r/askdatascience

Title: Looking for Data Analyst Opportunities – Fresher | India
Hi everyone! 👋
I’m a fresher actively looking for Data Analyst / Junior Data Analyst / Business Analyst opportunities in India.
I have hands-on experience with:
📊 Excel
🐍 Python
🗄️ SQL
📈 Power BI
🔍 Data Cleaning & EDA
📉 Data Visualization & Reporting
I’m currently building projects to strengthen my practical analytics skills and would love to start my career in a data-driven organization.
I’m open to full-time, internship, hybrid, or remote opportunities, especially in Delhi NCR / Noida / Gurgaon, but I’m also open to opportunities across India.
If your company is hiring freshers or if you know of any relevant openings, I’d really appreciate a referral or lead. 🙏
Resume: Available on request.
Thank you! Any advice, referral, or opportunity would mean a lot. ❤️

MS in Data Science (2025) and BS in CS prior, F-1 visa, working in another field (not data related) 8-5, doing applications and studies in the evenings. I have been getting interviews but always get rejected on the first rounds.

I feel like the issue is that I cannot explain what I know well enough, plus some gaps in the technical side. Looking back, I realized that I have been quite lazy in my studies at University.

I was thinking if it is worth it to invest in a interview coach to help me identify weak points and develop a strategy, but it may be a waste of time/opportunities. For people who have been in similar situations, and for international students in particular: would you recommend such investment?

Also, is it a good idea to stop trying to interview and improve your skills for a while, and then re-apply?

How to develop explanation skills for technical projects and concepts during interview?

And what would you do in my situation over the next 6 months?

One more question: Do any of you have existing knowledge of working in this field who would be willing to mentor me, if only loosely? Someone to review my progressand advise where I have gone wrong would be appreciated greatly.

Because the F-1 timeline makes me feel like i can't just spend a few months thinking about this, any advice would be appreciated

As a beginner I don't know how to keep things like oop in my mind until i reach the level that i need it in as for know (i am studying data cleaning and EDA) i didn't find a use for it yet

Hi everyone. I'm looking for recommendations on courses or certifications that really stand out on a resume. I'm currently a sophomore in college and I'm hoping to land my first entry-level job soon.

I'm highly interested in learning about AI automation and workflow optimization using platforms like n8n. Does anyone know of any good resources for this?

Any advice on what skills to prioritize would be greatly appreciated. Thanks

Hi everyone,

I'm an upper-year undergraduate student pursuing a degree in Data Science, and I'm currently starting to build my first portfolio projects.

I've seen many beginner Data Science projects focused on relatively simple databases, SQL queries, averages, visualizations, and exploratory data analysis. I think those projects are useful for learning, but I also wanted to try building something a little more complex that would allow me to integrate several different areas.

One idea I'm interested in is building a simple slot machine from scratch, but approaching it primarily from a mathematical and data perspective.

I would start by designing the mathematical model of the machine: reels, symbols, winning combinations, probabilities, paytable, hit rate, RTP, expected value, variance, etc. Then I would program a simulation that runs a large number of spins and experimentally verify whether the observed results converge toward the theoretical values.

From those simulations, I would generate a dataset containing information about each spin or session, store the data in a SQL database, and perform queries, exploratory data analysis, and visualizations. Later, I'd also like to explore which Machine Learning applications actually make sense for this type of data—for example, anomaly detection, clustering different types of simulated sessions/behaviors, or introducing additional variables and analyzing models based on them.

My goal is for the project to demonstrate an end-to-end process: problem formulation, mathematical modeling, data generation and storage, SQL, statistical analysis, programming, and eventually Machine Learning, rather than simply working with an already prepared dataset.

My main concern is how this might be perceived in a professional portfolio.

Do you think a project involving a slot machine or gambling could be viewed negatively by recruiters or companies if I publish it on GitHub/LinkedIn, even if it is presented as a technical probability, statistics, and Data Science project?

