Hello.
Explaining data in an easy-to-understand way,
Data communicator, "Levistal". 😁😁
If I were to split my career into two major parts,
they would be "Brand Strategy Consulting" & "Big Data Analysis."
What served as the foundation for my data analysis was
not "technical skills" like coding or statistics,
but the 'market intuition' I learned while consulting.
That is why my lectures and mentoring
📊are focused more on "whether this analysis leads to decision-making" than
🎯"which analysis technique was used."
New applicants pursuing a career in data analysis
Data analysts who need to build a business perspective due to AI
Also, marketers, planners, etc. who need to enhance their analytical capabilities due to AI
In the end, the nature of these concerns is quite similar.
✅ "Am I analyzing this correctly?"
✅ “I think I did the analysis well, but how should I present this?”
✅ “In an era where AI handles the analysis, what more do I need to know?”
✅ “What on earth is 'problem identification' and 'action proposal'?”
✅ “What exactly is a business perspective?”
✅ "I don't have a portfolio, so where should I start?"
If you are currently having these concerns,
would you like to talk about it together?
The main lecture areas are as follows.
📌 Introduction to Data Analysis for Non-Majors
📌 Data Analysis Using Generative AI
📌 How to Create a Data Analysis Portfolio
📌 Trend Analysis Based on Marketing and Search Data
📌 Text Data Analysis and Insight Report Writing
< Main SNS Channels >
Brunch : https://brunch.co.kr/@maven
Kakao Open Chat Room: <Data Analysis Portfolio Clinic>
https://open.kakao.com/o/gw30Pl1h
Although the word 'portfolio' is clearly included in the name of the open chat room,
there are many professionals from various fields as well as job seekers,
so it has long since moved beyond its original purpose lol
The main topics discussed in the open chat room are as follows.
👇
<Data Analysis Portfolio Clinic>
① Sharing current data analysis status and trends seen in the field
② Developing a sense for designing analysis topics & business perspectives
③ Finding effective analysis topics by industry, such as e-commerce and finance
④ Problem definition, interpretation, and storytelling—more important than coding
⑤ How to link analysis results to business decision-making
⑥ How to write and structure a data analysis portfolio
⑥ 1:1 portfolio proofreading and mentoring
⑦ Regular & irregular online lectures related to data analysis, etc.
