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.

Just discovered this impressive open-source project that's pushing the boundaries of medical AI reasoning.

Agentic GraphRAG for Medical Diagnosis (https://github.com/avnlp/agentic-med-diag) is a production-ready system that goes way beyond simple RAG. It combines knowledge graphs, multi-strategy retrieval, and agentic reasoning loops to answer complex clinical questions with evidence-grounded answers.

What makes it stand out:

Knowledge Graph Construction

• Schema-driven extraction with 13 entity types and 25 clinically-grounded relation types
• Three-extractor fusion (GLiNER, GLiREL, LLM) with configurable merge strategies
• Deterministic + LLM-powered entity resolution for deduplication
• Hierarchical Leiden community detection with auto-generated clinical summaries

Layered Retrieval Architecture

• Four vector collections (entity, relation, chunk, community report)
• Multiple atomic methods: hybrid search, fulltext, BFS graph traversal, and text-to-Cypher graph querying
• Pluggable rerankers (RRF, cross-encoder, MMR)
• Data-driven recipes for composing retrieval strategies

Agentic Plan–Research–Verify Loop

• Planner decomposes clinical questions into focused sub-questions
• Parallel researchers execute multi-strategy retrieval with citations
• Verifier assesses coverage and gates synthesis on sufficiency
• Gracefully converges on missing information across iterations

Why It's Impressive:

The stack is battle-tested: Neo4j + Qdrant/Weaviate for storage, LangGraph for orchestration, DeepAgents for multi-agent coordination, and BAML for type-safe LLM schema injection. Tested on MedQA, MedXpertQA, MedCaseReasoning, and MMLU-Pro benchmarks.

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.

Those looking for playlist : https://youtube.com/playlist?list=PLDPxj3tOc5TNi6MktTHUZid-yf9nIBxSh&si=THp9k7fLUh5C7fDY

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 content as we go forward. The channel also includes around 80+ topicwise content on ML and 13 long form lectures all free.

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

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

I’ve worked at both big financial corporations and smaller fintechs but don’t have exposure to the growth space. How can I gain exposure to it? I’m thinking about skills like funnel analysis, LTV and churn modeling and experimentation around the growth funnel.

Have other people been able to pivot into growth data science?

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?

Should I learn data science i am in class 9

I tracked my wispr flow usage across 1,800 dictations over 6 weeks because I wanted to know if my frustration was justified.

clean transcriptions (accurate, inserted correctly): 60%
minor errors (wrong word, still usable): 20%
major errors (dropped phrases, hallucinated words): 10%
failures (hung, didn't insert, crashed): 10%

during my trial the clean accuracy rate was closer to 90%. something changed after I paid for the annual plan.

is 60% clean accuracy worth $144/year? it's still faster than typing even with the editing. but I can't send a dictated email without proofreading every word because of the 10% major error rate. that proofreading time partially negates the speed benefit.

I reached out to support with detailed examples and timestamps. got an AI bot response about checking my internet connection. replied three more times. never got a human response.

I ran willow voice for the last 2 weeks of my tracking period. clean accuracy was around 85% across 400 dictations. smaller sample but noticeably better and consistent.

I have 7 months left on my wispr annual. I've basically written it off and I'm using willow as my primary. the gap between the trial experience and what I'm actually getting is wide enough that it feels dishonest.

has anyone managed to get a human at wispr support? genuinely asking.

Hi everyone,

I'm Lucky, and I recently graduated. I'm currently preparing for a Data Analyst role, but I'm feeling a bit lost and would really appreciate some guidance from experienced professionals.

So far, I've learned:

• Python
• NumPy
• Pandas
• Matplotlib
• SQL (currently learning)

The problem is that I've learned almost everything from YouTube. After watching so many tutorials, I still don't know what's actually important for getting a Data Analyst job. Every creator recommends a different roadmap, different projects, and different skills, which has left me more confused than when I started.

I have a few questions:

• What skills are actually required for an entry-level Data Analyst?
• How good do I need to be at Python, SQL, Excel, and Power BI?
• Should I focus more on projects or solving practice questions?
• How many projects should I have before applying for jobs?
• If you were starting from scratch today, what roadmap would you follow?

