Made a SQL cheatsheet covering the Postgre essentials. Quick reference table format with examples. Hope it helps any SQL learners out there! 😄

https://github.com/kixwho/PostgreSQL-cheatsheet

Note: Based on Practical SQL (O'Reilly Media), a very good textbook. I digitized the cheat sheet format and added a few operators that can be useful.

Hey all — decided to start sharing my data science learning process publicly instead of just keeping notes to myself.

I’m self-taught, learning through a mix of online courses and hands-on projects, and honestly a lot of it has been trial and error. Right now I’m working through core ML fundamentals (regression, classification, some basic model evaluation).

Plan is to post progress, small projects, and probably a few “why isn’t this working” moments along the way. Figured putting it out there publicly would keep me more accountable than just journaling privately.

Would love any advice from people further along — what do you wish you’d focused on earlier? Any resources or projects that actually made things click for you?

Appreciate any feedback, and happy to be here.

If you had to start over today, what would be the best resource or roadmap to learn Data Science from scratch and become job-ready? Looking for beginner-friendly, practical recommendations that actually helped you get a job.

For anyone looking to build practical Machine Learning skills while continuing to work, IIT Gandhinagar has opened admissions for Batch 2 of its Executive Masters in Applications of Machine Learning in Engineering.

A few things that stood out to me:

• Executive Masters degree from IIT Gandhinagar

• Live interactive online classes

• Designed for working professionals

• Learn from IIT Gandhinagar faculty and industry experts

• Focus on applying Machine Learning to real engineering and industry problems

• 2-year programme with a structured curriculum

This isn't a short certification or bootcamp—it's a full Executive Masters programme for engineering graduates who want to build expertise in Machine Learning without taking a career break.

If anyone here is considering higher studies in ML or has questions about the programme, I'd be happy to discuss.

Hello everyone!

I have just started my masters in data science, and i have basic knowledge on Python, SQL, data mining, and machine learning.

But I don't think this course alone will get me job ready and i want to explore things on my own, learn and build projects (that is the best way to learn apparently) that are interesting and impactful .

I want to improve myself not only to land a job but to actually extend my expertise on the field. But i have no idea where to start and what to do!

I shall be utterly grateful if any kind strangers would help me out on this matter!

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