Which certifications would you recommend someone getting into Data Science to get? Also kindly share any tips that worked and helped you to progress in data.

I’m applying to a data science program and wanted to do a small, unconventional project for my application.

My original idea was to use the program itself as a case study, combining social media engagement, follower data, information session attendance and university curricula to estimate the potential applicant pool and its characteristics without access to actual application data.

I started working on it but realized that some of the data-linkage methods could conflict with my country’s data protection laws.

Was the original idea even worth pursuing? What other projects could demonstrate similar analytical and technical skills?

I’m particularly interested in combining unconventional datasets to quantify things that aren’t easily measurable, rather than doing a generic ML project.

Hi r/datasets,

Before collecting or curating anything, I want to know what India-specific data people actually need.

If you've looked for India-related data and couldn't find it (or it was too small, outdated or badly licensed), tell me:

1. What data-were you looking for?

2. What for? (model training, research, a product, analysis)

3. Type, scale and labels (e.g., 500 hours of Kannada speech with transcripts)

Even a one-line reply helps. Thanks!

Title: Microsoft Fabric's Data Days: AI @ Work is happening! (Nov 2 – Dec 15)

I recently discovered an ongoing Microsoft Fabric community initiative called Data Days: AI @ Work.

It's a series of community-led online sessions focused on Microsoft Fabric, Power BI, SQL and AI.

Some interesting upcoming sessions include:

* November 15: Blogging Your Way Into the Fabric Community.

* November 18: GIS Day 2026 – Where Data Insights Are Mapped (Fabric x Esri).

* November 19: Microsoft Fabric Masterclass: Build Intelligent Analytics Solutions.

* November 19: DP-700 – Microsoft Fabric Data Engineer Associate session.

What makes this interesting is the focus on real-world projects, practical demonstrations and learning from the community.

I think this could be particularly useful for students and beginners interested in data engineering, analytics and AI.

Event schedule and registration:

Explore Data Days: AI @ Work

Is anyone here planning to attend any of these sessions? Which topics are you most interested in?

Start with NumPy for numerical operations, Pandas for data manipulation, Matplotlib and Seaborn for visualization, and Scikit-learn for Machine Learning. Later, explore TensorFlow or PyTorch for Deep Learning

Hi everyone, I’m currently a sophomore in Cs at college and I want to do some sort of data engineering internship or data engineering job in fin tech. I’m trying to teach myself python and build a ML model w AI and train it/study it so that I can understand it better. I kinda suck at leetcode and I’m not the best coder, I’ve never been to a hackthon( I’m trying to get into one) even though I’ve been coding since sophomore year of high school. I’ve done projects as well using HTML/CSS/Javascriot

I just wanted to give a backdrop of just whether I’m at (it’s ok to say that I’m a loser and behind, I get it)

How can I get on the track of doing data engineering? Does my interest in machine learning be able to aid me in achieving my goal? What roadmap can I follow?

Or should I just drop out lol??

Any advice or insight is appreciated!!

If you're building a data science portfolio, this might help. I built my startup: AI-Atlas that reads your public GitHub and tells you which projects count as data science or ML work, and why, pointing to the actual files.

What tends to make a DS project stronger:
- A baseline and a comparison, not just one model.
- Results written in the README, not only inside the notebook.
- Notebooks committed with their outputs.
- Honest evaluation: cross-validation, a held-out set, a confusion matrix.

If it finds nothing yet, it suggests three first projects in the language you already use. Free and no account needed , at getaiatlas.com

I'm a 17 year old student building this solo. Feedback from people learning right now is the most useful kind :)

An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

Hello everyone, Andrew here!

I've spent over 15 years in Data Science & AI, including time at Amazon & Sony PlayStation.

I've also spent a huge amount of time on the hiring side, interviewing & screening thousands of candidates (I published a book on this topic...never again!)

These days I run Data Science Infinity, an online training program for people breaking into the field. I've now mentored over 3,000 students and love helping people navigate the field.

But enough about that...happy to answer anything about learning Data Science, AI, career changes, the job market, my career, salaries, getting hired, portfolios, interviews, working in the industry, or whatever else you're curious about.

I'll be answering questions on Friday 13th November, between 2pm and 4pm UK time (GMT).

Ask me anything! :)

Hey r/learndatascience! I’m Tabs, a legal engineer at Isaacus. I've been a software engineer for a long time and started dipping into data science this year.

I built an open-source legal conflict detector that finds deviations and gaps between pairs of legal documents using only encoder models and natural language inference. 

Inspired by the explosion of interest in discriminative models like TypeSafe AI’s Jev, I wanted to see how far I could get using smaller, non-generative models for the bulk of the legal document playbook review workflow. 

The theoretical benefits of a non-generative approach to this problem are enormous. As true clause-level pairwise review is a quadratic problem, it’s unfeasible to do at scale using a model like Opus or Astra. Comparing two average-sized documents with 133 clauses each would take 17,689 API calls, which is far too slow and expensive. 

Simply uploading the files directly to Claude or GPT is no remedy either. Decoders and agentic architectures don’t have fixed attention that lets them thoroughly analyse a document in one shot. Even with a multi-shot approach, they’re weak on recall and routinely hallucinate. 

