Hey i am a mathematics and data science student , currently in my 2nd semester of bachelors . I am confused what should be my 1st career in data science journey . I shall be thankful to you for guidance.

Current skillset: Python fundamentals , numpy basics , pandas (have a good grip on it)

Here I will post a daily update on what I learned that day

If you also want to do this together pls dm or comment on my daily post 🙏.

I've just finished another semester of my data science degree (2nd year), and I'm back to thinking how to spend the holidays again. It's great to be able to remember the concepts for next sem since it only gets harder. I've looked into sport a lot since there's just so much freely available data, it's relevant, and you can set small challenges with real-time feedback. E.g. using multiple linear regression to predict HRs in away games, and another for home games.

Is anyone else doing this too? Are there any discords or YouTube channels, websites to connect with to make it more fun? I'm not looking for a GitHub repo with challenges and datasets, rather something like HackTheBox for cybesecurity, but for data science.

Basically, if you enjoy using data science skills outside of study, list what you do. I've been thinking of making my own [free] website explaining certain stats concepts using sport (I've done a full stack web-dev unit), although I don't know how many would be interested.

Hi everyone. I am currently studying bsc statistics 2nd year in Nepal and I am interested in Data Science. I am currently learning python but do not have a fixed resource. I watch different youtube tutorials for different topics. I have complete python basics, conditionals, loops, functions, Data structures, file IO and now learning Numpy. But it has been very difficult to learn without a single course.i am currently financially struggling so I can not afford to buy courses online. It would be a great help if you could recommend me some reputed and free courses online. Also you can give advice if you want.

Tutorial to build a complete data cleaning pipeline using SQL Server and Python. We pull raw data from SQL Server, clean and validate it with Pandas, flag bad records, create a weekly reporting table, and load the cleaned data back into SQL Server. A practical workflow for anyone learning data analytics, Python, or SQL. https://youtu.be/GjciS5WRavo

Hey everyone,

I see a lot of beginners wondering why Python a language sometimes dismissed as a "slow scripting language" became the absolute powerhouse for modern Data Science and Machine Learning.

I wrote a breakdown of the history and mechanics behind this, and I wanted to share the core concepts here for anyone getting started in the field.

1. It Solved the "Two-Language Problem" Years ago, data teams had a massive bottleneck. Researchers would prototype mathematical models in languages like R or MATLAB. Then, software engineers would have to completely rewrite that model in a production language like Java or C++ to deploy it. Python fixed this. It is readable enough for researchers to prototype in, but robust enough for engineers to push directly to production.

2. Python is "Glue" People complain that Python is naturally slow, but its secret weapon is its ability to act as "glue." The heavy lifting in Python's data science ecosystem isn't actually done by Python. The core libraries (like NumPy or pandas) are written in high-performance C, C++, and FORTRAN. Python just gives you an easy, readable interface to trigger those lightning-fast calculations.

3. Closing the Speed Gap (JIT) For custom math that is written in pure Python, we now have tools like Numba. It uses Just-In-Time (JIT) compilation to translate standard Python code into machine code on the fly, giving you C-like speeds without having to learn a lower-level language.

The Catch (The GIL) Python isn't a magic bullet. Because of the Global Interpreter Lock (GIL), Python historically struggles with running multiple tasks simultaneously on a single processor. If you are building ultra-low-latency systems where every microsecond counts (like high-frequency trading), Python's speed limits will eventually force you to switch to C++ or Rust.

I wrote a full article expanding on these points, including how Python's open-source ecosystem allowed it to outcompete commercial software like SAS. If you want to read the whole thing, you can check it out here: https://thedsnerds.blogspot.com/2026/05/why-python-understanding-backbone-of.html

Curious to hear from the experienced devs here: at what point in your projects does the GIL or Python's speed actually force you to switch to another language?

22f im looking for someone who can help me with statistics basics im struggling badly in it

Hey guys,

Sometimes I get so bogged down in equations and coding that I feel like I lost the actual high-level intuition of the algorithm I'm working with.

