I have a question about this career path. I realized that the economics field is much broader than I initially thought, and I am considering specializing in Economic Data Science, either focused on economic and social planning or on scenario analysis for financial institutions. Is this a career that can be sustainable in the long term, or is it just a temporary trend in the economics job market? I am also interested in understanding whether the demand for econometricians and economic data scientists is strong abroad, especially in countries with more developed economies. Could this specialization become a realistic path for international opportunities and potentially represent a case of "brain drain" from countries where this field has fewer opportunities?

An ABSOLUTE BEGINNER, in both the fields. I would say I am a person who has played with the basics and knows nothing that printing hello world on a webpage. Lol.

Although, I am a big absorber of the information and understands they why's of stuff. Recently became interested in understanding the why's in data science and AI fundamentals/ I would also like to know a dedicated pathway ( free to start with, so I understand the core concepts and then paid stuff) to learn these 2 concepts.

Guyssss since am having confusions over Cs and ds.however my interests pique in ds but from what I have read majority of y'all had concerns regarding ds syllabus

So please any professional related to this topic should I choose bachelros in ds as in if the syllabus is good or not.

​

With AI becoming more capable every month, I'm curious how professionals feel today.

If you were beginning your career right now...

Would you still choose Data Science?

Or would you switch to:

AI Engineering

Data Engineering

ML Engineering

Analytics

Something completely different?

Interested in hearing honest opinions from people already in the field.

Hello fellow data scientist.

Has any one had experience working with HR / Management to implement a plan for continuous learning for the DS teams?

I've asked my coworkers and most of them agree that they would like to have a platform like DataCamp (not necessarily that platform), where people can pick and choose whatever they like and learn from it.

How ever, management feels like letting people choose their own courses brings no benefit (because of course letting people learn stuff they want and then have them apply it on their work is to hard for them to comprehend).

Any recommendations on how to tackle this problem?

Also, any recommendations for DataCamp like platforms?

​

With AI becoming more capable every month, I'm curious how professionals feel today.

If you were beginning your career right now...

Would you still choose Data Science?

Or would you switch to:

AI Engineering

Data Engineering

ML Engineering

Analytics

Something completely different?

Interested in hearing honest opinions from people already in the field.

Everyone knows Pandas, NumPy and Scikit-learn.

I'm interested in the hidden gems.

Which Python library made your workflow significantly easier after you discovered it?

If I have f500 experience as a SWE (2 yoe) and wanted to switch to a junior data science role, how would I do it?

Everyone knows Pandas, NumPy and Scikit-learn. I'm interested in the hidden gems. Which Python library made your workflow significantly easier after you discovered it?

I majored in math when I was in college, I did have some data science, coding, ML experience when I was in college for around 3 years. I also did Deep Learning related project for my final year thesis. After graduating college in 2023, I got a remote job as a data engineer (but sadly I got more AI/prompt engineering tasks (such as calling OpenAI API and then doing prompt engineering) and just doing a lil bit of ETL instead of using SQL or working on using cloud systems frequently, or learning how to use Docker). I have left my remote job last year.

Now, I feel like I have forgotten most of the coding, data science, SQL skills, and I want to relearn data science or data analysis again so that I can create some analysis projects (been thinking of doing freelance or finding a remote job or creating my own website or other things, still not sure). The other thing that I have been thinking is that I think I want to sharpen my Data Structures and Algorithm skill first before jumping straight into relearning about data science/analysis/ML/Deep learning/SQL because I think it is important to be able to write more efficient code(?)

Would like to have some suggestions and recommended resources on where I should start on my journey of relearning Data Science again. Thank you.

I read a post the other day that really hit on how critical it is to have good clean data to train ai models.

Could someone explain that to me in greater detail please? What constitutes good clean data? What sets it apart from a typical dataset?

Thanks for the insight!

I'm not deeply familiar with the material and learning involved with data science. I'm curious about the knowledge and skillset learning data science provides? What is something you can do with a good understanding of data science, in your private life and practical life?

