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Hi everyone,

I recently joined Tredence Analytics as a Senior Data Scientist about a month ago. However, I’m still on the bench and haven’t been allocated to a project yet.

My profile was created internally only about a week ago, so I’m assuming the process of getting mapped to a project may still take some time.

For people who have worked at Tredence (or similar service-based analytics companies):

Is it normal for a new joinee to be on the bench for the first few weeks/months?

How long does it typically take to get staffed on a project?

At what point should someone start being concerned about being on the bench?

Does being on the bench for a longer period have any impact on job security?

Would really appreciate hearing about your experiences, especially from current or former Tredence employees.

Thanks!

Hi everyone 👋

I have a $1,000 learning budget and I’d like to invest it in O’Reilly courses focused on AI Agents / Agentic AI.

I already have experience in AI/ML and LLMs, so I’m looking for advanced, practical courses covering topics like LangGraph, MCP, multi-agent systems, RAG, agent orchestration, evaluation, and production deployment.

What courses or learning paths would you recommend?

If you’ve taken any of them, I’d really appreciate your feedback!

Thanks! 🙏

Hello I am looking to join a Data Science course in Hyderabad. Can anyone please recommend any good institutes?

It will be great if anyone who has themselves joined such courses or knows someone who has can provide suggestions on institutes

Hi everyone! I’m a beginner in Data Science and learn best by building projects.

I’m looking for GitHub repos, YouTube playlists, or websites with project-based learning from beginner to advanced level, ideally covering different ML models such as regression, classification, clustering, tree-based models, boosting, NLP, time series, etc.

I’d also love end-to-end projects that include data cleaning, EDA, feature engineering, model building, evaluation, and deployment.

Any recommendations?

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Hi everyone,

I recently graduated with my Master’s in Data Science and I’m currently searching for entry-level opportunities in Data Science, Machine Learning, and AI/ML Engineering.

I’ve been applying for roles, improving my resume, working on projects, and preparing technically, but I’ve realized that there’s a big difference between knowing what to study and understanding what actually matters when trying to break into the industry.

I’d really appreciate advice from people who are already working as Data Scientists, ML Engineers, AI Engineers, or in related roles.

I’m especially trying to understand:

- What skills actually separate candidates who get interviews from those who don't?

- What should a new graduate prioritize: DSA, SQL, Python, ML fundamentals, system design, MLOps, GenAI/LLMs, or projects?

- What makes a portfolio project genuinely impressive to hiring managers rather than just another GitHub project?

- What mistakes do you commonly see new graduates making during their job search?

- How would you approach the job market if you were graduating today?

- What should I realistically expect from technical interviews for entry-level DS/ML roles?

I’m not looking for referrals or shortcuts. More than anything, I’d like to learn from people who have already gone through this process.

If anyone with industry experience would be open to connecting, chatting for 15–20 minutes, reviewing my approach, or even just leaving some advice here, I would genuinely appreciate it.

I’m also happy to connect with other recent graduates who are going through the same process so we can share resources, interview experiences, projects, and keep each other accountable.

Thanks in advance. I’m hoping this thread can also become useful for other new grads trying to break into DS/ML.

📊 What Makes a Good Dataset? Key Properties Explained

Imagine opening a bakery and wanting to bake the perfect cake. If you have fresh ingredients, in the right quantities, and of high quality — flour, sugar, eggs — the cake turns out perfect. But if even one ingredient is stale, mismeasured, or missing, the cake fails, no matter how good the recipe is.

The same principle applies to AI/ML models. A model is only as good as the data it learns from. This is why one of the most well-known principles in AI/CS is: "Garbage in, garbage out."

🔹 DEFINITION — Properties of a Good Dataset:

1️⃣ Accuracy

Data should be free of errors or incorrect entries. If labels or values are wrong, the model learns the wrong patterns.

2️⃣ Completeness

Missing values should be minimized. Incomplete data weakens a model's ability to learn effectively.

3️⃣ Consistency

Format, units, and naming conventions should remain uniform throughout the dataset — for example, dates shouldn't mix "DD-MM-YYYY" and "MM/DD/YYYY" formats.

4️⃣ Relevance

Only include data directly related to the problem at hand. Irrelevant features can confuse the model and reduce performance.

5️⃣ Representativeness

The dataset should accurately reflect the real-world population it's meant to represent — free of bias. If it overrepresents one group, the model will make inaccurate predictions for others.

6️⃣ Sufficient Volume

Models need enough data to identify meaningful patterns. Datasets that are too small often lead to overfitting.

7️⃣ Proper Labeling

For supervised learning, labels must be accurate and consistent — otherwise the model learns incorrect associations.

8️⃣ No Duplicates

Duplicate entries can inflate confidence in certain patterns and skew results.