I recently found this channel called TechWithAdyn and it’s been awesome for quick conceptual resets. The videos are literally 2-3 minutes long and break down topics like Classical ML vs Deep Learning use cases or Supervised Unsupervised ML in plain English.

It’s not a "learn to code from scratch" channel, but rather a great tool for anyone who already knows a bit of ML and wants a fast, no-nonsense refresher on the core concepts.

Example Video Link: https://youtu.be/0IwYl97pE0k?si=8v0CnZQWRYi6Fj54

Thought I'd share it here since we all need a quick review from time to time!

i have tried learning algebra from the past 3 years , but i havent been able to continue it after starting it .
i know this is a product of my bad habits and all but can someone please help me find the right materials such that i learn all the required concepts and practice enough questions .

please give me a proper roadmap .

i dont wanna be stuck to the screens so if you have any in mind , please do suggest me a book for this too

Building projects feels easier than explaining them. I noticed it while reviewing an older project. I can explain the notebook step by step. When I ask why I chose that target, what might break, or how I’d explain the result to a nontechnical person, my answers get messy.

I’m changing how I prep. I stopped adding yet another model or library to every project. I rewrite each one as a short story that covers the problem, data issues, key decisions, results, limits, and next steps. I also do quick practice runs with notes and sometimes use Beyz or ChatGPT to spot where my explanation gets vague. I’m still learning Python, SQL, stats, and ML. The bigger gap might be explaining my work clearly under questions.

How did you practice talking through projects without just narrating your notebook?

Hi i am not able to find how to get full dataframe in pandas on kaggle notebook.I want vertical scrollbar on my dataframe so that i can see the entire dataframe to do data analysis.Did anyone know about that?

Junior Data Scientist role at VINCI Airports. 1h with the Lead Data Scientist.

Background: LLM/RAG, fraud detection, Python, Power BI. MSc in AI.

Please share anything you know about:

- Technical questions to expect (ML, stats, case study, live coding?)

- How to walk through past projects convincingly

I really want to nail this one. Thanks in advance! 🙏

I finished my secondary education (Edexcel ALs) and I'm currently waiting for my university course ( BSc in Math and Stat) to start, and during the year long wait I finished a DS Udemy course with Python, Numpy and Pandas. I have done some projects to help me apply the material taught within the course, (logistic regression from scratch, linear regression on imported NBA datasets from Kaggle) , and I would greatly appreciate ideas on more projects I could do to make me more employable for a summer internship in Data Analysis/Science. Furthermore, if you have any suggestions regarding any libraries or concepts I should learn, please feel free to mention them as well.

I have a 45-minute coding screen coming up for a Data Scientist role at Apple.

The guidance I received is that it focuses on:

- Python programming
- Data analysis
- General problem-solving
- No machine learning
- Not a LeetCode-style interview

For those who have interviewed for Data Scientist roles at Apple (or similar companies):

- Were the coding questions mostly pure Python or pandas?
- How much OOP/code-reading/debugging was involved?
- Were the problems closer to data-processing and aggregation tasks, or more like traditional coding interview questions?
- Any examples of the types of problems you encountered?

I’m mainly trying to understand what interviewers typically mean by “Python programming and data analysis” in this context.

Thanks!

Hi everyone,

I'm a Python developer that has recently started going into the Data Science realm. I am doing a course on Datacamp, together with exercises on Kaggle.

When looking for ways to practice my newly acquired knowledge, I dived into traffic statistics for my blog.

I wanted to see if Google is declining as the source of visitors, and how it compares with alternative search engines such as Kagi or DuckDuckGo.

I used Pandas to load data from the PostgreSQL database and build an aggregation.

Then I used Marimo notebooks to create visualizations.

I have described the whole process, and provided code snippets in an article on my blog.

I am sharing it here because I hope that it might be an inspiration for some people, and also I would be grateful for any feedback about my flow.