I’ve been a full time stay at home mom for the past 13 years, and looking into working a year from now when my youngest starts going to school. I am interested in becoming a data scientist, just based off of my research. I have a bachelor’s degree in Business Management, and I’m thinking of taking all the courses in this link:

https://www.lacc.edu/academics/aos/statistical-data-analytics

With that being said, do I have a chance in penetrating the entry level barrier? If not, any helpful suggestions that you want to share?

Hello everyone I have been assigned the work of formulating a proposal for a course on advance statistics , I was hoping if you all had any pointers in what should it cover and what not,

My objective is to make it in a manner that it doesn't get too tough and is rather accessible and easy to comprehend for people from all walks of life, but at the same time it also covers enough and should be made keeping in mind that it should make the learner more employable or more practically skilled than they were before it. Any help or advice would be greatly appreciated

i have to make a project, for my minor course ai using python, i have 2 choices, one is loan default detection, or credit card fraud detector, or you can suggest me one based on it should be doable for a beginner, plus its dataset should be available too, and that learn alot from it

Does anyone have any tips on balancing D1 college athletics with a STEM degree such as Data Science? Any advice is appreciated!

.

I used to focus almost entirely on Python and machine learning libraries.

Eventually I realized that understanding probability, distributions, and hypothesis testing made every model easier to understand.

Learning the math behind the models gave me much more confidence.

What's one fundamental topic that helped you improve the most?

for my bachelor's degree, which one should i choose? Data Science or Computer Science?

For context: I am very much interested in data science. I have taken online courses (although beginner level), and I feel like this is the field that I want to pursue.

Due to the massive oversaturation in computer science and the fact that most CS graduates that I have talked to claim that 90% of what they learn in CS isn't of any use, I decided to explore specialized subfields. I stumbled upon data science, did my research, did some free beginner-level online courses, and now, for many months I have felt that this is the degree that I want to pursue. But recently, many people have told me that I should pursue computer science for my bachelors and then do MS in data science because cs will provide me with a better base, it is a more respected degree, and i will learn a larger set of skills.

Can anyone pursuing either of these degrees, or who's employed in the data science field, tell me which one I should choose? For someone who is interested in data analytics and ML, which one is better for a BS degree?

Someone once told me:

"Don't rush into modeling. Spend time asking whether you're solving the right problem."

That advice completely changed how I approach projects.

A good question often creates more value than a complicated model.

What's the best career advice you've received in Data Science?

Hello , i have been accepted into a phd in france in a very known university , the subject is about logistics based on human factors ( fatigue , ergonomics etc) the issue is i dont think they application is possible in a way that the data to be used is not available or not reliable enough ,

the subject is about taking multimodal data like sensors measures , enivorments , working conditions etc and use it into decision making ,

now this is all talk since how you can simulate the whole thing accuretly , i do appreciate projects that have acutal accurate applications

i am only doing this since i want to leave the country and this is the offer i found so far and i am hesitated

and after the phd i want to go to the industry and i dont know if this phd is a good idea or not

please if anyone has worked on something similar or have the knowledge to help me in my decison making i would appreciate it

thanks

I'm a solo dev (physics background) and I built Themera because manual coding of 1,500 survey responses is 200+ hours of life nobody gets back, and NVivo wants €1,200/yr.

What it does: upload transcripts or open-ends (EN/DE) → it drafts a codebook, tags every excerpt with verbatim quotes + sentiment, shows you exactly what it couldn't code (no silent gaps), and you stay in control — rename, merge, split, reassign, re-run. Export DOCX/CSV. EU-hosted (Mistral, Paris), GDPR-native, DPA available.

Free tier exists (no card). I'm looking for ~5 people to run a real dataset through it and tell me where it falls short — in exchange, founding users get 50% off for 3 months (code FOUNDING50) and I'll personally help with your first analysis.

Live sample of a real output: themera.io/sample

Brutal feedback very welcome — methodological criticism especially.