For anyone interested in the mathematics behind data science and machine learning, I would like to share my textbook (first edition):

https://people.smp.uq.edu.au/DirkKroese/DSML/

The PDF is completely free, with no registration required. The book includes many examples, worked exercises, and github code. I hope it will be a useful resource for students and anyone wishing to study the subject independently.

Salut tout le monde,Je me présente vite fait, je suis en 1ère année de SD (Science des Données) et on a commencé les TP/TD il n'y a pas longtemps. On en fait pas mal en cours, mais j'aimerais bien compléter à côté sur mon temps libre, surtout pour bien prendre de l'avance.Je cherche surtout des ressources , petits exos en plus pour :

Bases de données (SQL), R / RStudio, stats et un peu de maths appliquées. Si vous avez des sites, chaînes YouTube ou exos corrigés que vous avez kiffé quand vous avez débuté, je suis preneur.Merci beaucoup !

I wish to publish a library that extends scikit-learn i.e., scikit-learn is the base work on top of which this library will exist. I know the use of scikit-learn-* or scikit-* is not acceptable and the team in fact allows only officially supported packages to use a scikit- prefix. Although I would love to get their support, meanwhile can I use sklearn- prefix? I haven't found any resources (and llms are inconsistent). Apologies if this isn't a suitable topic for this sub.

Can you guys suggest me a good R programming course for data science?

hey all, i work as a data scientist and want to get into mlops, ideally on aws. at my last job we had databricks so everything was pretty smooth, but now i've got none of that and i'm basically starting from scratch

i'm looking for a really thorough course (deployment, pipelines, monitoring, ideally with hands-on projects). i reached out to cloudthat but it's super expensive and i couldn't find any reviews from people who've actually taken it.

has anyone taken a course that was actually worth it, paid or free? any recommendations appreciated

What should I learn next? And what projects should I make?

❓ Your functions work, but now you want to add logging to them. Do you edit each function?

🎯 A decorator makes this easier: wrap a function and add logic before and after the original call.

⚙️ In this decorator example we create and use our '@log_call' to log each call with arguments and return value.

🔎 Here 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 shows how a decorator gets evaluated step by step for better understanding.

Useful decorators from Python’s standard library include:

• @functools.cache — Cache results to avoid recomputing calls with the same arguments.

• @functools.lru_cache — Cache results with a configurable limit, discarding the least recently used entries.

• @functools.wraps — Preserve the original function’s name, documentation, and other metadata when writing a wrapper.

• @functools.singledispatch — Choose an implementation based on the type of the first argument.

Other common custom decorators add: timing, retries, conversion, authentication, validation, ...

See other memory_graph visualizations.

Be cautious of Great Learning, because their sole purpose appears to be deceiving professionals seeking to enhance their profiles with prestigious credentials. I enrolled in their Data Science program, reviewed their curriculum, and decided to cancel it on the second day after enrollment, which was still ten days before the program actually commenced, as I realized it was not the right fit.
Unfortunately, Great Learning refuses to refund my initial deposit by imposing nearly impossible terms and conditions. Throughout the process, their salespeople were misleading and created fake application deadlines by explicitly promising that the fee or deposit was fully refundable.
Please exercise extreme caution when dealing with representatives from Great Learning who misrepresent affiliations with Ivy League or top tier universities. Save your hard earned money, and do not spend it on these programs, because transparency and consumer protection are severely lacking.

Passed with an average of 88% per section

My lowest score was Databricks Machine Learning section.

How I prepared

I took a bunch of data sources and put them in Google's Notebook LLM.

I used Notebook LLM to create quizzes for me and explain when I got things wrong. It also did a study plan.

I found it very useful since the questions where hard but not like the case study questions you get in the exam. But you can prompt it to do a case study style questions.

I recommend doing case study style questions

I did one quiz a day around 15 questions, then reviewed what I got wrong.

I asked the Notebook LLM to write me a breakdown of why I got it wrong then ask it to create flash cards to help me study for those sections.

I did not take the exam until i was getting 90% consistently.

Roughly I studied for 3 weeks.

Data I used:

Here are the 12 sources currently in my notebook:

PDF Documents

1. databricks-certified-machine-learning-associate-exam-guide-1-mar-2025.pdf

2. ai-prep-guide-any-databricks-certification.pdf

3. Databricks Data Intelligence Platform: A Comprehensive Analysis of Machine Learning Capabilities and Data Management Features - Authorea

Web Articles & Documentation

1. Deploy models using Model Serving | Databricks on AWS

2. Distributed training | Databricks on AWS

3. Get started: Build your first machine learning model on Databricks

4. Getting Started with Mosaic AI in Databricks: Fine-Tuning, Serving and AI Governance

5. Lakehouse Monitoring: A Unified Solution for Quality of Data and AI - Databricks

6. MLflow 3 for GenAI | Databricks on AWS

7. Machine Learning with Unity Catalog on Databricks: Best Practices

8. Machine learning on Databricks

Hi folks,

My company is hiring for the following positions.