My lawyer friends raise this problem constantly, and I couldn’t find an open-source solution to it, so I decided to build one myself. 

My pipeline pushes the bulk of the NLI, entailment and reasoning onto faster and more accurate legal encoder models. For the longest document pairs I sampled, the full pipeline ran in under 20 seconds. Unlike generative architectures, these models run a comprehensive pairwise comparison, and although they can still make false positive or false negative errors, those misses and misclassifications aren’t hallucinations (out-of-domain results introduced into the feature set). 

The pipeline works as follows: 

1. Enrich: we enrich each document into a hierarchical tree of sections, clauses and subclauses. Clauses are the key atoms that the pairwise comparisons run on downstream. 

2. Embed: we embed each clause as a vector. 

3. Filter: we use a legal zero-shot classifier to drop items that aren’t provisions, like footnotes or signature blocks. 

4. Shortlist: we keep each clause’s three most relevant pairs from the reference document, as long as their cosine similarity is at least 0.5. 

5. Score: we use a reranker and a classifier to score each shortlisted pair. Each answers a question in the format it’s built for: the reranker scores how relevant text is to a search query (relevance), and the classifier scores whether a statement is true (NLI/entailment). 

6. Sort: if a clause and its best match are on the same topic but have a high conflict score, it’s a deviation. If there’s no same-topic match, it’s a gap, i.e. the topic is in one document but not the other. 

That’s the whole conflict detection pipeline. On top of it, you can optionally plug in an LLM to summarise each finding in plain English or redraft a clause to favour the party you’re representing. Because the generative step only runs after the NLI and encoder stack has already decided what the conflicts are, it can’t hallucinate a conflict into existence. 

To demonstrate the pipeline’s power, we inputted Bob Iger’s 2011 and 2022 Disney employment agreements. The pipeline found many disparities and conflicts, and across most of them, the 2011 agreement was more favourable to Iger than the 2022 one. 

One interesting finding was a gap: the 2011 agreement had no intellectual property assignment clause, while the 2022 agreement makes Iger assign all work-related IP to Disney, with an exception for inventions he makes on his own time without company resources. 

The code is MIT-licensed, so it’s free for personal and commercial use. The guide walks through every step, so you can swap in your own models or make your own architectural changes wherever you like. 

Have fun, and let me know what you think! 😁

Write-up and static demo: https://isaacus.com/blog/build-a-legal-document-conflict-detector 
GitHub repo: https://github.com/isaacus-dev/cookbooks 

My suggestion: Build strong foundations in Python, SQL, Statistics, and Machine Learning first. Then gradually explore Generative AI, cloud computing, and MLOps. Combining practical projects with these skills can help Data Scientists remain adaptable as the industry changes.

Hi guys i have crafted a study plan for myself for this year and next year. What do u think of this study plan?

Things to do and learn in order of priority.

1. Core Data analytics/Scientist work, learn the entire Data pipeline process on python and SQL including Data extraction, preprocessing, EDA, machine learning models, etl, data pipelines, data warehouse concepts and cloud platforms.

2. Exploring agentic AI to automate the Analytics and Data Science/ML workflow. Including things like RAG and Autonomous Workflow Automation.

3. Full end to end ML pipeline and cycle, including deploying ML models to cloud and monitoring models for drift.

4. Deep Learning concepts.

5. Read more about LLM and how they work, including things like transformers, Attention Score etc.

Hi everyone!

I'm from National Tsing Hua University in Taiwan, and I'd like to share a beginner-friendly online course we offer on FutureLearn.

Data Science for Business Analytics: Introduction to Computing

The course introduces R programming, basic statistics, data visualisation, simulations, and bootstrapping through practical activities.

• 3 weeks (about 5 hours/week)

• Fully online and taught in English

• No prior programming experience required

• Free to learn with limited access (optional paid upgrade)

If you're just getting started with data science or want to build a foundation in R, this might be a useful resource.

Course link:
https://www.futurelearn.com/courses/data-science-for-business-analytics-introduction-to-computing

Hi everyone,

I’m a 2nd-year engineering student (focusing on data engineering / data science) taking a statistical learning course. It covers loss functions, risk, bias-variance decomposition, cross-validation, linear models, SVM, kNN, and so on, and it follows ISL / Elements of Statistical Learning.

I’m looking for YouTube channels, courses, or sites that combine both of these:

Mathematical depth: where the formulas are actually derived and the logic behind each model is explained (why this loss, why this decomposition, what the optimization is doing), not just stated.

Simple, concrete explanations: worked examples and real-world intuition, in the style of Simplilearn or StatQuest, so the math doesn’t stay abstract.

What I’ve already tried: Simplilearn (good for the big picture but too light on math), StatQuest, and Stanford’s ISL lectures. They’re useful, but I haven’t found one resource that does both well for each model.

Ideally I’d like something that:

walks through a derivation and then applies it to a small numeric example,

connects the theory to code (Python/scikit-learn),

is free or cheap.

If you were learning this again from scratch, what would you use? And is there a better way to combine resources (for example, intuition first, then derivation) than what I’m doing?

Thanks!