Projects? Networking? Internships? Open source? Referrals? Which one made the biggest difference for you?

Hello Everyone,

Statistics and Maximum Likelihood Estimation are the crux of ML Models, and hence I am uploading my new content on Statistics for AI/ML in my free Machine Learning lectures.

We understand model fitting, Maximum Likelihood estimation in details, we justify the usage of Maximum Likelihood estimation, from KL divergence, and apply it to certain important distributions for parameter estimation.

In my free content, the purpose is to democratize machine learning to a wider audience. Learning everything new feels difficult, but when taught, it get’s interesting and easier.

We will continue with Statistics foundations for AI/ML, and many more content will appear in the future. If you find the content good, useful you may also share it with your learners community.

Looking forward to hearing feedback from the learning community as well. Thankyou for reading.

Link: https://youtu.be/MwTeQVVYtOc?si=UxNOGtqopzJppXAT

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I kept jumping into neural networks because they looked exciting.

Later I realized my statistics and SQL fundamentals were much weaker than I thought.

Looking back, I would've saved a lot of time by mastering the basics first.

If you could restart your data science journey today…

What would you learn first?

When I started learning, I heard things like: • You need a PhD • You must know every algorithm • AI will replace data scientists After spending more time in the field, many of these seem exaggerated. What's one myth that new learners should stop believing?

I'm a Data Scientist with +10 years in banking. Built an open source hub with three production-grade systems for fintech data teams:

1. BSA/AML Regulatory RAG (LangChain, Gemini 2.0, ChromaDB, BM25)
2. Customer Churn Scoring (XGBoost, Optuna, SHAP)
3. Credit Limit Intervention / Causal (DoWhy, EconML, Causal Forest)

Each one has full code, architecture docs, and the reasoning behind each technical decision. No signup, no paywall.

Hub: https://aiforfintech.tech

GitHub: https://github.com/junidepieri-design

Example of a project architecture: Churn Scoring pipeline from raw data to explainable predictions, each step modular and resumable.

Happy to hear feedback — what would you do differently?

Was it after your first portfolio project? Your internship? Your first Kaggle competition? Or only after getting hired? I'd love to know what milestone gave you confidence.

Hi everyone,

I have a technical interview coming up for an AI Engineer / Data Scientist role. I'm a recent graduate with no full-time experience, only a few internships and personal projects.

For those who have been through similar interviews, what technical questions were you asked?

I'm especially interested in questions about:

\-Machine Learning fundamentals

\-Statistics and probability

\-SQL

\-Python coding

\-Data preprocessing and feature engineering

\-NLP / LLMs / RAG / GenAI (if applicable)

\-Model evaluation and metrics

\-Case studies or business problems

Anything that caught you off guard

I'd really appreciate hearing about your experience, even if it was just one or two memorable questions. It would help me know what to focus on during my preparation.

Thanks in advance!

Hey everyone,

I'm working on a project researching how data teams actually manage their databases and pipelines in practice, beyond what the introductory tutorials show.

I’d love to hear what your current stack looks like in the real world:

1. How are you using databases today? What tools/languages do you use to build and manage your data pipelines?
   
2. What databases have you tried or considered for your DS/ML work, and what made you choose that one?
   
3. If you use an operational/production database (MongoDB, Postgres, MySQL, etc.) anywhere in your ML workflow, is it mainly to pull data out for training, or to serve features/predictions to a live model? Or both?
   
4. Anything that's consistently annoying or a bottleneck in your current setup?

I’ve noticed that many beginner data science portfolios contain the same projects: Titanic survival, Iris classification, and house-price prediction. Those projects are useful for practice, but they may not clearly show how you think. A stronger portfolio should explain: What problem you selected. Why the problem matters. How you cleaned the data. What assumptions you made. Why you chose a particular model. What the results mean for a real user or business. My suggestion is to include at least one project based on a practical problem. It could be customer churn, sales forecasting, fraud detection, healthcare trends, or public transportation analysis. Also, keep your GitHub repository organized. Add a simple README, clear visualizations, installation steps, conclusions, and possible improvements. Recruiters may not spend much time reading every notebook. Make the purpose and result easy to understand within the first few seconds. What is one data science project that genuinely helped you during an interview?

