🎓 15 Free MIT Courses to Kickstart Your Data Science Career 🔥

Published by MIT Open Learning, this curated list brings you the foundational building blocks of Data Science—math, stats, Python, ML, and more. All 100% free!

📚 Course List:

1️⃣ Linear Algebra
Explore linear algebra and matrix theory through multidisciplinary topics.

2️⃣ Single Variable Calculus
Master derivatives, integrals, coordinate systems, and infinite series.

3️⃣ Multivariable Calculus
Learn differential, integral, and vector calculus for multivariable functions.

4️⃣ Introduction to Probability and Statistics
Foundations of probability, Bayesian inference, and linear regression.

5️⃣ Probability: The Science of Uncertainty and Data
Part of MITx MicroMasters in Statistics & DS—random processes, statistical inference.

6️⃣ Fundamentals of Statistics
Estimation, hypothesis testing, prediction. Also part of MITx MicroMasters.

7️⃣ Understanding the World Through Data
Use basic data forms, tools & ML algorithms to make sense of the world.

8️⃣ Introduction to Computer Science and Programming Using Python
Solve real-world analytical problems with Python 3.5.

9️⃣ Introduction to Computational Thinking and Data Science
Learn to solve problems computationally & write small, effective programs.

🔟 Data Analysis: Statistical Modeling and Computation in Applications
Analyze real-world data using stats & computation (also MicroMasters course).

1️⃣1️⃣ Introduction to Algorithms
Model computational problems and solve them using powerful algorithms.

1️⃣2️⃣ Introduction to Machine Learning
Explore ML principles, modeling, and predictive applications.

1️⃣3️⃣ Matrix Methods in Data Analysis, Signal Processing, and ML
Linear algebra meets neural networks, probability, and optimization.

1️⃣4️⃣ Mathematics of Big Data and Machine Learning
Understand D4M (Dynamic Distributed Dimensional Data Model) using graph theory and databases.

1️⃣5️⃣ Machine Learning with Python: from Linear Models to Deep Learning
Hands-on ML with linear models, deep learning, and reinforcement learning in Python.

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🌐 Source: MIT Open Learning
🔗 https://openlearning.mit.edu/news/15-free-mit-data-science-courses

💸 100% Free | 📍 Self-Paced | 🧠 Taught by Top MIT Professors

#datascience
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Machine Learning Free Courses

Machine Learning with Python – FreeCodeCamp
🎬 1 video lesson (full course)
Duration ⏰: 4 hours
🏃‍♂️ Self paced
Resource: freecodecamp
🔗 Course Link


Machine Learning for Beginners – 26-Lesson ML Video Series
🆓 Free Online Course
🎬 Video lectures
⏰ 10 hours
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Microsoft Cloud Advocates Team
Source: Microsoft Learn
🔗 Course Link

STAT 451: Introduction to Machine Learning – Sebastian Raschka (UW‑Madison)
🆓 Free Online Course
🎬 Video lectures
⏰ ~20 hours
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Sebastian Raschka (U‑Wisconsin‑Madison)
Source: UW‑Madison STAT 451 YouTube & course materials
🔗 Course Link

Intro to Machine Learning with Python (Kaggle)
Rating ⭐️: 4.5 out of 5
Students 👨‍🎓: 125,000+
Duration ⏰: 3hrs 30min
Created by: Kaggle (Dan Becker)
🔗 Course Link

Google’s Machine Learning Crash Course
⏳Modules: 25+
Duration ⏰: 15 hours
🏃‍♂️ Self paced
Resource: Google AI
🔗 Course Link

Machine Learning Specialization – DeepLearning.AI (Audit Free)
Rating ⭐️: 4.8 out of 5
Students 👨‍🎓: 900,000+
Duration ⏰: ~30 hours (3 courses)
Created by: Andrew Ng (DeepLearning.AI)
🔗 Course Link

Introduction to Machine Learning – CMU (10-301/601)

🆓 Free Online Course
🎬 Video lectures
📝 Lecture notes (PDF)
⏰ ~30 hours
🏃‍♂️ Self-paced
Teacher 👨‍🏫: CMU Faculty (varies by year)
Source: Carnegie Mellon University
🔗 Lecture Notes

Machine Learning Full Course – Edureka (YouTube)
🎬 1 video lesson (full course)
Duration⏰: 10 hours
🏃‍♂️ Self paced
Resource: YouTube
🔗 Course Link

#machinelearning #ml
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Hands‑On Intro to Data Science with Python (2025, Univ. of Applied Sciences Düsseldorf)

📍 Topic: Python‑based data science workflow from scratch
📑 Format: Jupyter notebooks + datasets + PDF book
📆 Release: 2025
👨‍🏫 Created by Huber et al. at Düsseldorf UAS & ZDD
⏰ Duration: Self‑paced (~40 hrs)
🔗 Link: https://florian-huber.github.io/data_science_course/book/cover.html
📝 Description: A modern, project-oriented course teaching Pandas, Matplotlib, scikit‑learn via real datasets. Perfect for early-stage data scientists.