Or, if it is properly documented and the objective is clearly explained, could it actually be an interesting project for demonstrating technical skills?

I'd also appreciate any suggestions about what you would add, remove, or change to make the project stronger from a Data Science perspective. ty

https://preview.redd.it/g3h65ltymsjh1.png?width=767&format=png&auto=webp&s=e01808b3b6d935d47ae5d9f5c8300345c72fabed

A business team wants to test something. They pull a customer list, split it in half, and run the test. That split is usually where it goes wrong.

Splitting randomly across the whole base can leave one group with more customers from a specific profession, region, or education level than the other. When the results come in, nobody can tell whether the difference came from the test or from the composition of the groups.

There is also the sample size question, which most teams skip entirely. If the base is too small to detect the effect you care about, the test will come back inconclusive no matter how well you run it. That is a calculation you do before, not something you discover after two weeks of waiting.

I built a pipeline that handles this part: upload a customer base, get back two stratified groups with proportional composition, the minimum sample size backed by power analysis, and a statistical check (t-test and chi-square) proving the split was fair. Output is two CSV files plus a plain text summary the business team can actually read.

Tested it on a bank marketing dataset with 11k customers. Stratified by profession, marital status and education, the resulting groups came back with p-values above 0.99 across every variable, meaning no meaningful skew in either direction.

Hub: https://aiforfintech.tech
Github: https://github.com/junidepieri-design/expd-001-ab-test-design-pipeline

How does your team handle the split when designing a test?
👊

If you suggest to go with the paid course. Then I have two options: 1. Sheryians Ai school's data science course 2. Code with Harry's data science course.

Please tell me which one would be better for me to pursue and start my learning ASAP.

hi , i completed my 12th(or PUC) then joined a 6 month diploma in design and currently having a 1.5 years of experience in design field . i tried to get into core

ai ml but it looks like too much competition for degree holders only, so

is it possible to get a data analyst job without a formal degree ? anyone got it before.

consider the current AI impact also and i going to pursue bootcamp course in Bengaluru Excelr , is it okay or shall i self study ?

or instead of data analyst shall i try something else in technical side .

( please don't comment to go into design only )

thanks

What is the best approach to detect anomalies with clustering or so, in time series data?

Asking for Product Data Scientist interviews specifically.

Would I be expected to memorize and calculate values off of the top of my head using formulas for things such as power analysis, t-test test statistic / p-value calculations, CUPED variance reduction, etc? Or would simply being able to understand the logic and intuititon behind it be enough?

For example:

If an interviewer asks you to size a two-proportion A/B test on the spot, are you expected to produce the formula and compute, or is describing the inputs and tradeoffs acceptable?

I know it may be pretty dependent, but would really appreciate anyone with experiences at interviewing at companies such as these to help me understand the scope. I feel that I understand the logic really well, but I don't know if it's worth memorizing formulas and etc.

TLDR (same as title) - Am I expected to memorize formulas for Big Tech Product Data Scientist Interviews (e.g. Google, Meta, Uber)? Or would it be enough to simply understand the intuition and logic behind them.

Hi There! I am currently going to school for my masters in data science, approaching the latter half of my degree plan. I've done classes on data wrangling, machine learning, intro to programming and statistics for it. I've really enjoyed learning what I've learnt so far, and am looking forward to practicing and learning for...well...the rest of my life probably lol. My current job just changed from an analyst to a manager of analysts, so I'm actually taking a step away from coding on a daily basis, which means I'll be doing it more in my spare time to keep sharp.

This class there's an assignment that requires us to reach out to different data professionals on communities like this one and establish a rapport with other people in the field as well as talk about data and projects in depth. My current school project is looking at census tract level data and trying to correctly classify records as "low income, low access within a half mile urban, and 10 miles rural". I've used logistic regression, decision trees, and random forest as my models. I'm happy to answer any questions and hear any feedback from people if you all want more details!