more effectively for business insights. It focuses on using SQL to identify patterns, perform advanced statistical calculations, and work with various data types such as time-series, geospatial, and text data. Participants will also learn how to prepare data for analysis, optimize queries, and increase productivity through profiling and automation.
Q: Who should attend this course?
A: This course is ideal for data analysts, business intelligence professionals, data scientists, and anyone who already has a basic understanding of SQL and wants to enhance their skills for more advanced data analysis. It’s especially beneficial for individuals looking to work with complex data types, optimize queries for performance, and unlock deeper insights from data.
Q: What are the prerequisites for this course?
A: Participants should have a solid understanding of SQL and relational databases. They should be comfortable with basic SQL operations such as SELECT queries, creating tables, and performing simple data manipulations. Familiarity with SQL syntax and functions is essential to fully benefit from the more advanced concepts in this course.
Q: How long is the course?
A: The course duration is 3 days.
Q: What key topics are covered in this course?
Key topics include:
Summarizing and identifying patterns in data using SQL
Applying advanced SQL clauses and functions for descriptive statistics
Using SQL queries and subqueries for data preparation
Performing statistical calculations with window functions
Analyzing special data types such as time-series, geospatial, and text data
Importing and exporting data using PostgreSQL
Debugging and optimizing SQL queries
Best practices for data profiling and automation
Q: Will I receive a certification after completing the course?
A: While this course provides the skills necessary for effective data analysis with SQL, it does not include a certification exam. A certificate of completion may be provided by the training provider.
Program Content & Skills:
Q: What skills will I gain from the SQL for Data Analytics course?
A: You will gain hands-on skills in using SQL to identify patterns, summarize data, and perform advanced statistical calculations. You’ll learn how to work with complex data types such as time-series, geospatial, and text data. The course will also teach you how to optimize queries, use window functions for analysis, debug queries, and apply automation for increased productivity.
Q: Will I learn how to optimize SQL queries for performance?
A: Yes, the course covers SQL query optimization techniques to enhance performance, including how to improve execution plans, use efficient join methods, and reduce query times. You will also learn about indexing, profiling, and best practices for optimizing queries for faster results.
Q: Can I learn how to handle complex data types in SQL?
A: Yes, the course includes in-depth coverage of working with various complex data types such as date and time, geospatial data, arrays, JSON, and text. You will learn how to analyze and manipulate these data types effectively in SQL.
Q: Is data preparation for analysis included in the course?
A: Yes, the course covers SQL for data preparation, including techniques for assembling and transforming data. You will learn how to clean and organize data for further analysis and use SQL to generate the insights needed for business decision-making.
Q: Will I be able to apply SQL for real-world business scenarios after the course?
A: Yes, the course is designed to teach you how to use SQL in everyday business scenarios. You will gain the skills to analyze and understand business data critically, identify trends, and provide insights that can drive business decisions.
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