4 Non-Majors, How Did They Become Data Analysts? [Monthly Datarian Seminar Replay | January 2022]

Cumulative number of applicants: approximately 2,600!
Watch the hotly debated seminar in video format.

Now we do it every month! Seminar!
Monthly Datalian Seminar 🎤

After much thought, Datalian finally got down to business.
In 2022, we will hold a relay seminar on the evening of the last Tuesday of every month .

Over 200 applicants! You can watch the popular monthly Datalian January seminar again in video.

Datalian Seminar in January is 🔍

In January, under the theme of "How did four non-majors become data analysts?", four non-major analysts from Datalian sit down and talk candidly about how they became data analysts, what preparations they made, and what difficulties they faced as non-majors.

I recommend this to these people ✅

A person who is interested in getting a job as a data analyst or changing jobs, as is commonly referred to as a 'non-major'

If you are not a data analyst but want to develop your analytical skills and are wondering where to get information

Anyone curious about how four students from the Department of Logistics, Department of Business Administration, Department of Creative Writing, and Department of Mechanical Engineering became data analysts

Data Analyst
It's hard to read job postings
I feel vaguely
Job seeker

January Seminar Timeline ⏰

• Four data analysts, each majoring in logistics, management, creative writing, and mechanical engineering, answer questions candidly.
• To become a data analyst, we talk frankly about what is really important for the job. "Is it true that you need a master's degree to become a data analyst? Who is spreading that rumor?"

#1 - Data Analyst Job Hunting + How to Read Job Postings

Part 1 covers various stories needed to start a data analyst career path, such as how to become a data analyst, what kind of mindset you have, and how to read job postings.

#2 - Q&A with 4 non-major data analysts

In Part 2, four data analysts with different majors will answer the pre-questions. They will each major in logistics, business administration, mechanical engineering, and creative writing, so they will be able to show different perspectives on one question :D

Preliminary questions answered in Part 2

• I'm curious as to why you chose a career in data analytics.
• Are data analysts players who perform all analytical positions? When I look at job postings for data analysts, business analysts, and product analysts, some places list the data analyst job as modeling, while others list it as product analysis such as Funnel/AARRR. I'm curious about where I should basically build up my capabilities.
• What was the most difficult or challenging part of working as a data analyst? I'm curious about the concerns you have in the field.
• I think that for a non-major to become a data analyst, they need to have relevant experience or a portfolio that goes beyond what a major would have. How did you prepare these experiences or portfolios?
• If you had to pick the three most important skill sets to become a data analyst, what would they be?
• I've heard that a master's degree is almost always required for data analysis jobs. Do people working in the field think the same?
• What to say in an interview as a new data analyst
• I am a non-major and have only been studying data analysis for about a year, so I am still lacking in confidence. I feel like I am not very solid in the basic concepts of machine learning and my coding skills are lacking, so I am hesitant to even apply for an internship because I am not sure if I can do well in the company. How can I muster up the courage to take the first step?

January Seminar
About the participants 📖

Check out the Q&A ! 💬

Live participation review
If you're curious 👏

What was the most impressive thing you learned during the seminar?

• I was impressed by the fact that SQL and Google Spreadsheets are mainly used in practice. Most data analysis lectures cover Python, R, and even Hadoop, but I actually had a lot of trouble deciding which tools to prioritize and study. Now, the criteria are clear.
• What impressed me was the response that when the interviewer asked , "Do you have any questions?", you should definitely ask questions.
• I was impressed by the concrete practical examples, such as when data is transferred to a spreadsheet to help with further analysis.
• I decided that I wanted to work in data analysis, and I was just making study plans, but then I was hit with the word 'excessive perfectionism' ...
• I liked that there were no set answers because you all had such diverse experiences.
• I liked that it cleared up a lot of misconceptions about data analysts (especially the idea that they absolutely need to have a master's degree) and taught me the skills and attitudes that are really necessary.
• I was impressed by the content you shared during the Q&A session! (In fact, all of it) I could feel how you read the numerous questions from your answers. I got a lot of hints from the answers from various perspectives.

A word to Datarian!

• Thank you for opening this gathering.
• It seems like a ray of light in these days when the demand for data analysis is increasing. Thank you.
• It was a more meaningful time because the stories were from the personal experiences of the four panelists.
• The job of data analysis seemed so foggy, but it was good to gain confidence and find a direction for how to study in the future.
• I always feel very scared when I first encounter something, but when I actually encounter it, I realize that I was scared of the shadow of something very small. This lecture was like that for me, and I think it will help me have a good mindset when preparing for employment in the future.