The Two Most Important Skills for the Data Analyst

10.1: Final Quiz Mandatory

11.1: Transition to Python & Interviews

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.

The Two Most Important Skills for the Data Analyst

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Contents

1: Welcome to The SQL Experience

1.1: Course Outline

1.2: How Learning SQL can Help You in Your Career

1.3: Who is Peter Pandey?

1.4: Peter Pandey’s Early Life and SQL

2: SQL Basics: Data Retrieval - Single Table

2.1: Install MySQL: Windows

2.2: Install MySQL: Linux, Mac

2.3: Import Movies Dataset in MySQL

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

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

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

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

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

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

2.10: HAVING Clause

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

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

2.13: The Data God’s Blessing

2.14: Quiz

2.15: Chapter Summary

3: SQL Basics: Data Retrieval - Multiple Tables

3.1: Why do We Need Multiple Tables?

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

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

3.4: Cross Join

3.5: Analytics on Tables

3.6: Join More Than Two Tables

3.7: Exercise - Join More Than Two Tables

3.8: Quiz

3.9: Chapter Summary

4: SQL Basics: Complex Queries

4.1: Subqueries

4.2: ANY, ALL Operators

4.3: Co-Related Subquery

4.4: Exercise: Subqueries

4.5: Common Table Expression (CTE)

4.6: CTE Benefits & Other Applications

4.7: Exercise: CTE

4.8: Peter Pandey Flexes his SQL Skills

4.9: Quiz

4.10: Chapter Summary

5: SQL Basics: Database Creation & Updates

5.1: Database Normalization and Data Integrity

5.2: Entity Relationship Diagram (ERD)

5.3: Mentor Talk: Art of Googling

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

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

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

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

5.8: Luck Favors the LinkedIn Post

5.9: Primary key

5.10: Foreign Key

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

5.12: Import Data From a CSV File Into a Database

5.13: Insert Statement

5.14: Update and Delete

5.15: I Need a Favour

5.16: Expect the Unexpected: The Intermission Scene

5.17: Quiz

5.18: Chapter Summary

6: AtliQ Hardware & Problem Statement

6.1: The Rise of Databases at AtliQ

6.2: Relational vs No-SQL Database

6.3: AtliQ Hardware’s Business Model

6.4: Profit & Loss Statement

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

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

6.7: Simplified: What is Kanban?

6.8: Quiz

6.9: Chapter Summary

7: SQL Advanced: Finance Analytics

7.1: Backlog Grooming Meeting: Gross Sales Report

7.2: User-Defined SQL Functions

7.3: Exercise: User-Defined SQL Functions

7.4: Gross Sales Report: Monthly Product Transactions

7.5: Gross Sales Report: Total Sales Amount

7.6: Exercise: Yearly Sales Report

7.7: Stored Procedures: Monthly Gross Sales Report