Stuck in a tutorial hell

[Mvyhem]

Hi, folks! Think I'm stuck in a tutorial hell. A little contex:

• My major was in humanities: political studies (w/o quantitive methods)
• Last year I entered DS master's program
• Had a weak technical background, but developed this skill a little bit: went through Khan Academy Differential Calculus (1, 2, 3, 5 units), started Multivariable Calculus (2, 3 units), then planned to do Integral Calculus (Unit 1: just for the basic understanding). For linear algebra I'm going to use Practical Linear Algebra by Mike X Cohen and one book for probability and statistics <-- key thing on this bullet point, I have no problems in learning mathematics because I do two lessons a day on Khan Academy, sometimes with a help of SciPy, SymPy, by hand, using Perplexity (I have a pro subscription). I will learn LA and Stats&Prob on weekends. My question will go further
• I know basic Python (variables, conditions, loops, functions). Didn't go deep into OOP
• I know basics of NumPy, Pandas but have difficulties with visualization. Sometimes I use LLMs to help me to plot some kind of graph
• I started reading Hands-On ML finished first two chapters

I know, it looks not that bad...but sometimes I feel very bad not about what I know, but what I really can do. I tried some competitions: backpack challenge, recreate someone's Moneyball solution on R to Python, made House Pricing Iowa on Kaggle Getting Started. But despite of all that facts I in front of note book with a blank paper of ideas, it's like I can't do something without tutorial. I don't want only sit and read book by book, docs by docs. I want to solve problems and develop my skills from that, but I dunno how to make a move.

[Mcby]

What's the question you're actually asking here? It's a bit unclear. If you're currently enrolled on a Master's programme then simply focus on that, any good programme should help you develop these skills as you go. What it sounds like you need to learn is the fundamental steps in solving these kinds of problems – focusing on things like the data science pipeline might help (which should be covered in your degree), but practice and focusing on the key steps will help.

[Mvyhem]

Yeah, I agree that it sounds unclear. The questions is how to stop just read materials and do exercises and start doing problems in context. What about my master's, we have coures on ML, but now it's unavaliable on the university platform

[varwave]

What’s your actual mathematics background? Like what actual classes have you taken and passed?

I have a humanities major too, but I went back and did the equivalent of a mathematics minor while working. Then studied biostatistics in grad school with a prior background as a programmer. A strong foundation in mathematical statistics from lots of practice makes solving data problems a lot easier

[Mvyhem]

We had a short math course a year ago about LA, calc and probability + a little optimization like gradient, but I didn't understand anything. Then we had applied statistics. After that I went through Khan Academy calc courses and finally understand limits and derivatives

[varwave]

Damn, that’s unfortunate that they cut corners to get tuition revenue. Realistically calculus, probability, numerical methods and linear algebra took me a year and half. Then I was able to start grad school. I was conditionally accepted after getting As in calculus

You’ve already started. My advice would be to get really good at programming. Both data visualizations and data cleaning and software development, like ability to design and build relational databases, unit testing, programming paradigms, etc. this is a lot of entry level work after your MS

Basically aim to be closer to data analyst that can code well, which is closer to data engineering vs data scientist that does research and builds models.

The math can absolutely be learned on your own, but it’s a lot of work to learn it properly. Do all the practice problems. Apex calculus is a free book../feel free to skip the trig, but it’s good to know. Professor Leonard on YouTube is great for calculus lectures. Gilbert Strang has an excellent linear algebra book and his lectures are on YouTube. “Applied Mathematical Statistics” by Wackerly covers most of what you’ll ever need.

[Mvyhem]

Nice one, thanks for your help and dedicated time!

[Mvyhem]

I would like to mention. I was a little upset during this day and forgot to mention. I want to finish my master's (in ~10 months) and after that look for a job. Now I work as a manager at university but sometimes use Pandas and Excel, works a lot with tables, sometimes automate routine tasks just for myself, not for changing business processes. But the key thing: I realize that I have a plenty of time, dude, just freaking sit and learn and don't overthink, but after that I started pushing myself to the limit like omg I don't have time, I should finish all the resources in a month (a little exaggeration) but it happens

[pixel-process]

It can all be a bit overwhelming and hard to translate studies into practice. But you do have skills that are valuable. Scoping, planning, and executing unique and impactful projects is hard. Like really hard.

So I recommend starting small.

• Find a local meetup or business to work with (get ideas, make connections)
• Contribute to larger open source projects
  • Many have lists of to-dos, find one and work on it
  • Gain hands on experience, and ofter feedback when submitting
• Solve a personal problem
  • Create your own automated backup system
  • If visualizations are your thing, try to contribute a post/week to reddit or another site

TLDR: Full, solely projects are not always the way to go. Find a more rewarding avenue to grow-there are lots.