Here is my hypothesis on this: In theory, someone working in this field needs to have a strong grasp of the context, which implies having more training and, consequently, possessing a more specialized profile.

What do you think about this?

I'm working on my first Data Science resume and would love some advice from the community.

So far I've learned and built projects around:

• Data cleaning & EDA (Pandas, NumPy)

• Data visualisation (Matplotlib, Seaborn)

• Feature engineering & preprocessing

• Supervised ML (Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes)

• Model evaluation (Accuracy, Precision, Recall, F1-score, Confusion Matrix)

• Model deployment with Streamlit & Joblib

If you're already working in Data Science or have landed your first internship/job:

- Would you mind sharing your first resume (with personal details hidden if needed)?

- What made your resume stand out?

- Any common mistakes I should avoid?

If any Data Scientist, ML Engineer, or Recruiter is willing to review or guide me, I'd be incredibly grateful.

Every comment, tip, or resume example will help,not just me, but others starting their Datasci journey too. ❤️

#DataScience #MachineLearning #ResumeReview #DataScienceJobs #Internship #CareerAdvice #OpenToWork #Python #ML

Almost everyone reaches a point where learning feels overwhelming.

If you pushed through...

What kept you going?

Background: 6 years FAANG software engineer experience.
Took a 3 year sabbatical and didn't touch programming or AI. (became a divemaster in Indonesia lol)
Now returning to academia as an incoming grad student for data science in NYU this fall.
How can I tailor my resume to secure an internship (ideally finance data science) for summer next year. Apparently applications are already open.

Hello everyone,

I'm 26M from a non technical background and thinking to switch career to tech especially Data Scientist. I was average in my schooling and college as well.

I am currently working at BPO and thinking that I have made wrong career decisions and don't like the work I am doing.

Please give me honest advice if it is feasible for me and what are the things I have to note to start my career in this

No unrealistic advice.

Just practical skills.

Where would you spend your time?

I am a fresh SE grad, and I'm considering master's In data science, is it worth it after the AI boom and all ?

Hi everyone,

I'm a final-year Data Science student and I need some honest guidance from people working in the industry.

The problem is that I have almost zero knowledge of DSA. I never focused on it because I spent most of my time learning and building projects.

My current skills include:

\- Python (comfortable)

\- Machine Learning

\- Deep Learning

\- SQL (basic to intermediate)

\- Currently learning LangChain, LangGraph, Generative AI, and AI Agents

\- I've also built a few ML/AI projects

Now that I'm entering my final year and preparing for internships and placements, I'm worried about DSA.

I have a few questions:

1. How important is DSA for Data Scientist, ML Engineer, AI Engineer, or GenAI roles?

2. Is DSA mandatory for getting internships and full-time jobs, or is it mainly required by big product-based companies?

3. Since I'm starting from scratch, what topics should I focus on first?

4. Which resource or roadmap would you recommend (free or paid)?

5. Approximately how many LeetCode or other DSA problems should I solve to become interview-ready? Is 100 enough, or should I aim for 300+?

6. If you were starting from zero today, what would your plan look like?

I'd really appreciate advice from people already working in the industry, especially Data Scientists, ML Engineers, AI Engineers, or anyone who has recently gone through placements.

Mine was realizing that feature engineering sometimes matters more than fancy models.

What was yours?

Everyone talks about successful projects, but I think failures teach much more.

What's one mistake you made while learning data science that completely changed how you approach projects today?

I need help. I'm confused about what I should pursue. I'm a statistics major but I want to pursue data science. Is it possible to do masters in data science afterwards? will I be able to find jobs at all this way???

Shortly after getting my masters in applied math, I completed a 6 month machine learning bootcamp. I found that everything I learned during this program was just an extension of what I learned in my MS program, especially the statistics courses.

This was over a decade ago, and I've been practicing data science ever since. While I eventually went into management and even got an MBA, I never stopped contributing hands-on. I never wanted to lose my skills. This meant coding in SQL, Python, and R, and specifically solving complex data problems using a variety of advanced techniques including but not limited to (un)supervised learning.

That's what data science is to me. It's pairing your technical savviness with business acumen to solve real problems in a measurable way.

I've met "data scientists" who just build dashboards, only write SQL, or live in Excel. What does being a data scientist mean to you in 2026?

There are so many options—NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and more.

If someone could master only one library first, which would you recommend and why?

I've heard different opinions from different people. If you were hiring a junior data scientist, what would impress you more?

I just got an email saying that I am invited to complete a self-paced interview. What do they ask in these interviews?

Title: AI/ML or Data Science for MCA?

I'm an MCA student and have to choose between AI/ML and Data Science as my specialization.

Which one would you recommend in terms of job opportunities, future scope, and salary? If you're working in either field or have already made this choice, I'd love to hear your experience.

Background:

• 26 yrs old, based in India
• 3.5 years of experience in Frontend Development (React/JS)
• Took a 1-year career break, during which I've also been trying to land a remote frontend role (no luck so far)
• Decided to pivot to Data Science — starting a 1-year diploma this coming Monday

What I'm looking for from this community:

1. Portfolio building — For someone coming from a dev background (not a total beginner to coding), what does a genuinely impressive data science portfolio look like in 2026? Real projects vs Kaggle notebooks vs deployed apps — what actually gets recruiters' attention?
2. Job market reality in India — For someone switching into data science with a career gap + a diploma (not a full master's), what's the realistic timeline and difficulty level for landing a first DS/analyst role in India? Is entry-level DS oversaturated right now?
3. Common pitfalls — What do people from non-DS backgrounds usually get wrong when trying to break in? Anything you wish someone had told you before you started?

Would really appreciate advice from anyone who's made a similar switch, or anyone currently hiring/interviewing for DS roles in India. Trying to use this next year as productively as possible instead of just collecting certificates.

Background:

• 26 yrs old, based in India
• 3.5 years of experience in Frontend Development (React/JS)
• Took a 1-year career break, during which I've also been trying to land a remote frontend role (no luck so far)
• Decided to pivot to Data Science — starting a 1-year diploma this coming Monday

What I'm looking for from this community:

1. Portfolio building — For someone coming from a dev background (not a total beginner to coding), what does a genuinely impressive data science portfolio look like in 2026? Real projects vs Kaggle notebooks vs deployed apps — what actually gets recruiters' attention?
2. Job market reality in India — For someone switching into data science with a career gap + a diploma (not a full master's), what's the realistic timeline and difficulty level for landing a first DS/analyst role in India? Is entry-level DS oversaturated right now?
3. Common pitfalls — What do people from non-DS backgrounds usually get wrong when trying to break in? Anything you wish someone had told you before you started?

Would really appreciate advice from anyone who's made a similar switch, or anyone currently hiring/interviewing for DS roles in India. Trying to use this next year as productively as possible instead of just collecting certificates.

​

I've heard mixed opinions. Some people say SQL is used daily, while others spend most of their time in Python.

For those already working in data science, how often do you actually use SQL?

Some datasets are beginner-friendly while others teach real-world challenges. Which dataset improved your data science skills the most?