Transition to Maths and Stats

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.

Transition to Maths and Stats

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Contents

1.1: How much SQL is Needed for Data Scientist / AI Engineer?

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

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

4.1: SQL Final Quiz

5.1: Transition to Maths and Stats

Transition to Maths and Stats

This lecture is a part of Data Science Bootcamp 2.0.
Upgrade now to access enhanced modules.

Contents

1: Welcome to The SQL Experience

1.1: How much SQL is Needed for Data Scientist / AI Engineer?

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

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

4: Final Quiz

4.1: SQL Final Quiz

5: What's Next?

5.1: Transition to Maths and Stats

Transition to Maths and Stats

This lecture is a part of Data Science Bootcamp 2.0. Upgrade now to access enhanced modules.

This lecture is a part of Data Science Bootcamp 2.0.
Upgrade now to access enhanced modules.