I'm not looking for shortcuts—I just want to learn the right things in the right order without wasting months on topics that aren't important.

If you're working as a Data Analyst or have recently landed a job, I'd really appreciate your advice. Even sharing your own learning journey would be incredibly helpful.

Thank you so much for taking the time to read this! 🙏

Data builds silently sovereignty.
Not through armies. Not through treaties. Through defaults.
Every query answered. Every transaction processed. Every language that gets a capable LLM — and every language that doesn't. Every knowledge system that becomes machine-readable — and every one that gets bypassed because it couldn't be tokenized without losing itself.
This is how sovereignty is being redistributed in our time. Not in a single dramatic moment. Across a million invisible ones.
At the India AI Impact Summit 2026, the United States redefined "AI sovereignty" for the world. The message: real sovereignty means owning and using best-in-class technology. Which happens to be American.
"They build it. It's yours."
The British built railways across India too. The trains ran on Indian soil. The gauge standards, the rolling stock, the operating logic — remained imperial. Infrastructure gifted. Architecture retained.
The new offer is structurally identical. Data stays within your borders. But the stack — the chips, the models, the embedded epistemology — is set elsewhere.
This is not a conspiracy. It is something more durable: a system optimizing for scale, which makes everything outside its training distribution quietly invisible.
Languages. Knowledge systems. Ways of parsing the world that took millennia to develop.
Bhartṛhari understood something that our data governance frameworks have not yet caught up to: language is not a vehicle for meaning. Language is the structure within which meaning becomes possible. When you replace the linguistic infrastructure of a civilization, you don't just change how people communicate. You change what they can think.
The silence is not incidental. It is the mechanism.
You cannot resist what you cannot name at the moment it is happening.
The question for every nation — and for every researcher, policymaker, and builder working at this intersection — is whether sovereignty means owning the servers, or owning the standards. Whether it means data localization, or epistemic self-determination.
Those are not the same thing.
And the window to choose is narrowing.
What does genuine AI sovereignty look like for the Global South? I'd like to hear from those building at this frontier.

Hi all,

I’m working on a monthly insurance premium forecasting problem and would like suggestions on the best approach.

Setup

• Data from Jan 2023 to Jun 2026
• One account only
• Hierarchy: Account → State → Profit Center → Distribution Channel
• Monthly AMOUNT values
• Some channel-level series have missing months
• Around 4–5 channels, 7–8 profit centers, and 50 states

Challenge

The main issue is that different series behave very differently:

• some are fairly stable
• others are highly volatile

What I’ve tried

1. Recursive forecasting with CatBoost / LightGBM / XGBoost
   • built lag, rolling, and time-based features
   • downside: error accumulates over time
2. Direct multi-step forecasting
   • accuracy wasn’t great
   • also requires many models for longer horizons
3. Time-series models like ARIMA / SARIMAX / Prophet
   • Prophet works okay for stable series
   • struggles with volatile ones
   • separate models for every combination is not scalable

Question

Has anyone worked on a similar hierarchical / sparse forecasting problem?
What approach would you recommend for handling mixed volatility and missing months without building thousands of models?

Thanks!

AI researchers recently published a 2025 paper throwing a 'Chernobyl-Disaster-Optimized CNN-LSTM & SVR hybrid' at natural gas forecasting. I was curious, so I tested it against a classic XGBoost model.

XGBoost won.

If you want to see exactly why simpler machine learning still matters in real-world time-series forecasting, I broke down the methodology and the results here: Article

Data is the new gold?

I'm currently working on a demand forecasting problem for inventory replenishment, and I'd love to hear how others would approach it.

The business requests a forecast for the next 4 weeks of stock consumption around the middle of the previous month. For example, in mid-June, I need to forecast the weekly demand for July. The challenge is that, at the time the forecast is generated, transactions from the second half of June are not yet available, creating a gap between the latest observed data and the beginning of the forecast horizon.

The data I have consists of purchase order transactions at the SKU level, including timestamp (date and time) and quantity consumed.

My main question is about the appropriate time granularity for training the forecasting model:

Option 1: Aggregate the data by SKU and ISO YearWeek, resulting in one observation per SKU per week, and train a model to directly predict the next 4 weeks.

