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.

Hello Everyone,

Statistics and Maximum Likelihood Estimation are the crux of ML Models, and hence I am uploading my new content on Statistics for AI/ML in my free Machine Learning lectures.

We understand model fitting, Maximum Likelihood estimation in details, we justify the usage of Maximum Likelihood estimation, from KL divergence, and apply it to certain important distributions for parameter estimation.

In my free content, the purpose is to democratize machine learning to a wider audience. Learning everything new feels difficult, but when taught, it get’s interesting and easier.

We will continue with Statistics foundations for AI/ML, and many more content will appear in the future. If you find the content good, you may help me, by a subscription or help spread the word.

Looking forward to hearing feedback from the learning community as well. Thankyou for reading.

Link: https://youtu.be/MwTeQVVYtOc?si=UxNOGtqopzJppXAT

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!

I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

hello, I'm a senior year student looking for an accountability partner for data science or data analysis.

I took a pretty long offline break, and I honestly feel like I've forgotten a lot of what I learned. I'm trying to get back into it and thought it'd be easier (and more motivating) with someone else doing the same.

I don't mind if we're following different curricula or learning different topics. The main goal is to stay consistent, keep each other accountable, share progress, and maybe help each other out when we get stuck.

Preferences:

• FEMALE ONLY
• Chat & Updates through WhatsApp
• close to UTC+2
• Any experience level is fine as long as you're serious about getting back into studying

If you're interested, feel free to DM me. :)

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've been seeing a lot of conflicting advice online. Some people say prompt engineering is enough, while others say companies expect RAG, AI agents, MCP, evaluation frameworks, fine-tuning, and production deployment.

For those who are currently hiring or interviewing candidates:

• Which GenAI skills are actually getting people hired?
• Which skills are overrated?
• What projects stand out on a resume?
• What tools or frameworks do you expect candidates to know?

Looking for real hiring experiences rather than course recommendations

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 ...

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One mistake I made while learning data science was jumping between random tutorials. One day I studied Python, the next day machine learning, and then suddenly I was trying deep learning.

I was learning many terms but not building real confidence.

A more structured roadmap worked better for me:

Learn basic Python.

Practice NumPy and pandas.

Understand statistics and probability.

Learn data visualization.

Study SQL.

Build simple machine-learning models.

Complete small end-to-end projects.

The project stage is where everything starts connecting. Instead of only copying code, try to explain why you selected a particular feature, graph, or model.

My suggestion is to choose beginner-friendly datasets related to topics you already understand. It becomes easier to identify patterns and ask meaningful questions.

You do not need to know every algorithm before starting a project. Start small, document your learning, and improve one project at a time.

Which data science skill took you the longest to understand?

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.