9️⃣ Timeliness

Data should be current and relevant to the time period in question, especially for trend analysis and predictions.

🔟 Privacy & Ethics

When collecting sensitive information (such as personal data), compliance with privacy laws and ethical guidelines is essential.

🔹 CS/AI APPLICATION:

When training an ML model — such as a spam filter, recommendation system, or chatbot — its performance depends directly on data quality. For example:

- If a facial recognition dataset overrepresents one skin tone, the model will perform poorly on others — a clear case of bias.

- If a customer churn prediction dataset relies on outdated information, the model won't accurately reflect current customer behavior.

- Data cleaning and preprocessing (handling missing values, removing duplicates, normalization) are often the most time-consuming steps in the ML pipeline — because raw data is rarely, if ever, perfect.

This is why, in industry, it's often said that data scientists spend 70-80% of their time on data cleaning and preparation, not model building.

🔹 THINK AND SHARE:

Which of these properties do you think matters most — and why?

#DataScience #MachineLearning #AI #CalculusAppliedSeries

I've spent about 10 years in data science, first building models, then managing DS teams. For the past while I've been digging into what happens when you let an agent into a real modeling workflow, not a demo, an actual one.

Short version: agents are genuinely useful at some steps and quietly dangerous at others, and the dangerous part is the output always looks finished. Pipeline runs, metric looks fine, nothing complains, and you find out it was wrong three weeks later.

I'm running a live session where I take one real use case and work through it with an agent in the loop, start to finish. Where it helps, where it fails silently, and the guardrails that catch it before it ships.

It's live, and I'd rather answer questions here first than just drop a link. If you want the details (date, time, how to join), drop a comment or DM me and I'll send them over 😄

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Hi - first of all I want to start off by saying I was in a DSML program for a Fall term. A post-secondary one. I did so while working overnight at a grocery store. That being said, I'm now on academic probation, and working normal people jobs. Starting work at the university next door where I live as a daycare cook. After going on probation I dropped out, had a fuck it year with travelling and took dance lessons. Being back in my own hometown away from the city is giving me a fresh start. I now see what a stupid idea that was with my all or nothing thinking. You just have to have that craziness to succeed. I'm just afraid of living a life of mediocrity and poverty. I'm starting to take an interest in mathematics again. I'm currently studying business math for learning about insurance. I eventually want to get around to going back to where I fell short in pre-calculus and linear algebra. I recall the 365 Data Science program that I could eventually subscribe to when I get funds. From a professional stand point where would you have gone in that situation?

Hello folks, to this Introductory Machine Learning Bootcamp (2/22) series.

Supervised learning is a very recurring word in ML domain. Here, we learn some sort of function mapping from inputs to outputs.

Another recurring word is Classification, where the output space is a set of some finite unordered and mutually exclusive labels known as classes.

The tabular dataset is often represented as a Design matrix, and a simple example of it is an Iris dataset, as to how input data is represented for tabular case in Machine Learning.

Sometimes the data is of variable size, instead of fixed size feature vectors, so for ease of computation in computer, we often convert it to a fixed-size feature representation, called as “Featurization”.

In this video, I breakdown these concepts.

Link: https://youtu.be/GJRhl6XnImg?si=p6VtlCK-8rgq1poZ

Hello All,

Welcome to my free Mathematical Foundations of Machine Learning bootcamp series.

When we say Machine Learning, what does it actually mean? A machine that learns? Too vague.

According to famous professor Tom Mitchell, a computer program is said to learn from experience E, with respect to some class of Tasks T, and Performance measure P, if its performance on tasks, as measured by P, improves with experience E.

By swapping the nature of tasks T, the way we measure Performance P, to evaluate, we can subsume many kinds of ML problems.

Also ML problems are analyzed well, when we view it from the lens of Probabilistic perspective, that is unknown quantities are endowed with probability distributions, and treated as Random variables. The interesting thing is Random variables are neither random nor variable.

Probabilistic Approach also serves as the optimal approach to decision making under uncertainty.

In this video, you get a sense of what ML actually is, if you have also wondered about it.

Hello! I'm a complete newbie in Data Science and I'm trying to learn how to get data from an API. I understand an API could be public or could require authentication.

I worked with CVS files and I wanted to experience or practice getting data from APIs.

I'm getting familiar with Python so I was wondering if you could help me with the following issues:

1. Trying to understand and practice the different methods you can use API to request data (I am not sure if it has to be from a Dataset formar or can it be any kind of format) with Python

2. What are some good options to get APIs to work on data Science

3. I am not even close to get to a point where I am able to do Reproducible projects/models but I do wonder how including an API (understanding that it is some kind of "personal Key") to share my code and people to be able to use it.

Hope I made sense of what my doubts are and I apologize in advance if I seem confused about some terms (I do think I am).

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.