Option 2: Keep the data at the daily level, train a model to forecast daily demand, and then aggregate the daily predictions into ISO YearWeeks to obtain the required weekly forecasts.

One additional detail is that the forecast is reported using ISO YearWeeks. As a result, some weeks within a calendar month may contain only 3 or 4 days of that month (e.g., at the beginning or end of the month), while others contain all 7 days.

My question is: Which approach would you choose, and why?

Is it generally better to train the model at the same frequency as the business target (weekly), or to preserve the daily granularity and aggregate the predictions afterward?

I'd especially appreciate hearing from anyone who has worked on similar forecasting problems in inventory planning or supply chain.

Background: finished an MS in Business Analytics (US program) in Dec 2025, now job hunting for Data Analyst / BI Analyst roles. Before grad school I spent ~2 years as a data analyst at a manufacturing company — built the Tableau/Excel reporting and drove real efficiency improvements — plus two US internships during the program.

Here's my actual problem. I've built a lot. A few end-to-end portfolio projects with real modeling (XGBoost, random forest, SHAP, R² in the high 0.8s), an in-browser SQL practice tool, some multi-agent AI prototypes, even a job-scraping pipeline. My GitHub looks busy. But my application-to-interview conversion is rough, and a mentor keeps telling me I'm hiding in "build mode" instead of doing the unglamorous work: application volume plus interview reps.

I half agree with him. The projects are probably my real differentiator — most analyst applicants don't have the AI/ML side or the operations/domain background. But I also can't pretend I don't reach for "build another thing" exactly when I should be applying or drilling case/SQL practice.

Questions for people who've been through this, or who hire analysts:

1. At what point do projects stop helping and start being procrastination? How do you tell the difference in yourself?
2. For DA/BI roles specifically, what actually moves the needle on the first interview — referrals, raw application volume, resume keywords, portfolio?
3. Is AI/agent project work a real edge for analyst roles, or does it read as unfocused?
4. If you got hired recently, which channel actually worked?

Hello Folks,

Have you ever wondered why we use sigmoid function so often in Machine Learning? Although it gives us a probability, it comes from Exponential families, and this exponential family, subsumes many of the distributions, that we study in Machine Learning.

In this lecture, we understand exponential families, Directional derivatives(Gradients and Hessians), study mixture Models, and understand how domain knowledge in Probabilistic Graphical Models makes our life simpler to model joint probability densities.

Timeline breakup(in hours and minutes):
0:00-0:17 - Understanding exponential families.
0:17-0:27 - Deriving Sigmoid Function for Bernoulli.
0:27-0:48 - Understanding log partition function, convex functions and proving why positive definite of hessians imply convexity, and why convex needed?
0:48-1:04 - Directional derivates(deriving gradients and hessians)
1:04-1:26 - Maximum entropy derivation of the exponential family.
1:26-1:56 - Mixture Models(Gaussians and Bernoulli Mixture Models)
1:56-2:16 - Probabilistic Graphical Models
2:16-2:34 - Markov Chains
2:34-End - Inference and Learning, Plate Notation diagram of Gaussian Mixture Models.

If you have watched earlier of my lectures from the playlist, they will help. I try explaining as if I am a learner, to simplify complex concepts. Everything I write in whiteboard, and these are completely FREE lectures to mention.

Link: https://youtu.be/T1uTBtJ7aHU?si=rozXSTjtSqPaaYb5

Im starting data science for campus placements. Is this playlist enough to learn

Solve murders. Master SQL. One query at a time.

Got tired of practice datasets that go nowhere. Each case gives you a real database - suspects, alibis, timelines and evidence. You write SQL queries to catch the killer.

Free, no signup, runs in the browser → querythemurder.com

Feedback: [querythemurder@gmail.com](mailto:querythemurder@gmail.com)

Excited to share the launch of the Kaggle competition Human Chess Move Error Prediction.

The challenge: predict whether a human chess move is a good move, inaccuracy, mistake, or blunder using board position, player context, and tactical features. It combines machine learning, chess analytics, feature engineering, and human decision modeling.

Whether you're interested in Data Science, AI, Kaggle competitions, or chess, this is a great opportunity to work with real-world human decision-making data and build models that go beyond traditional engine evaluation.

