Analytics on Tables

8.1: Final Quiz

9.1: Step Into the Practice Room (SQL)

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.

Analytics on Tables

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Contents

1: Welcome to The SQL Experience

1.1: Who is Peter Pandey?

1.2: Peter Pandey’s Early Life and SQL

2: Enhance your Learning Experience

2.1: Setting Video Resolution and Video Speed Globally

2.2: Join Discord

3: SQL Basics: Data Retrieval - Single Table

3.1: Install MySQL: Windows

3.2: Install MySQL: Linux, Mac

3.3: Import Movies Dataset in MySQL

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

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

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

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

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

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

3.10: HAVING Clause

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

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

3.13: The Data God’s Blessing

3.14: Quiz

3.15: Chapter Summary

4: SQL Basics: Data Retrieval - Multiple Tables

4.1: Why do We Need Multiple Tables?

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

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

4.4: Cross Join

4.5: Analytics on Tables

4.6: Join More Than Two Tables

4.7: Exercise - Join More Than Two Tables

4.8: Quiz

4.9: Chapter Summary

5: SQL Basics: Complex Queries

5.1: Subqueries

5.2: ANY, ALL Operators

5.3: Co-Related Subquery

5.4: Exercise: Subqueries

5.5: Common Table Expression (CTE)

5.6: CTE Benefits & Other Applications

5.7: Exercise: CTE

5.8: Peter Pandey Flexes his SQL Skills

5.9: Quiz

5.10: Chapter Summary

6: SQL Basics: Database Creation & Updates

6.1: Database Normalization and Data Integrity

6.2: Entity Relationship Diagram (ERD)

6.3: Mentor Talk: Art of Googling

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

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

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

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

6.8: Luck Favors the LinkedIn Post

6.9: Primary key

6.10: Foreign Key

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

6.12: Import Data From a CSV File Into a Database

6.13: Insert Statement

6.14: Update and Delete

6.15: I Need a Favour

6.16: Expect the Unexpected: The Intermission Scene

6.17: Quiz

6.18: Chapter Summary

7: SQL Advanced: Window Functions

7.1: Window Functions: OVER Clause

7.2: Window Functions: ROW_NUMBER, RANK, DENSE_RANK

7.3: Quiz

8: Final Quiz

8.1: Final Quiz

9: What's Next?

9.1: Step Into the Practice Room (SQL)

Analytics on Tables

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