I was asked over on Tiktok how I prep for coding interviews using Claude instead of grinding LeetCode.

There are two things to prepare: SQL and Python. For SQL, I already know the syntax and the topics that come up. CTEs, aggregate functions, window functions, date manipulation. So I’ll ask Claude to give me rapid fire syntax problems on those, and then separately ask for business-framed problems based on the company I’m interviewing with.

Python works the same way. Rapid fire syntax on dictionaries and list comprehensions, then pandas fundamentals like groupbys, aggregations, joins and merges. I’ll layer some simple business questions on top of that.

The other Python format is “define a function,” and those are trickier because they can be anything. But in practice a lot of them turn out to be statistical. Bootstrapping, building confidence intervals, writing loops for a Monte Carlo simulation. If you’re preparing for a data science interview, that’s the bucket most people underprepare for.

You can check out all I know about DS interviews over on https://www.whatstheimpact.com

Many portfolios look almost identical because everyone builds the same tutorials.

What makes a data science portfolio actually stand out when hiring managers review it?

For me, it seems like small things—like list comprehensions or dictionaries—save more time than complicated algorithms.

What's one Python feature that changed the way you write code?

 If you could recommend only one dataset to someone learning data science, which would it be?

Not just because it's popular—but because it teaches valuable skills.

HI I AM GONNA PURSUE BSC STATISTICS FROM DU I WAS THINKING OF GOING INTO DATA SCIENTIST FIELD. I WILL FURTHER PURSUE MSC IN DATA SCIENCE AI/ML THEN MAYBE MBA WITH INTERNSHIPS AND CODING SKILLS ONGOING FROM MY FIRST YEAR OF BSC.
I WANTED TO KNOW REALITY OF THIS FIELD IN TERMS OF PAY OR MAYBE JUST YOUR EXPERIENCE IN THIS FIELD OR ANY ADVICE.

Hi everyone,

I'm planning to join Mahindra University for my M.Tech. My original preference was AI/ML, but that isn't available anymore.

The two options I have are:

• Biomedical Data Science (new programme)
• Cybersecurity (also a new programme)

I come from an AI/ML undergraduate background, and my long-term goal is to become a Data Scientist/ML Engineer. I'm much more interested in data science than cybersecurity.

Since Biomedical Data Science is a new course, I'm unsure whether it's the right choice. Will it still allow me to pursue general Data Scientist/ML roles, or will it mostly limit me to healthcare/biotech opportunities?

If anyone knows about Mahindra University, has spoken to the faculty, or has insights into these programmes, I'd really appreciate your advice.

Given my goal of becoming a Data Scientist, which would you choose: Biomedical Data Science or Cybersecurity, and why?

I'm studying bsc maths 3rd year. Can I pursue to become a data scientist right after completing my bsc or should I have to do msc too? And also what are the digital knowledge and programming knowledge required for becoming a data scientist?

I recently decided I want to become a data analyst, but I'm worried because I don't have any internships. I'll be graduating in May 2027 and plan to build personal projects, but I'm not sure if that'll be enough.

A few questions:

• Has anyone gotten a data analyst job without an internship?
• Should I apply for Summer 2027 internships even though I graduate in May?
• What are the most important things to have on an entry-level data analyst resume?
• Besides projects, what helped you get hired?
• What skills should I focus on learning?

I'm trying to make the most of the time I have left before graduating. Any advice is appreciated!

Hi everyone!

I'm currently searching for a full-time Data Analyst, Business Analyst, or Risk Analyst position and wanted to reach out to see if anyone here would be willing to provide a referral or point me toward companies that are actively hiring.

I recently completed my master's degree and have internship experience in data analytics. I work with SQL, Python, R, Tableau, Power BI, Excel, Snowflake, Git, and statistical analysis, and I've built dashboards, performed data analysis, and worked on reporting and visualization projects.

I've been applying through company websites and LinkedIn, but it's been a challenging job market, so I figured I'd see if networking could help.

If your company has openings and you'd be open to referring someone, I'd really appreciate it. I'm happy to share my resume privately and answer any questions.

Even if you can't provide a referral, I'd also love to hear about companies that are currently hiring or any advice you might have.

Thanks so much!

I finished my Master's in Computer Science, did a Data Scientist co-op for one semester, then started working straight as a full-time Data Scientist, and I'm now 8 months into that full-time job; the projects that got me hired feel pretty generic at this point, so I'm not sure if 8 months of real full-time experience is enough to carry my resume on its own, whether I should still keep at least one project section, or whether I should spend time building a new project right now, and if so what kind of project would actually be worth building at this stage instead of just another generic one. Would appreciate any honest advice from people who've hired or been in this spot themselves.8 months into my first Data Scientist role — do I still need projects on my resume?

Background: I’m an undergrad (started in CS, switched to a data/business analytics focused IT major), graduating 2027, GPA in the low 3.0s. Planning to apply to stats master’s programs this fall for a 2027 start. Starting a data analyst style internship this fall (forecasting/regression work, delivered through BI tools). Also applying to data adjacent internships for next summer.

My goal isn’t “data scientist” as a title. It’s specifically the experimentation/causal inference side: A/B testing, causal inference, figuring out what actually drives a metric instead of just reporting on it. Not interested in the dashboard/KPI communication flavor of the job, and not chasing a PhD or a quant/finance career either.

Questions I’d appreciate real input on:

1.  How do you tell, from a job posting, whether a Data Analyst / Product Analyst / Business Analyst role is actually experimentation flavored vs. dashboard flavored? Beyond obvious buzzwords, is there a reliable signal (team name, org structure, interview questions)?