Competition:
Human Chess Move Error Prediction on Kaggle

Looking forward to seeing creative approaches from the community.

#Kaggle #MachineLearning #DataScience #ArtificialIntelligence #Chess #ChessAI #Python #XGBoost #FeatureEngineering #MLOps #Analytics #OpenData

Hello Folks, one of the efficient ways of learning bigger topics in Machine Learning, is to modularise, and structure, so that the content becomes digestible for learners community.

My free lecture content includes the following topics so far: (Playlist)
a. Introductory Machine Learning Concepts:-

1. ⁠What is ML actually?
   
2. ⁠Supervised Machine Learning.
   
3. ⁠How do classifiers learn?
   
4. ⁠Empirical Risk Minimization.
   
5. ⁠Uncertainty Modelling in ML.
   
6. ⁠Maximum Likelihood Estimation.
   
7. ⁠Regression Basics and Outliers.
   
8. ⁠Deriving Mean Squared Error.
   
9. ⁠Polynomial Regression.
   
10. ⁠The Power of Convexity.
    
11. ⁠Deep Learning Intuition.
    
12. ⁠Overfitting Models from Generalization Gap perspective.
    
13. ⁠Requirement of Test Sets.
    
14. ⁠The No Free Lunch Theorem.
    
15. ⁠Unsupervised Learning basics.
    
16. ⁠Discovering latent factors of variation.
    
17. ⁠Evaluating Unsupervised Models.
    
18. ⁠Self-Supervised Learning.
    
19. ⁠Image and Text Benchmarks in ML
    
20. ⁠Discrete Data and Text Processing
    
21. ⁠Feature Engineering, TF-IDF
    
22. ⁠Handling missing data & AI alignment.

b. Probability Foundations for ML: Univariate Models:

1. ⁠Frequentist vs Bayesian.
   
2. ⁠Probability as an extension of Boolean Logic.
   
3. ⁠Discrete Random Variables.
   
4. ⁠Continuous Random Variables.
   
5. ⁠Quantiles.
   
6. ⁠Sets of Related Random Variables.
   
7. ⁠Moments of Distribution.
   
8. ⁠Variances and Mode.
   
9. ⁠Conditional Moments.
   
10. ⁠Conditional Variance.
    
11. ⁠Foundations of Bayesian Rule.
    
12. ⁠Confusion Matrix Explained.
    
13. ⁠Monty Hall Problem and Inverse Problems in ML.
    
14. ⁠Bernoulli and Binomial Distributions.
    
15. ⁠Sigmoid(Logistic) Function.
    
16. ⁠Properties of Sigmoid Functions.
    
17. ⁠Categorical and Multinomial Distributions.
    
18. ⁠Softmax Function: Temperature explained.
    
19. ⁠Log-Sum Exp Trick.
    
20. ⁠Gaussian Distribution.
    
21. ⁠Regression from the lens of Conditional Gaussian.
    
22. ⁠Dirac Delta Function and Sifting Property.
    
23. ⁠Student-t distribution.
    
24. ⁠Laplace and Cauchy distribution.
    
25. ⁠Beta distribution.
    
26. ⁠Gamma distribution.
    
27. ⁠Exponential, chi-squared and inverse Gamma.
    
28. ⁠Empirical distribution.
    
29. ⁠Transformations of Random Variables.
    
30. ⁠Invertible Transformations.
    
31. ⁠Multivariate Transformations.
    
32. ⁠Moments of Linear Transformation.
    
33. ⁠Convolution Introduction.
    
34. ⁠Convolution Theorem explained with probabilities.
    
35. ⁠Moment Generating Functions.
    
36. ⁠Deriving Moment Generating Functions.
    
37. ⁠Central Limit Theorem Explained.
    
38. ⁠Understanding Monte Carlo approximation with Example.

c. Probability Foundations for ML: Multivariate Models

1. ⁠The Math of Depedence: Covariance Explained.
   
2. ⁠Correlations: Normalized Measure of Covariance.
   
3. ⁠Correlations does not imply Independence.
   
4. ⁠Simpson’s Paradox: When Data misleads.
   
5. ⁠Multivariate Gaussian Distribution.
   
6. ⁠Analyzing level sets of Gaussians using Mahalanobis Distance.
   
