How did 4 non-majors become data analysts? [Monthly Datarian Seminar Replay | January 2022]

Approximately 2,600 cumulative applicants!
Check out the much-talked-about seminar through this video.

Now happening every month! Seminar!
Monthly Datarian Seminar 🎤

After much deliberation, Datarian has finally made a move.
In 2022, we will be holding relay seminars every last Tuesday evening of the month.

Over 200 applicants! You can rewatch the popular Monthly Datarian January Seminar on video. 

January's Datarian Seminar is 🔍

In January, under the theme "How did 4 non-majors become data analysts?", four analysts from Datarian who come from non-relevant academic backgrounds sit down to have a candid conversation about how they became data analysts, what preparations they made, and whether they faced any difficulties as non-majors.

Recommended for these people ✅

Those who are commonly referred to as 'non-majors' but are interested in getting a job or switching careers to become a data analyst.

Those who are not data analysts but want to develop their analytical skills and are wondering where to find good information.

Those who are curious about how four individuals from Logistics, Business Administration, Creative Writing, and Mechanical Engineering backgrounds became data analysts.

Job seekers who find it difficult
to read data analyst job postings
and feel they are
vague

January Seminar Timeline ⏰

• Four data analysts, who majored in logistics, business administration, creative writing, and mechanical engineering respectively, provide honest answers to pre-submitted questions.
• We talk candidly about what is truly important for becoming a data analyst and performing the job. "You need a master's degree to become a data analyst? Who's the one spreading that rumor?"

Part 1 - Data Analyst Job Search Story + How to Read Job Postings

In Part 1, we covered various stories essential for starting a career path as a data analyst, including how they became a data analyst, what their mindset was, and how to read job postings.

#Part 2 - Q&A with 4 Data Analysts from Non-CS Backgrounds

In Part 2, four data analysts from various academic backgrounds answer pre-submitted questions. Since the respondents majored in Logistics, Business Administration, Mechanical Engineering, and Creative Writing respectively, you'll be able to see a wide range of perspectives on a single question :D

Pre-questions answered in Part 2

• I'm curious to know why you decided to pursue a career in data analysis.
• Is a data analyst a player who performs all types of analytical roles? Looking at job postings for Data Analysts, Business Analysts, and Product Analysts, some describe Data Analyst tasks as modeling, while others describe them as product analysis like Funnel/AARRR. I'm curious about the fundamental range of skills I should build up.
• What was the most difficult or challenging part of working as a data analyst? I'm curious about the concerns people face in the field.
• I believe that for a non-major to enter the data analysis field, they need relevant experience or a portfolio that surpasses those of related majors. How did you go about preparing such experiences or your portfolio?
• If you had to pick the top 3 most important skill sets for becoming a data analyst, what would they be?
• I've heard that a Master's degree is almost mandatory for data analysis roles; do those currently working in the field share the same opinion?
• Points that are good to emphasize in an interview as a rookie data analyst
• As a non-major who has only been studying data analysis for about a year, I still lack a lot of confidence. I feel like my understanding of basic machine learning concepts isn't solid and my coding skills are lacking, so I'm hesitant to even apply for internships because I'm not sure if I can perform well at a company. How can I find the courage to take that first step?

January Seminar
Participant Introduction 📖

Check out the Q&A! 💬

If you're curious about the live participation reviews 👏

What was the most impressive part of the seminar?

• It was impressive to learn that SQL and Google Sheets are primarily used in practice. While most data analysis courses cover Python, R, and even Hadoop, I had a lot of trouble deciding which tools to prioritize for my studies. Now, the criteria have become clear.
• I was impressed by the advice to always ask a question when the interviewer asks, "Do you have any questions?"
• The specific practical examples were impressive. For instance, the story about delivering data via spreadsheets to help facilitate further analysis.
• I decided I wanted to pursue a career in data analysis and was just endlessly making study plans, but the mention of excessive perfectionism really hit home...
• It was great that there were no standardized answers thanks to the diverse experiences of the four individuals.
• It was great because many misconceptions about data analysts (especially the idea that a master's degree is a must) were cleared up, and you shared the truly essential skills and attitudes.
• The content shared during the Q&A session was very impressive! (Which basically means everything was great.) I could really feel the sincerity and care you put into reading through the numerous questions in your answers. Thank you for providing answers from various perspectives; I’m leaving with many helpful hints.

A final word for Datarian!

• Thank you for providing such an opportunity for us to gather.
• In these times when the demand for data analysis is increasing, you are like a ray of light. Thank you.
• It was an even more meaningful session because it consisted of stories based on the four panelists' firsthand experiences.
• The role of data analysis felt like it was shrouded in fog, but I’m glad I could gain confidence and establish a direction for my future studies.
• I always feel a great deal of fear when facing something for the first time, but once I actually confront it, I realize I was just afraid of a shadow cast by something very small. This lecture was exactly like that for me, and I think it will help me maintain a positive mindset as I prepare for my future career.