Practical service analysis cases such as sales, order, and web log analysis
Understand key metrics for analysis such as RFM, DAU/MAU, churn rate, retention rate, and conversion funnel, and implement them in SQL.
Utilization and practical application techniques of Join, Group by, and Window functions
Ability to freely derive desired analysis results through SQL
Strengthening core SQL data analysis capabilities based on practical data similar to actual work
Chart visualization of analysis data
Learn SQL with practical data analysis! You can become a leading data expert 🏃♂️
SQL skills + practical analysis skills all in one!
The demand for data professionals with both exceptional SQL skills and a deep understanding of company operations and services is growing. Therefore, for data analysts, data scientists, analytics developers, and data engineers , possessing superior SQL skills and the ability to derive and supportanalytical results that can improve products and services is a crucial competitive advantage.
The course 'Learn SQL Data Analysis through Various Case Studies' is ✅
SQL skills should be developed through solving challenging problems in real-world situations. However, the SQL I've encountered in lectures and books so far has been quite different from the SQL used in real-world situations.
This course is filled with theoretical and practical SQL queries used in real-world analytics , something you won't find in existing lectures or books. Furthermore, it covers domain-specific topics like sales and order analysis, as well as various analytical metrics utilized in Google Analytics and growth hacking, all implemented using sophisticated SQL. This approach aims to simultaneously improve both analytical and SQL skills .
After taking this course 📜
By the end of this course, which implements many analytical indicators used in actual work using SQL, you will become a SQL expert who can freely derive the desired analysis results.
Additionally, the various analysis cases covered in this course will help you understand how to design metrics and conduct analyses to drive business and service growth.
If you have practical experience with SQL but have not taken the Data Analysis SQL Fundamentals course, please review the course curriculum and be sure to refer to the ' Course Introduction ' video in Section 0 and the ' Course Selection Guide for Those Who Have Not Taken Data Analysis SQL Fundamentals ' video to determine if the course is suitable for your skills before deciding to take the course.
We would like to inform you in advance that you may have difficulty understanding the contents of this lecture if you have not taken ' Data Analysis SQL Fundamentals '.
Features of this course ✨
Explanation of different types of key analytical indicators + A hands-on exercise to implement analytical metrics using SQL queries.
We'll cover key metrics for various types of sales analysis, cross-selling, order analysis like RFM, and more, as well as DAU/WAU/MAU, stickiness, channel analysis, entry/exit page analysis, bounce rate, retention rate, and conversion funnel analysis, all of which are well-utilized in Google Analytics and growth hacking.
Difficult SQL exercises based on real-world datasets: We'll help you improve your SQL skills to the max!
Instead of toy data, you'll implement challenging SQL on a Google Analytics data set for practice. To actively improve implementation skills, most of the course is structured around live coding . By the end of the course, you'll become a SQL expert, capable of freely deriving the desired analysis results.
Detailed and thorough explanation of complex logic
To make complex and lengthy SQL queries easier to understand, we'll break down each processing logic step by step, using detailed diagrams and charts. Through this course, you'll gain the ability to understand and apply even the most complex SQL queries step by step.
Practice implementing chart visualizations to aid intuitive understanding
You can visualize the analyzed SQL results in charts to intuitively understand the analysis results. You'll also learn how to visualize the analysis results using charts to communicate them more effectively. (The visualization code is implemented using Python's Plotly.)
Practice environment Check it out 💻
PostgreSQL is used as the practice environment DBMS, and DBeaver is used as the SQL editor.
PostgreSQL is a free, open-source DBMS that boasts stability, performance, and, most importantly, rich SQL support. It complies with the ANSI SQL standard and offers a wide range of SQL functions and analytical capabilities, making it widely used not only online but also as an analytical DBMS.
DBeaver Community Edition is free, but it offers superior features, faster performance, and greater stability than most commercial SQL editors. DBeaver supports various DBMSs, including PostgreSQL, MySQL, and Oracle.
Additionally, I use Jupyter Notebook and Plotly for chart visualization.
Although the video was created based on a Windows environment, it can also be performed without any problems in a Mac environment.
📢 Instructions for downloading lecture materials
The lecture materials (PDF), practice SQL code, and data can be downloaded from the [Lecture Materials and Practice Data and Practice Materials] class in Section 0: Lecture Introduction and Practice Environment Configuration.
Recommended for these people
Who is this course right for?
People who perform analysis tasks using SQL
Those who want to experience various practical data analysis cases
Anyone who wants to greatly improve their SQL skills
Data Scientists and Data Analysts Leveraging SQL
Data engineers who need to perform data processing/extraction/refinement based on SQL to create tables for analysis or marts
Need to know before starting?
Data Analysis SQL Fundamentals lecture understanding required
I can definitely say that this is one of the best SQL data analysis lectures.
I highly recommend this to those who have learned basic SQL grammar but want to improve their vague SQL skills through various practical cases.
Thanks to the instructor's excellent teaching skills and very informative lecture content, I feel like I have newly discovered SQL, and if you listen to the lecture and practice consistently, your skills will definitely improve. This is a high-quality lecture.
It was also doubly good because he kindly answered my questions during the lecture. I really recommend it. Thank you.
This is a great SQL lecture with lots of practical examples. I always had a hard time improving my skills because I didn't have any practical analysis experience, but this lecture was a great help.
Last year around this time, I was hired as a new data engineer and
I bought both the instructor's SQL Foundation and Data Analysis lectures at the same time with the determination to watch them both.
'I don't have talent in this field.. I understand when I watch the lectures.. but why can't I get the feel for it when I actually work?' I started the instructor's SQL data analysis lecture yesterday after a year of going back and forth and reviewing only SQL Foundation.
Moving averages and weighted averages... It's not easy for a beginner like me. ㅎㅎ
I'm so glad it's unlimited..ㅎㅎ
I haven't seen other data analysis SQL lectures, but I believe this lecture will be the easiest to explain,
so I'll slowly and steadily finish it.
If I had one wish,
I'm unfamiliar with business accounting, statistics, such as sales, so it's too difficult to accumulate data by ETL in the direction that a BI analyst wants. ㅎㅎ
I would be so happy if accounting, statistics, engineering mathematics, etc. came out at the level of SQL Foundation that anyone can see.
Thank you so much for providing such a high-quality lecture at a low price. I'll always cheer you on!
Oh, thank you so much for the long course review. If you continue to follow the exercises in this lecture repeatedly, you will be able to become a recognized SQL analysis expert anywhere.