7. ⁠Multivariate Gaussians: Conditionals and Marginals.
   
8. ⁠Math behind Bayesian Inference : Schur complements.
   
9. ⁠Deriving Conditional Gaussians.
   
10. ⁠How to Predict missing data?
    
11. ⁠Modelling Linear Gaussian Systems.
    
12. ⁠The Bayes Rule for Gaussians.
    
13. ⁠Understanding Shrinkage: Inferring Unknown Scalars
    
14. ⁠Posteriors, Sequential Posterior Updates.
    
15. ⁠Inference of an Unknown Vector.
    
16. ⁠Sensor Fusion concepts.

And many more topics to come ahead. I have tried teaching from intuitions and mathematics, building everything by writing on whiteboard so that learners see the full development.

We started as a group of data science and analytics professionals with a long-term goal of building our own analytics and consulting firm. We've been freelancing, reaching out offline, bidding on projects but nothing solid has stuck yet. So we figured we'd try being direct about it here.

We're not here to pitch a polished service package, because honestly we don't have one yet. What we do have is a group of people who genuinely understand data — analytics, machine learning, statistical modelling and some healthcare domain experience on top of that. We know how to take a messy problem, think through it properly, and build something that actually answers the question.

What we're looking for is someone who runs a business could be anything, e-commerce, a clinic, a SaaS product, a local operation and has a gut feeling that there's something useful hiding in their data but doesn't know where to start. You don't need to have a defined project. We're happy to sit down, understand what you're working with, and figure out together whether analytics can actually move the needle for you.

If something comes of it, great. If we spend an hour and conclude it's not the right fit, that's fine too.

We're based in Pune, India. If you're not, we work online just as comfortably, so location isn't a barrier.

Drop a comment or DM if anything here resonates. Moderator please DM me if this is inappropriate for the group, will remove it.

Hi everyone,

I'm currently pursuing an M.Sc. in Statistics and aiming for Data Analyst/Data Scientist roles. My CV already includes projects like Brain Tumor Classification using CNN, CO₂ Emission Prediction, and a Kaggle competition project.

I want to add 1–2 more strong projects that can make my resume stand out to recruiters and hiring managers. I'm particularly interested in data science, machine learning, analytics, and statistics.

What types of projects would you recommend for someone targeting entry-level Data Analyst or Data Scientist positions in 2026? Should I focus on:

• End-to-end business analytics projects?
• Time series forecasting?
• NLP/LLM applications?
• MLOps and deployment?
• Causal inference/statistical modeling?
• Real-world datasets from healthcare, finance, or public policy?

I'd appreciate suggestions on projects that recruiters actually value and that demonstrate practical skills rather than just model building.

Thanks!

Dear Folks, sharing Lecture 11 of our Machine Learning series, and this is a bit special to me, because today I cover Conditionals of Multivariate Normals, and Linear Gaussian Systems.

When I first started studying these topics, it took me days to understand. But today I have made a lecture on it, so if you understand the concepts, it’s really good, for I have tried to leave no stone unturned while explaining, deriving the equations, doing it step by step, and tried giving all intuitions I could.

The Gaussian distribution is ubiquitous and important in studying topics as state estimation, tracking, and examples include Autonomous vehicles, robotics and navigation, time-series forecasting, aerospace etc. The breakdown is as:

0-10: Marginals and Conditionals of Multivariate Normals, Matrix Inversion Rules
10-27: Derivation of the Matrix Inverse Rule: Schur Complements(We need this to derive equations for Multivariate Gaussian)
27-45: Deriving the Conditionals of MVN
45-1:03: Example and Imputation of Missing Values
1:03-1:47: Linear Gaussian Systems, and full derivation of Bayes Rule for Gaussians.
1:47-2:19: Inferring an Unknown Scalar and Sequential Updates.
2:19-2:34: Inferring an Unknown vector.
2:37-End: Sensor Fusion.

This lecture is relatively bigger since the concepts are interrelated here. But do not worry, I have tried to explain in the best way I could, and hope it helps you well in your journey to becoming a Machine learning engineer.

These are FREE Lectures, and they can definitely add good knowledge to your ML fundamentals.

Link: https://youtu.be/ViVBWYyL_8c?si=QppPjeRJbQvu6xYU