5 Ways SQL is Used in the Industry

12.1: Final Quiz

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Hemanand Vadivel

Ex- Data Analytics Manager, 8+ Years in Europe, Microsoft Certified, Certified Supply Chain Professional

I’m a mechanical engineer who transitioned to a full time Data & Analytics manager in the UK & Germany by teaching myself Power BI, excel & anything else that was required to solve the problem. I have worked with complex data across Supply Chain, Sales, Marketing, Revenue Management, Finance and HR functions over the last 8 years to deliver effective solutions. To me, Analytics is an extra sensory organ that anyone can develop to become a superhuman at work. I have conducted several workshops and internal training to help non-technical people to become superhuman at work and now, really excited for this opportunity to share all that knowledge with you in this course.

5 Ways SQL is Used in the Industry

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Contents

1: Course Overview

1.1: Who can Enroll in this Course? Any Prerequisites?

1.2: What Type of Computer do I Need for this Course?

1.3: Course Outline

2: Welcome to The SQL Experience

2.1: How Learning SQL can Help You in Your Career

2.2: Who is Peter Pandey?

2.3: Peter Pandey’s Early Life and SQL

3: Enhance your Learning Experience

3.1: Setting Video Resolution and Video Speed Globally

3.2: Join Discord

4: SQL Basics: Data Retrieval - Single Table

4.1: Install MySQL: Windows

4.2: Install MySQL: Linux, Mac

4.3: Import Movies Dataset in MySQL

4.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)

4.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)

4.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)

4.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)

4.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)

4.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)

4.10: HAVING Clause

4.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)

4.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)

4.13: The Data God’s Blessing

4.14: Quiz

4.15: Chapter Summary

5: SQL Basics: Data Retrieval - Multiple Tables

5.1: Why do We Need Multiple Tables?

5.2: SQL Joins (INNER, LEFT, RIGHT, FULL)

5.3: Exercise - SQL Joins (INNER, LEFT, RIGHT, FULL)

5.4: Cross Join

5.5: Analytics on Tables

5.6: Join More Than Two Tables

5.7: Exercise - Join More Than Two Tables

5.8: Quiz

5.9: Chapter Summary

6: SQL Basics: Complex Queries

6.1: Subqueries

6.2: ANY, ALL Operators

6.3: Co-Related Subquery

6.4: Exercise: Subqueries

6.5: Common Table Expression (CTE)

6.6: CTE Benefits & Other Applications

6.7: Exercise: CTE

6.8: Peter Pandey Flexes his SQL Skills

6.9: Quiz

6.10: Chapter Summary

7: SQL Basics: Database Creation & Updates

7.1: Database Normalization and Data Integrity

7.2: Entity Relationship Diagram (ERD)

7.3: Mentor Talk: Art of Googling

7.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)

7.5: Data Types: String (VARCHAR, CHAR, ENUM)

7.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)

7.7: Data Types: JSON, Spatial (JSON, GEOMETRY)

7.8: Luck Favors the LinkedIn Post

7.9: Primary key

7.10: Foreign Key

7.11: Create a Database From an Entity Relationship Diagram - ERD

7.12: Import Data From a CSV File Into a Database

7.13: Insert Statement

7.14: Update and Delete

7.15: I Need a Favour

7.16: Expect the Unexpected: The Intermission Scene

7.17: Quiz

7.18: Chapter Summary

8: AtliQ Hardware & Problem Statement

8.1: The Rise of Databases at AtliQ

8.2: Relational vs No-SQL Database

8.3: AtliQ Hardware’s Business Model

8.4: Profit & Loss Statement

8.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog

8.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import

8.7: Simplified: What is Kanban?

8.8: Quiz

8.9: Chapter Summary

9: SQL Advanced: Finance Analytics

9.1: Backlog Grooming Meeting: Gross Sales Report