SQL for Data Analysis

Returning to SQL basics proved to be an invaluable experience, even for someone with prior knowledge. The course offered a detailed and thorough exploration of SQL concepts, serving as an excellent refresher while also providing new insights. Its comprehensive approach covered fundamental to advanced topics, reinforcing existing skills and filling knowledge gaps.

The in-depth nature of the course not only solidified understanding but also introduced nuances that may have been overlooked previously. This renewed perspective on SQL is particularly beneficial for those pursuing a career in data analysis, as it enhances query writing proficiency and database manipulation skills.

Overall, the course's quality and depth make it a worthwhile investment for both beginners and experienced practitioners. It provides a strong foundation and advanced techniques that will undoubtedly prove useful in real-world analytical scenarios, boosting confidence and competence in SQL usage.

This is a very well put together introduction to SQL, using one consistent toy data example throughout (the database of orders and accounts for a fictional paper company). New features of SQL are introduced at a sensible pace, and there are frequent quizzes which ask you to create your own queries to interact with the database.

Unlike some other online courses, I didn't feel at any point that I was being left to go read all the online documentation for something on my own, as it wasn't explained properly. Neither did I feel that I was only being asked to construct trivial queries that wouldn't actually develop my understanding.

Here's an example problem set, from the GROUP BY section of the third chapter:

1. For each account, determine the average amount of each type of paper they purchased across their orders. Your result should have four columns - one for the account name and one for the average quantity purchased for each of the paper types for each account.

2. For each account, determine the average amount spent per order on each paper type. Your result should have four columns - one for the account name and one for the average amount spent on each paper type.

3. Determine the number of times a particular channel was used in the web_events table for each sales rep. Your final table should have three columns - the name of the sales rep, the channel, and the number of occurrences. Order your table with the highest number of occurrences first.

4. Determine the number of times a particular channel was used in the web_events table for each region. Your final table should have three columns - the region name, the channel, and the number of occurrences. Order your table with the highest number of occurrences first.