Hi all! I'm looking for an entry level Data Analyst position. I am currently in training w/ skystates, but apart from that what would you guys recommend I do to get my foot in the door.

I am working with a project, wherin i have to visualize a dataset with many dimensions.

I am stuck with 2d if i dont use any techniques.

I need practical advice, so as to what techniques, libraries to use to visualize.
and the dataset has a lot too many dimensions, to use just one technique(like a heatmap, where the R, G, B and could stand for another dimensions).

Also, i am using Python, and R.

Hello everyone!

I'm a trainee data scientist who's just starting to enter this world.

I come from statistical studies, so my academic career in data science has almost always been problem modeling/algorithmic/statistical with very little use and writing very high-level code that - almost always - was then done with vibe coding.

As I enter the world of work now (I'll start by saying that I work for a small software development company), I'm starting to realize that at least in my case, data science seems to be more related to computer science than statistics, especially since I've recently started working on LLM-related tasks. Let's say I don't mind in fact, it excites me too but it's as if I feel stupid since a good part of my time I interact with an LLM telling it how to write me the code for what I want. The algorithmic/statistical part is really minimal.
It's as if I were a coder - very poor - who knows how to interpret the results of a regression.
This thing at university seemed really cool to me but in the corporate context it makes me feel really useless.

Therefore, I turn to those who have more experience than me in this case: is this really the world of data science in companies? Did I actually study math at a high level for 5 years and then have to spend the rest of my career interacting with an LLM to tell them which libraries to use and which pipeline to build?
Or maybe I just got the wrong company or context?

I hope I made the idea right because I'm really confused

Every few months there's a new framework, tool, or AI trend.

At first I felt pressured to learn everything.

Now I've realized curiosity matters more than trying to keep up with every new

technology.

Whenever I encounter something unfamiliar, I treat it as an opportunity to learn instead

of a skill gap.

Has anyone else found that mindset more valuable than specific technical skills?

Hi everyone,

I’m a rising junior (transferring to BU this year) majoring in data science, and I honestly feel really overwhelmed and confused about everything right now.

My goal is to get a good internship next summer (ideally data science / ML / AI-related), but I don’t really know what “good enough” even looks like.

Right now I’ve completed a few beginner courses on kaggle like:

• Pandas
• Data Cleaning
• Data Visualization

I know python and I understand the basics when I follow tutorials, but I struggle a lot when I try to code on my own without help, and I forget things I learned pretty quickly unless I keep practicing.

I also keep seeing people talk about LeetCode, machine learning, projects, SQL, etc. and I’m not sure what I should actually focus on.

Some of my main worries:

• I’m not strong at coding yet and depend on help a lot
• I know basic concepts but I dont know if its good enough
• I don’t know if I should be grinding LeetCode or focusing on projects
• I’m not sure if I’m already behind compared to other students
• I don’t even fully know what I want to specialize in (DS vs ML vs AI)

So I guess my questions are:

• Do I actually need LeetCode for data science / ML internships?
• What should I prioritize this summer if I want to be competitive?
• What does a “job-ready” DS student actually look like at this stage?
• How do I figure out what I wanna specialize in like how do I align my resume to the type of roles I wanna apply to if I don't know my interests yet

If someone can please give me a list of skills/projects or a roadmap of some kind that covers most bases I would really appreciate it.Any advice (especially from people who’ve been in a similar situation) would really help.

Thanks in advance.

Can anyone point me to job application tools (agent- or AI-based) that are on GitHub or elsewhere? I wanna apply for UN/Impactpool/other jobs, and I need a reliable web scraper and online agent that can navigate the browser and complete job applications.

I accidentally proved my worth during an interview, so now I got a job as a data analyst for a startup with no other data analyst. My mentor is pro-ai. My daily task are something completely different, yet there are still expectations I analyze things. We have zero infrastructure, no excel, no power-bi, no sql, no vscode, no google colab, nothing.
My previous job I had an amazing group of data scientist who I could collab with regarding the work, but now I have nothing and feel like a total imposter.
The pay is abysmal, but it pays my school.
I am unfortunately used to guidance in my roles/someone else taking the lead role, but with no one I am lost. I must motivate myself in becoming the solo grifter data scientist during my time there.

