Linkedin - Intermediate SQL for Data Scientists (2025)

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Free Download Linkedin - Intermediate SQL for Data Scientists (2025)
Released: 03/2025
Duration: 3h 50m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 465 MB
Level: Intermediate | Genre: eLearning | Language: English
Organizations are increasingly adopting digital transformation strategies that lead to greater data generation and, as a result, a greater need for data analysis. This course is designed for analysts and data scientists who work with SQL databases. Instructor Dan Sullivan outlines how to perform common data science tasks, including finding, exploring, and extracting data within relational databases. Explore the basics of using aggregates, statistical functions, window operations, regular expressions, and subqueries. Along the way, learn how to use JSON, a semistructured data type, and perform join operations to enable more complex queries.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you'll likely encounter in the workplace. Check out "Using GitHub Codespaces" with this course to learn how to get started.
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Intermediate SQL for Data Scientists
.MP4, AVC, 1280x800, 30 fps | English, AAC, 2 Ch | 2h 38m | 429 MB
Instructor: Dan Sullivan​

There is an increasing need for data scientists and analysts to understand relational data stores. Organizations have long used SQL databases to store transactional data as well as business intelligence related data. This course was designed for data scientists who need to work with SQL databases. Specifically, it was designed to help these professionals learn how to perform common data science tasks, including exploration and extraction of data within relational databases.

Instructor Dan Sullivan kicks off the course with a brief overview of SQL data manipulation and data definition commands. He then focuses on how to use SQL queries to prepare data for analysis; leverage statistical functions to better understand that data; and work with aggregates, window operations, and more.

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