SQL for Data Analytics
Course Description Overview
Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don’t know how to use it to gain business insights from data, this course is for you.
SQL for Data Analytics covers everything you need progress from simply knowing basic SQL to telling stories and identifying trends in data. You’ll be able to start exploring your data by identifying patterns and unlocking deeper insights. You’ll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you’ll understand how to become productive with SQL with the help of profiling and automation to gain insights faster.
By the end of the course, you’ll able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of analytics professional.
After completing this course, you will be able to:
- Use SQL to summarize 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 file and PostgreSQL
- Debug queries that won't run
- Optimize queries to improve their performance for faster results
- OS: Windows 7, Mac OS X 10.8 or a recent GNU/Linux distribution
- Browser: Google Chrome, Latest Version
- VSCode IDE, Latest Version
- Compiler: LLVM clang, Latest Version
For the optimal student experience, we recommend the following hardware configuration:
- Processor: Intel Core i3 or equivalent
- Memory: 2GB RAM
- Storage: 1 GB available space processor: 2.5 GHz or higher (or equivalent)
Lesson 1: Understanding and Describing Data
- The World of Data
- Methods of Descriptive Statistics
- Statistical Significance Testing
Lesson 2: The Basics of SQL for Analytics
- 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
Lesson 3: SQL for Data Preparation
- Assembling Data
- Transforming Data
Lesson 4: Aggregate Functions for Data Analysis
- Aggregate Functions
- Aggregate Functions with GROUP BY
- The HAVING Clause
- Using Aggregates to Clean Data and Examine Data Quality
Lesson 5: Window Functions for Data Analysis
- Window Functions
- Statistics with Window Functions
Lesson 6: Importing and Exporting data
- The COPY Command
- Using R with Our Database
- Using Python with Our Database
- Best Practices for Importing and Exporting Data
Lesson 7: Analytics Using Complex Data Types
- 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
Lesson 8: Performant SQL
- Database Scanning Methods
- Performant Joins
- Functions and Triggers
Lesson 9: Using SQL to Uncover the Truth - A Case Study
- Case Study