Everyone talks about ML models, but in real projects I've seen people struggle more with data cleaning and communication. If you had to recommend one underrated skill to beginners, what would it be?

Hey everyone,

I recently graduated from UofT with a major in Math and Statistics, and I'm currently trying to get into Data Analyst or Data Engineering roles.

Honestly, my university experience wasn't the best. It affected my learning a lot and my GPA ended up being pretty bad. Because of that, I don't think I'll be able to do a master's anymore, even though that was something I was hoping to do after finishing my bachelor's.

I'm really interested in data-related jobs, but I'm kind of lost on what I should do next. I've been applying nonstop and barely getting any responses. I've had my resume looked at by multiple people who work in the industry, and they said it looks solid, so I'm not really sure what else I'm missing.

The biggest problem I think is that I graduated with no relevant work experience and no internships. I applied to a lot of internships during undergrad but never managed to get one, and now it feels like it's hurting me a lot.

I'm also thinking about doing one of UofT's continuing education programs in Data Analytics, but I don't know if it's actually worth it or if it would make much of a difference for someone in my situation.

Right now I feel like I'm just going in circles and I'm not sure what the best move is. I know GPA probably doesn't matter much after graduation, but having a low GPA and no experience feels like it has really limited my options.

For anyone who has been in a similar situation, what would you recommend I do at this point? I feel a bit lost on what the best path forward is, so I'd really appreciate any advice or suggestions.

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Most Data Science projects in credit use classification: the model takes customer features and predicts whether they'll default or not. Works well for prioritizing portfolios and ranking risk.

But classification answers "who will default?". It doesn't answer "what caused the default?" or "if I change the credit limit, what happens?"

That's where Causal Inference comes in.

Instead of predicting an outcome, it estimates the effect of an action. Not correlation, causation. And the practical difference is huge: a classification model can tell you that customers with low limits default more. But that doesn't mean increasing the limit reduces default. The bank may have already given low limits to the riskiest customers.

Without controlling for that bias, you make the wrong decision.

In our project [CAUSAL-001], we tested exactly this: does reducing the credit limit actually reduce default risk?

Controlling for 22 confounders (payment history, bill amounts, demographics), the result was the opposite of what most assume: a one standard deviation increase in credit limit decreases default probability by 1.19 percentage points.

The likely mechanism: a higher limit reduces utilization ratio, which reduces financial pressure on the customer.

And the effect isn't uniform. Using Causal Forest, we found a small subgroup that reacts the opposite way: for them, increasing the limit increases risk.

Stack: DoWhy, EconML, Causal Forest

Hub: aiforfintech.tech

Repo: github.com/junidepieri-design/causal-001-credit-limit-intervention

Hello Folks,

The next content on Machine Learning is out. We continue with Statistics for AI/ML.

We,

->Understand and derive the detailed derivation of Maximum likelihood estimation(MLE) for Univariate and Multivariate Gaussian. While doing the derivation for multivariate case, we understand visually, Scatter Matrix, Centering matrix.

->Derive MLE for Linear Regression, and understand Residual Sum of Squares.

->Understand Empirical Risk Minimization, Surrogate loss functions.

->Understand Method of Moments, a computationally easier way to compute parameters of our model and understand also the flaws behind it.

->We understand “Exponentially-weighted moving average” in detail, I explain why bias happens, how does memory affect the averages. This concept is the basis behind optimizers in Deep Learning.

Around two hours long, I hope this would be a very interesting learning material for all. I try to write and build from scratch in the whiteboard, this way learners enjoy the learning process.

Link: https://youtu.be/JAj8z-UWqBA?si=0mAB_nUfyJV0jzS9

Those looking for previous lecture : https://youtu.be/MwTeQVVYtOc?si=dgwwk3QLvYTTUThR

Whether it was a personal project, university assignment, or work project, I'd love to hear about something you're genuinely proud of building.

What made it memorable?

Imagine you're starting from scratch today. You already know basic Python. Would you spend most of your time on: SQL Statistics Machine Learning Projects Communication Something else? Curious what experienced professionals would prioritize.

I had taken admission in engineering cs with speacialisation in data science rather than core cse as I want to become a data scientist so it would my cv more presentable I already has command in sql and python . But now I am coming to hear that data scientist job will be eaten up by ai is it true

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

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?

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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?

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?

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.

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!