Coding Exercises: SQL Data Science

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Harshit Tyagi

1:10:08

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[1] Introduction

[1] Up for a code challenge.mp4

01:29

[2] 1. SQL Fundamentals

[1] Column selection.mp4

02:47

[2] Filtering rows from data.mp4

03:13

[3] Removing duplicate data.mp4

03:02

[4] Sorting and grouping query results.mp4

04:57

[3] 2. SQL Joins

[1] Inner joins.mp4

05:04

[2] Outer and cross joins.mp4

04:53

[3] Set theory for SQL joins.mp4

03:57

[4] 3. Writing Complex Queries

[1] Using OVER and PARTITION BY statements.mp4

04:04

[2] Filtering and subsetting data.mp4

06:29

[3] Using CASE and WHEN statements.mp4

06:05

[4] Calculating moving window average.mp4

04:07

[5] 4. Data Cleaning with SQL

[1] Partitioning dates with the DATE PART function.mp4

06:48

[2] Text formatting and pattern matching.mp4

05:21

[3] Using COALESCE to replace null values.mp4

03:52

[4] Sorting with regular expressions.mp4

04:00

Description

Want to test—and develop—your SQL data-engineering skills? Do you fancy yourself pretty skilled at SQL and want to see just how sharp you are? Then check out this installment of the Code Challenges series, as instructor Harshit Tyagi presents a number of challenges requiring you to write SQL queries to solve problems and answer questions. The challenges start at a relatively low level of difficulty—column selection, filtering rows from data, sorting and grouping query results—and get progressively tougher, testing you on topics like handling missing and invalid data, calculating moving window average, and data transformations. The challenges are all a few minutes long, and use a single database, so you can jump around from challenge to challenge or watch the entire course. So, if you want to see if you’re equal to the tasks, try out these SQL challenges.

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Microsoft SQL

Harshit Tyagi

Instructor's Courses

- An engineer with amalgamated experience in web technologies and data science(aka full-stack data science). - Mentored over 10K AI/Web/Data Science aspirants. - Designing data science, data engineering and ML engineering learning tracks. - Previously, developed data processing algorithms with research scientists at Yale, MIT, and UCLA.

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