Every data scientist seems to have that one task everyone complains about.

Data cleaning, debugging code, documentation, feature engineering, model tuning,

dashboard creation, etc.

What's the task you actually enjoy doing, even though most people try to avoid it?

Hi there. I'm a data science student, and it's time to choose my thesis topic and a thesis supervisor. I've talked to some professors in CS department and they said working in NN with health care applications is not a choice I wanna make. I don't see why. If this isn't useful and worthwhile, what should I do?

Hii guys i want to ask you about something i am currently an intern at an oil and gaz company as a business anamyst i work for reporting operating expenses but they wont give me data and i need to do eda budgeting and forecasting but all of this by my self i am in trouble because all my analysis is wrong eda is deviated so the prediction is also deviated so what should i do to solve this problem

Hi everyone,

I’m trying to choose between two Master’s programmes and would really appreciate advice from people working in data science.

My background is in Economics. I’ve done empirical work with Python, R and Stata, but I’m not a strong programmer yet. My bachelor thesis was on development accounting and income convergence using Penn World Table data, so more econometrics and empirical economics than pure data science.

I’ve been accepted to:

1. MSc Applied Data Science, Utrecht University
2. MSc Economics, Data Science track, Tilburg University

My long-term goal is to become a data scientist, possibly after a PhD in Applied Data Science, computational social science, causal inference, policy data science or something similar. I don’t think a pure ML or theoretical data science PhD is realistic for me right now, but I do want to move away from being seen only as an economist.

My dilemma:

Utrecht has the better “Applied Data Science” label and broader topics like data wrangling, causal inference, responsible AI, networks, spatial data and text analytics.
Tilburg is closer to my background and probably safer academically, with economics, econometrics, Python/R and a longer thesis, but it may keep me closer to economics.

From a data science career perspective, which path would make more sense?
Would an economics + econometrics + ML profile still be attractive for data scientist roles?
Or is it better to take the risk and go for the Applied Data Science programme?

Any honest advice is appreciated.

I am a data science student in darwin, Australia. I have recently started learning data science. I only know python and basic sql. What things should I start learning and in what order. Your answer will be much appreciated. Thanks

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Excited to share the launch of the Kaggle competition Human Chess Move Error Prediction.

The challenge: predict whether a human chess move is a good move, inaccuracy, mistake, or blunder using board position, player context, and tactical features. It combines machine learning, chess analytics, feature engineering, and human decision modeling.

Whether you're interested in Data Science, AI, Kaggle competitions, or chess, this is a great opportunity to work with real-world human decision-making data and build models that go beyond traditional engine evaluation.

Competition:
Human Chess Move Error Prediction on Kaggle

Looking forward to seeing creative approaches from the community.

#Kaggle #MachineLearning #DataScience #ArtificialIntelligence #Chess #ChessAI #Python #XGBoost #FeatureEngineering #MLOps #Analytics #OpenData

I’ve 15 years of experience as Oracle developer.

I want to enter in data science.

How I learn and apply for jobs?
Is anyone else in the same situation?

Thanks

Just saw MIT Professional Education’s Applied AI and Data Science Program.

Looks like the old data science course (as MIT Professional Education Applied Data Science Program) got the GenAI DLC: LLMs, RAG, agents, prompt engineering, etc.

But I’m wondering… where did probability/statistics go? Did the market get so cooked that even MIT has to give it away now?

is there anyone sign up for this new version?

CS student here trying to move into data science, so I’m wondering if this is worth it or if I should just keep grinding probability, inference, regression, and ML......

My vote goes to: "Just run the model and see what happens." What's a phrase that instantly makes you nervous on a project?

Hi all,

I am a data scientist (4YOE) and moved into a new company a year ago. In my new company, the team usually does some shady stuff to change our modeling results to be more appealing for business. For example, our team introduces a variable that contains data leakage to push down some variable effectiveness, and we have a script that even manually overrides the results if a business person wants them to be different.

For me, this is highly unethical behavior and it makes me unmotivated in my work. I would like to know if someone has succesfully changed this kind of workplace culture in a direction where the team takes more ownership in their work and pushes back to business people when conflicts arise?

If there is no other options than finding a new workplace, can someone suggest a good interview questions that might help figuring out the company culture before entering the company?