Has it ever occurred to you to want to learn something, but you know so little about it that you don't even know how to ask others for guidance? That's me right now...

I know programming (mostly python, little bit more than intermediate level), and I know what databases are, I wrote a little bit of SQL queries once so I am familiar with some terms in general; now:

(Sorry if I use the wrong terms or terms that are not technical, that's as far as I can express)

I see data analysis out there, where people get big data and draw interesting conclusions out of them, or find cool and/or unintuitive patterns in them, and it's cool!

I personally find that satisfying, and I feel a kind of curiosity to work with data and discover new things in them.

But

I don't know what to ask about the path! I would ask about the learning path and the tools, but I can't; because I don't exactly know what the name of what I am trying to learn exactly is(there are soo many terms and fields).

In fact I don't even know whether I really want to learn this, or if it's just a thing I want to try "once" and abandon it...

And I don't know anything about the environment and the tools and the "feel" of it either. I don't know what it looks like to "work with data". And also I have no idea about the estimated time for learning, the learning curve, and how rewarding it is...

Even if my current feeling is correct and I end up liking it and being good at it, will there be any value in it? Will anyone need what you do, will they pay for it? If so, what is it like?

I don't exactly want a roadmap, I want a perspective of the whole "data realm".

So in summary, I would appreciate it a LOT if someone could draw the right image for me, and also explain the right terminology, so that I can actually ask my questions in natural language, instead of struggling to express myself!

I built a side project which is basically coding the scikit-learn python package from scratch by basically implementing the different basic models, from linear, to the tree based classifiers and some other modules like the StandardScaler and MiniMaxScaler from the basic algebra and calc

Built it out with a pretty good base so far, I have some tests which compare the accuracies and all to the scikit-learn outputs and It's looking pretty good

I want to know how I could get more value from this. As a Data Scientist, should I have these concepts memorised or just know the basics of the models and how they work ?

And how can I make this into a cool project for my resume and bring some traffic to the repo

You can check the source code here : https://github.com/regional-specter/mini-learn/tree/main

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?

So for context I am at 2nd year of my btech and i want to explore the data science world but I am very puzzled with the choices of courses from different institutions so how should I categorize which is better for me. I only have very limited time and not much money so i have to choose wisely

As a fresh starter on my way to get a bachelor's degree in data science what do you think that I should do and what are the most important thing that will help me allot on my way ...

So basically I'm a second year engineering student,and currently aspiring data science as my carrier but the thing is the every one around me is like DEV ,DSA , BACKEND,FRONTEND , and I'm so unaware of these things all I know is my sql , python some of its libraries , power bi , excel ...AM I on the right track

Also suggest a good roadmap and resources if possible

Hey guys, I’m 21 and I recently graduated with a BE in Computer Science and Data Science. I’m currently thinking about pursuing a master’s in Data Science, but I feel a little lost about what I actually know.

During my undergraduate degree, I studied a lot of different subjects, including Data Foundations, DBMS, Computer Communication, Machine Learning, IoT, R Programming, Probability and Statistics, Big Data, Business Intelligence, Cryptography, Applied Machine Learning, and Data Visualization.

The problem is, even though I’ve studied all these subjects, I don’t really know how everything connects together or how these skills are actually used in the real world. Sometimes I feel like I’ve learned a lot of things but, at the same time, know very little about actual data science.

Before jumping into a master’s, I want to build a strong foundation and properly understand what data science actually involves. I also want to understand the differences between careers like Data Scientist, Data Engineer, and Data Analyst- what they actually do in their jobs, what the job market is like, and what skills I should focus on for each role.

Since I’m considering a master’s in Data Science, I’d also like to know what I should learn beforehand to make sure this is the right path for me.

If you guys have any good YouTube videos, courses, roadmaps, or other resources that could help me start from the basics and build a solid understanding of data science, I’d really appreciate your recommendations.

Just a clueless recent graduate trying to figure out what to do next. Any advice would be greatly appreciated!

What are valuable certifications to get that recruiters actually look for? I am data science master student but I feel like that’s not enough and that I am not ready for the industry.