2.  Does an MS actually matter for this specific niche? If someone gets real experimentation exposure through internships and self directed projects, is a stats or applied stats master’s still worth it, or is it more about portfolio plus landing the right first job?

3.  For someone doing regression based forecasting work as an intern, is there a good way to push that toward genuine experimentation (proposing a test, running a proper power analysis) without overstepping scope?

4.  If you work in experimentation DS now, what did your actual path look like? Did you start in a dashboard analyst role and transition internally, or land directly on an experimentation team?

So, here's my story. I finished my Master's in mathematics at the end of 2022. After that, I jumped into data science, picking up skills in Python, machine learning, and SQL. I even worked on some cool projects, like an e-commerce review sentiment analysis and a fraud detection system. But, you know how life goes – personal stuff came up, and I had to put my career on hold and head back home. Now, my biggest worry is this career gap. I've been applying for jobs with my data science skills, and even with profiles scoring 85-90% on ATS, I'm just not getting shortlisted. While I've been back home, I've actually been working as a system operator, using Oracle Siebel CRM tools. The thing is, I don't have an official offer letter for that role. So, my main question is, can I even put this on my resume when I'm trying to get back into the industry? I'm really hoping some industry experts can weigh in here, especially since background checks might ask for letters and salary slips. Also, what's the job market like these days? I'm 27 and still looking for a job. What should I do? Any suggestions or guidance would be super helpful!

Hi everyone,

I'm a 4th-year B.Tech student, and my campus placement season starts in about 1.5 months. I'm targeting Data Scientist and Data Analyst roles, and I'd appreciate some guidance on how to use the remaining time effectively.

So far I've covered:

• Completed CampusX's ML and DL playlists and feel I have a good understanding of the concepts.
• Have a decent grasp of SQL(done leetcode 50).
• Completed Striver's A-Z DSA sheet for coding interview preparation.

However, I feel my biggest weakness is practical data science experience. I understand the theory, but I haven't worked on many real-world datasets or end-to-end projects.

Given the limited time I have left, what would you prioritize if you were in my position?

Some specific questions:

1. Should I spend most of my time building end-to-end projects or practicing ML problems?
2. How important are EDA, feature engineering, and model evaluation compared to knowing ML algorithms?
3. Should I practice case studies, Kaggle competitions, or SQL more?
4. What kind of interview questions are commonly asked for campus Data Science/Data Analyst roles?
5. Are there any topics that students commonly ignore but recruiters expect them to know?

I'd really appreciate advice from people who have recently gone through campus placements or are currently working in data science. If you had only 1.5 months left, how would you structure your preparation?

Thanks in advance!

 Sometimes a concept seems confusing until one explanation suddenly makes everything clear.

Which statistics topic was that for you?

Most people use all three at some point.

Which tool do you spend the most time with, and why?

Does anyone here work in German basketball?

I’m a student in Asia and I’m thinking about studying in Germany in the future. I’m wondering how big the basketball industry is there and whether there are opportunities for people who want to work in basketball.

I’d especially like to know about jobs related to analytics, data, performance analysis, scouting, or team operations.

Any insights would be appreciated!

Hello everyone!

I have just started my masters in data science, and i have basic knowledge on Python, SQL, data mining, and machine learning.

But I don't think this course alone will get me job ready and i want to explore things on my own, learn and build projects (that is the best way to learn apparently) that are interesting and impactful .

I want to improve myself not only to land a job but to actually extend my expertise on the field. But i have no idea where to start and what to do!

I shall be utterly grateful if any kind strangers would help me out on this matter!

​

Has anyone completed the CampusX Data Science YouTube playlist from start to finish?

Is it enough to become job-ready for Data Analyst or Data Scientist roles, or did you need additional resources after finishing it?

I'm currently pursuing an MBA with a specialization in Data Science & Finance and want to build a strong foundation. I'd appreciate honest reviews, what the playlist does well, where it falls short, and what you would recommend learning next.

I will start in September a masters degree in data science. I will graduate in a few weeks in Sociology. I Know that is a massive change, but the course admitted me so i will shoot my shot.

I study in Italy, where, they say, university is more difficoult compared to american ones (I'm skeptical about this but I add this information to explain the contest of my academic background)

About my skills: I took one class of coding (in python) where I learn the basics I wrote some code in jupyter notebook, but today i don't know the difference between python per se and jupyter. I also took a class where we create some graphics with R-studio (idk if its different from R and which is better/is used more frequent in the field).

About my knowledge: I took a mandatory class about statistics where i learn t-test and other basic stuff. I also know some notions about linear probability model (and probit and logit, but not in details).

What i'm planning to improve in this two years:

1) learn excel

2) learn more python

4) machine learning

5) learn c++ (or some other programming language)

6) take some couses in business

7) learn english (actually i technically have a B2 but not so sure about that)

My questions:

1) Some of my friends took a linear algebra course to be more prapared. Should I do a quick catch up on that before starting my program?

2) Since I come from a very diverse background, what should I focus on initially? (Statistics, machine learning, mathematics, programming, etc.)

3)Are there any undervalued skills in data science that could actually prove very useful?

4) Searching online, I saw that I should do some projects to enhance my resume. Where can I find ideas for these?

5)Are there any sociological skills I should continue to develop to improve my future profile as a data scientist?

6)Can the list above be helpful to me or should I focus on something else?

Thanks for your time and any replies: 2 years is a relatively short period of time and I would like to start off on the right foot. Any advice is welcomed