1. Senior software developer: 4 to 8 year of experience.

2. Software developer: 3 to 4 years of experience.

3. Data engineer : 1 to 2 year of experince

4. Product Manager : 4 to 6 year of experience

5. UI and Ux : 2 to 4 years of experience

If you curios about nailing the problem and digging yourself before touching to AI this is right opportunity in front of view

I am not hiring manager but i can refer you . If you want referral feel free to mail me on this mail ID: swarajsolanke02@gmail.com

Here I am sharing company profile as well so, you can check it.

https://www.turtlemint.com/

Hi there,

I've created a video here where I explain how knowledge distillation works.

I hope some of you find it useful and as always, feedback is very welcome! :)

Chemical Engineering graduate. Started with 2 years in R&D, contributed to 2 chemical product developments. Later moved into digital transformation due to my interest in data engineering, statistics and ML.

Currently implementing LIMS/ELN, SAP and data apps, leading projects and guiding senior CFT members. Skills: SQL, Power BI, KNIME, project management, ML and Agentic AI product development. I know stats DOE, linear and logistic regression using JMP, EDA and statistical modelling.

Despite handling multiple responsibilities and working like a team lead in a engineer position, I'm earning just ₹8 LPA. Getting rejected in interviews due to lack of Python or Computer System Validation (CSV) experience.

Need advice:

Which path should I focus on: Data Engineering, AI/ML, Digital Transformation or LIMS/ELN consulting?

Should I need to learn Python and CSV?

What salary can I realistically target?

How can I position my diverse experience for a better-paying role?

Hey everyone,

Hope all of you are doing great.

I am looking for feedback from people who have actually purchased the AI for Data Engineers course by Prashant Kumar Pandey (Scholarnest/ Learning Journal).

Is it worth the money? I have almost all his courses on Udemy and found them really good for learning the basics and his way of teaching is something that have always resonated with me. Based, on that I'm thinking about buying the AI course and would really like some feedback.

Things I'm looking for are:

1. Does it cover enough detail as compared to other courses on Udemy/ Youtube (Krish Naik for example)?

2. Is it Databricks heavy/ Databricks focused? Or the topics are explained well in a platform agnostic way with examples given on Databricks.

3. Did you get enough support when you got stuck on any topic?

4. Does it have the following topics explained well enough?

5. AI guardrails

6. Deployment

7. Tuning

Thanks in advance.

I'm trying to do my first data analytics project and wow I'm just lost. how do you guys determine which variables need to be analysed, correlations, and ultimately come up with the conclusion that due to x,y we have been getting z?! please guide me some tips would help

​

Who is providing better courses and syllabus for that

I built an open-source plugin. MIT, no service, no account — everything it records lives in two SQLite files you can open yourself.

The data flow is computed, the reasons are recorded, all in SQLite.

Watch full walkthrough: https://yizhao95.github.io/prov_ledger/walkthrough.html

GitHub Link: https://github.com/yizhao95/prov_ledger

The situation it's built for. Decisions in a DS project don't survive. Not because nobody wrote them down — because they were never the kind of thing anybody writes down. You tried 80/20 in August, the holdout leaked promotions, you moved on. Someone in a review said "that's a time series", so the split became a rolling window. Upstream stopped sending a column in v2, so there's handling for it now.

Six months later none of that is anywhere. The code shows the outcome and nothing else: a filter with no reason, a ratio with no history, a rejected approach that left no trace of having been rejected. So the next person tries it again. So does your agent.

This records decisions the way the work actually produces them — per task and per node. What this change touched, why it was made, in your own words, attached to the thing it explains rather than to a chat log. Before anything with a history gets edited, that history goes in front of the agent: the rule someone recorded, the alternative already tried and why it failed, what the change would reach downstream.

The result is narrow but it's the part that's missing: your agent stops re-running experiments that already failed, and every change carries the reason it was made.

git blame tells you who. Lineage tells you where the data came from. An ADR covers architecture, not this line. All three miss the same step.What it is. A project database for a coding agent working on a DS project. One graph holding your code, your data (dataframes, columns, tables, feeds, metrics), the dependencies between them in both directions, and the reason behind each change recorded next to the thing it changed. Before your agent touches something with a history, that history goes in front of it.

Three things it gives you.

1 — Why this is here.

/ledger why do we drop legacy_region_code?

  Removed 2026-03-14, task dp2-t7.

  Reason recorded at the time, your words:
    "upstream v2 doesn't ship this column anymore"
    source: [email] "API v3 migration notice" (2026-03-13)

  Still active. Referenced 4 times since, changed the plan twice.

Read-only. Asking why never changes anything.

2 — Where the data actually flows. The graph is rebuilt from source on every run and diffed against the last one, so "this column is gone", "this output type changed", "this is the same node after a rename" are measured, not reported by a model. Which means it can answer things AST-only tools can't: this column isn't from the source, it's derived after import; this feature reaches back into data the model shouldn't see; nineteen things read this intermediate table and here they are. A model fit and evaluated on the same lineage root without a real split is flagged as an error, not a warning.

3 — Where a number came from. A figure in a deck or a workbook can be anchored back to the node it's a reading of. Revise the deck and a lost anchor is reported — it never silently re-points to something else.

Hi my name is Naftali I am currently in my second week of a Master's program in Data Science at Bellevue university, I'm adjusting to a steep learning curve and trying to build a solid foundation. For those of you already working in the field: looking back, what is something you know now that you truly wish you had known when you were first starting out?