#datascience #python
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Deep R Programming
An open-access textbook
by Marek Gagolewski

https://deepr.gagolewski.com/

#datascience #rprogramming
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SQL/MySQL Learning Series: Day 8️⃣
🔹 CREATE TABLE – Defining Structure

🧱 Everything starts with structure! In SQL, CREATE TABLE lets you define your data blueprint – columns, data types, and constraints.

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📌 Let’s create a sample table:

CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    department VARCHAR(50),
    salary DECIMAL(10,2),
    hire_date DATE
);

As you can see first you write table name, and inside brackets you put column names and type of each column name. Id is also set as primary key (we talked about primary keys in day 6️⃣).

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🗂 What this means:

* id: Unique identifier for each employee.
* name: Stores up to 100 characters.
* department: Like "HR", "IT", etc.
* salary: Precise amount with decimals.
* hire_date: Date the person joined.

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📊 This will define a table like:

+----+------------+-------------+----------+------------+
| id | name       | department  | salary   | hire_date  |
+----+------------+-------------+----------+------------+
|    |            |             |          |            |
+----+------------+-------------+----------+------------+

You now have a skeleton ready to fill in!

Make sure to leave reactions ❤️💯 if you liked this post.

Next: We’ll add some real data with INSERT INTO 🚀

#SQL #MySQL #SqlLearningSeries

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Introduction to Computer Science and Programming Using Python

🏃‍♂ Instructor-paced
⏰ 9 weeks - 14-16 hours per week
Instructors👨‍🏫: John Guttag and Eric Grimson

🔗 Course Link

#computerscience #python
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🧠 New open-source LLMs: gpt-oss-120B & 20B

OpenAI has just launched those two new models from the gpt-oss project.
I have already tested them!
They’re positioned as open, local alternatives to GPT-4-class models - and yes:

they actually run on your own machine (with enough hardware).

This of course means they are completely free ✅

Here’s the breakdown:

🔹 gpt-oss-20B – smaller, easier to run locally (with a beefy GPU). Decent for coding, Q&A, and experimentation.

🔹 gpt-oss-120B – huge model aiming for GPT-4-like reasoning. Needs serious hardware (128GB+ VRAM), but shows promising results for a fully offline model.

⚠️ Let’s be honest:
They’re not better than GPT-4o - slower, less nuanced, and less aligned out of the box. But for a local, no-API setup, they’re a huge step forward for open source LLMs.

Some facts:
• First open‑weight release since GPT‑2- they’re fully downloadable under Apache 2.0.
• 20B runs on a 16 GB GPU, 120B needs something massive (80 GB+).
• They actually think step by step. Not as sharp as GPT‑4, but surprisingly solid - 120B competes with o3/o4‑mini.

If you want to know more: https://openai.com/index/introducing-gpt-oss/

I can't believe it, those guys just stole post i wrote 😅

I shared it few minutes ago ☝️

All posts in all our channels are custom generated and written by me...

Sometimes I ask ChatGPT to format my sentences but all courses I share, all tricks and tips, all tech news etc... everything is written by me and few of you guys that step up to help me run our channels.

We spend many hours every day preparing content for you and all those big channels out there just stole posts like they created them.

And yet somehow they got 50-100k subs and our @chatgpt_bds channel where i initially shared it has only 2k 🤷‍♂️
Same keeps happening to @python_bds and many other channels of ours.

Confusing isn't it?

Let me just correct myself here.
Its not open-source but its open-weight!

Open-weight means you can get access to the trained model itself, customize or deploy it however you like.
but it is not fully open source because you don't have access to everything behind the model.

One of our subscribers just corrected me in this post comment section, I thank her for that!

She even wrote a blog related to this topic 🤯
https://reamby.substack.com/p/open-weight-open-source

I really like when our subs show some engagement and extensive knowledge. Good job!

OpenAI announced the GPT-5 models in their livestream yesterday!

I watched it live and one interesting statement Sam Altman said was:

GPT-3 sort of felt to me like talking to a high school student...
GPT-4 felt like you're kind of talking to a college student.

GPT-5 is the first time that it really feels like talking to an expert in any topic, like a PhD-level expert.

Watch it here 👀 https://openai.com/live/
Alt link: https://www.youtube.com/watch?v=0Uu_VJeVVfo