SQL Mastery For Data Science
Learn practical applications of SQL queries for data analysis. Join tables, calculate rolling averages, and use window functions to manipulate and analyze data. Gain career-building SQL skills with this hands-on course.
What you’ll learn
- SQL for Data Science
- Learn practical applications of SQL queries for data analysis
- How to join tables and calculate rolling averages
- How to use window functions, aggregate and filter data
- Learn how to retrieve data
- Much more
Gain the career-building SQL skills you need with this course. Through hands-on learning you’ll load, extract, and manipulate data from relational databases. Study at your own pace and grow your SQL skills.
In this course, we’ll go over the most common data science and analytics questions that you’ll receive, such as how to find the top products per category, how to find active employee counts by month, how to calculate rolling average of sales and much more.
We’ll start by showing you how to retrieve data from a database using SQL Server and AdventureWorks, then show you how to aggregate, join, and filter your results to create context for your analysis.
We’ll also get into answering more complex questions with ranking, moving averages, and window functions.
Learn how to retrieve data, join tables, calculate rolling averages and rankings, work with dates and times, use window functions, aggregate and filter data, and much more.
SQL is one of the most requested skills in Data Science. This course is great for anyone looking to build their skills and take it to the next level.
Learn to use Structured Query Language (SQL) to extract and analyze data stored in databases. You’ll first learn to extract data, join tables together, and perform aggregations. Then you’ll learn to do more complex analysis and manipulations using subqueries, and window functions.
By the end of the course, you’ll be able to write efficient SQL queries to successfully handle a variety of data analysis tasks.
Who this course is for:
- Programmers, Developers, DevOps, Data miners
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