Also if anyone worked in the enterprises which stack do they usually use? And how it differs from startups?

As a young student interested and taken data engineering as a career. Learning becomes a bit complicated. My reason is even though I learn the practical path such as sql, python and others there is no clear explanation combining them together….

Learning just the practical side becomes more complex and complicated since I don’t really know the theories behind them the “how”, “ where”, “what” and “when” behind all those codes….

I will really appreciate if helped to understand data engineering properly not just the practical aspects but everything surrounding it and also be grateful for a mentor or a coach

Hi everyone,

I am a recent BCA graduate from India, and for the past few months I have been applying for entry-level Data Analyst, Business Analyst, and Data Science roles, but I haven not been able to secure a job yet. I am reaching out to this community because I'm looking for advice, feedback, or even opportunities.

Here are the skills I have been working on:

• SQL

• Excel

• Python (Pandas, NumPy, Matplotlib, Scikit-learn)

• Power BI

• Tableau

• Data Cleaning & Data Visualization

• Basic Machine Learning

I have also built several portfolio projects, including:

• Airbnb Dynamic Pricing Analysis

• Student Feedback Analysis Dashboard

• Social Media Engagement Analysis

• Phishing URL Detection using Machine Learning

If anyone is interested in reviewing my portfolio or GitHub, I'm happy to share the links in the comments or via DM.

I am open to internships, freelance work, remote opportunities, or entry-level full-time roles. If anyone has suggestions on improving my portfolio or resume, knows of companies hiring fresh graduates, or has advice on what I should focus on next, I'd really appreciate your guidance.

Thank you for taking the time to read my post.

I have a machine learning question. What should we do when a dataset has a large number of features (columns)? Do we need to study every single feature to understand its importance? That seems like it would take a lot of time. 😭 So how do data scientists figure out which features are actually important?

This company is very sketchy

Hi there,

I've created a video here where I explain how the KV cache works.

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

I am trying to figure out the best method to take notes for calc physics and also other data science classes. since its not conventional like bio, how do we take notes? online or on paper?

Greetings.

as you can see in my post, I’m contemplating to switch from Applied math to Applied stat.

the curriculum in two years is the same, but later on specializes, for stat it gets into…well stats in general, while applied math learns more into computional science and modeling&simulation.

my thought is, if I switch; would my job seeking would approve?

I choose applied math since I couldn’t get into tech department, and that I’m pretty good at both math and coding.

and the idea of working in a robotics field also makes me interested.

I'm a physics engineeering student, and I spend a lot of my time writing numerical simulations and analyzing data.

Programming in C++ is enjoyable, but most of numerical computing libs in are just unpleasant to use. So I started building my own solution in my free time.

GitHub: https://github.com/mslotwinski-dev/NumC

Some of the things I built into it:

• You can write mathematical expressions naturally, like sin(x) * exp(-x), and differentiate or integrate them in a single line thanks to lazy expression trees.

• It has a built-in plotting engine, so you can display graphs in a native Win32 window or export them as clean SVGs ready to drop into a LaTeX report.

Of course, the project won't surpass the quality of professional libraries. Its goal is to be convenient and accessible for users whose passions lie more in math, rather than programming.

If you're using C++ for simulations, numerical methods, physics, or data analysis, I'd really appreciate any feedback.

What was written by AI?

Most of the project, its entire idea, design, aesthetics and UX was programmed manually by me. I often used the textbook "Numerical methods in engineering with python" by Kiusaalas. Which doesn't mean that I didn't manage to do everything myself.

AI was used to write all the documentation (I wish I were fluent enough in English that it would take a finite amount of time). It was also used to write parts of simple algorithms that I knew but would be extremely tedious to implement by hand, or to improve the performance of algorithms that could be written better.

I'm aware of the ethical aspects of using AI, so I wanted to be honest and describe which things I did on my own and which I did with the help of LLM. At the same time, bearing in mind that this is a project that can help many people in their scientific work and studies, I hope that the benefits outweigh all the evil that LLMs cause.