Skill
SQL for Data Practitioners: Grouping
April 2022 with Jonathan Barrios
- Entry
- 5 videos
- 52 mins
The GROUP BY statement is commonly used with aggregate functions you learned in the last videos. In this set of videos, you dive into grouping columns together by some category. As you might expect, we'll go through the syntax and examples to make sure you understand the concept.
Then, we'll jump into HAVING statements that handle aggregate functions since the WHERE keyword doesn't work with functions at all. Again, we'll go through the syntax and examples to make sure you understand both GROUP BY and HAVING using other keywords you learned so far.
Finally, you'll get an opportunity to use what you learned by performing a code challenge using GROUP BY and HAVING clauses.
Then, we'll jump into HAVING statements that handle aggregate functions since the WHERE keyword doesn't work with functions at all. Again, we'll go through the syntax and examples to make sure you understand both GROUP BY and HAVING using other keywords you learned so far.
Finally, you'll get an opportunity to use what you learned by performing a code challenge using GROUP BY and HAVING clauses.
Recommended Experience
Jonathan Barrios has been a CBT Nuggets trainer since 2021. His expertise areas include full-stack software development, data science, and machine learning. He has experience using the following technologies: HTML, CSS, JavaScript, PHP, Python, SQL, NoSQL, and frameworks/libraries such as Vue, React, Django, NumPy, Pandas, Matplotlilb, Scrappy, BeautifulSoup, SciPy, Seaborn, Plotly, Scikit-learn, Tensorflow, and PySpark.
- None
- Data Analysts
- Data Practitioners
- SQL Developers
Jonathan Barrios has been a CBT Nuggets trainer since 2021. His expertise areas include full-stack software development, data science, and machine learning. He has experience using the following technologies: HTML, CSS, JavaScript, PHP, Python, SQL, NoSQL, and frameworks/libraries such as Vue, React, Django, NumPy, Pandas, Matplotlilb, Scrappy, BeautifulSoup, SciPy, Seaborn, Plotly, Scikit-learn, Tensorflow, and PySpark.
Trainer