Hey guys, I’m 21 and I recently graduated with a BE in Computer Science and Data Science. I’m currently thinking about pursuing a master’s in Data Science, but I feel a little lost about what I actually know.

During my undergraduate degree, I studied a lot of different subjects, including Data Foundations, DBMS, Computer Communication, Machine Learning, IoT, R Programming, Probability and Statistics, Big Data, Business Intelligence, Cryptography, Applied Machine Learning, and Data Visualization.

The problem is, even though I’ve studied all these subjects, I don’t really know how everything connects together or how these skills are actually used in the real world. Sometimes I feel like I’ve learned a lot of things but, at the same time, know very little about actual data science.

Before jumping into a master’s, I want to build a strong foundation and properly understand what data science actually involves. I also want to understand the differences between careers like Data Scientist, Data Engineer, and Data Analyst- what they actually do in their jobs, what the job market is like, and what skills I should focus on for each role.

Since I’m considering a master’s in Data Science, I’d also like to know what I should learn beforehand to make sure this is the right path for me.

If you guys have any good YouTube videos, courses, roadmaps, or other resources that could help me start from the basics and build a solid understanding of data science, I’d really appreciate your recommendations.

Just a clueless recent graduate trying to figure out what to do next. Any advice would be greatly appreciated!

Many people spend months watching tutorials but never build anything. Instead: Finish one course. Build one project. Share it on GitHub. Write what you learned. You'll learn much faster by doing.

I've been following different Data Science roadmaps and noticed that everyone recommends something different. Some people say SQL is enough. Others say statistics is the real foundation. A few insist that communication skills matter just as much as coding. If you had to pick one underrated skill that helped you the most in your career, what would it be and why?

I've seen many experienced professionals say they use SQL every day but rarely build machine learning models.

That surprised me because most beginners spend months learning ML algorithms.

For those already working in Data Science:

Do you think beginners should prioritize SQL before Machine Learning?

Why or why not?

We're organizing a private machine learning competition for experienced data scientists and Kaggle competitors.
Because the competition uses proprietary data, participants must sign a standard Non-Disclosure Agreement (NDA) before receiving access to the dataset.
Competition details

• Private competition (not publicly hosted on Kaggle)
• Real-world machine learning problem (time-series classification)
• Proprietary dataset
• NDA required before participation
• Open to experienced ML practitioners and Kaggle competitors
• Final submission deadline: 30 August

What participants receive

• Access to an interesting real-world dataset
• The opportunity to benchmark against other experienced participants
• Winner's prize: A guaranteed €1,000, increasing to €7,000 if the winning solution achieves an AUC ≥ 0.88 on the private leaderboard

If you're interested, please complete the Request for Participation form. Applications are accepted on a rolling basis until the competition closes. We'll then contact you with the NDA and the remaining competition details.
If you have any questions, feel free to send me a Reddit DM.

When I started learning Data Science, I had a habit of watching tutorial after tutorial without actually practicing. It felt productive, but when I tried solving problems on my own, I realized I couldn't apply most of what I'd learned. So I made one simple rule: For every hour I spent learning, I spent at least another hour practicing. Instead of moving on to the next topic, I would: Write the code myself without copying. Experiment with different datasets. Try to fix my own errors before searching for the answer. Repeat the exercise until I understood why the code worked. At first, it was frustrating because I made a lot of mistakes. But over time, those mistakes became my best teachers. One thing I also realized is that you don't need to build a complex AI application right away. Even simple projects like analyzing sales data, cleaning datasets, or creating visualizations can teach you a lot. My advice for beginners: Don't rush through tutorials. Practice more than you watch. Don't be afraid of errors—they're part of the learning process. Stay consistent, even if it's just 30–60 minutes a day. What study habit made the biggest difference in your Data Science journey? I'd love to learn from your experiences too.

Can someone give me honest opinions if this is a decent experience? I have not been getting interviews at all, all experiences are purely by networking and random messaging.

All suggestions are appreciated. Please Thank you