[center]Free Courses And Resources: Roadmap To Master Data Science 
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[center]High-quality free resources in data science are often buried under paywalled platforms and bootcamps. Yet, there exist rare, lesser-known courses, repositories, and project platforms that can fast-track your expertise while costing nothing. Below is an expanded, carefully curated list of such resources—structured for learners at all stages.[/center]
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Foundational Data Science & Programming Courses
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Intro to Data Science – Udacity
Beginner-friendly with Python, SQL, and data visualization, leading to first-hand projects. -
IBM Data Science Professional Certificate (Coursera)
Covers Python, databases, data analysis, and ML in a structured pathway, with projects and labs. -
Microsoft Data Science for Beginners
A free curriculum with 20 lessons covering Python, visualization, and ML concepts—available open-source on GitHub. -
OpenIntro Statistics
A complete free statistics textbook with exercises—ideal for building strong statistical intuition. -
Intro to Python for Data Science (DataCamp Free Track)
A free beginner Python course tailored specifically for data analysis.
Intermediate to Advanced Data Science Specializations
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Applied Data Science with Python (University of Michigan – Coursera)
Focused on Pandas, Matplotlib, scikit-learn, NLP, and text mining. -
Open Machine Learning Course (mlcourse.ai)
Practical ML course with Jupyter notebooks, Kaggle-based assignments, and competitions. -
Caltech Learning From Data (YouTube + Book)
A mathematical yet approachable ML course by Yaser Abu-Mostafa, widely respected in academia. -
Data Science MicroMasters – UC San Diego (edX)
Multiple advanced-level modules covering probability, statistics, machine learning, and big data. -
CMU Introduction to Machine Learning (Free Course Materials)
Carnegie Mellon’s official ML course content: slides, assignments, and readings.
Deep Learning & AI Focused Resources
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Deep Learning Specialization – DeepLearning.AI (Coursera)
Andrew Ng’s deep learning program covering Neural Networks, CNNs, RNNs, and optimization. -
Neural Networks and Deep Learning (Michael Nielsen)
A free online book that makes neural networks intuitive through visual explanations. -
NYU Deep Learning (Yann LeCun)
Course notes, labs, and lectures by Yann LeCun, one of the “godfathers” of AI. -
Full Stack Deep Learning Bootcamp
Goes beyond modeling to cover data pipelines, deployment, and scaling AI systems.
Mathematics for Data Science
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Mathematics for Machine Learning (Coursera – Imperial College)
Linear algebra, calculus, and probability tailored for ML applications. -
3Blue1Brown – Essence of Linear Algebra
Visual and intuitive video series explaining linear algebra concepts. -
Khan Academy – Probability and Statistics
A free structured statistics curriculum covering probability distributions, inference, and hypothesis testing. -
MIT 18.06 – Linear Algebra (Gilbert Strang)
A legendary math course essential for ML and data science foundations.
Data Projects, Datasets & Competitions
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Kaggle Competitions
Compete globally on datasets in finance, healthcare, sports, and NLP. -
Google Dataset Search
A search engine to find open datasets across domains. -
Data.gov
Free U.S. government datasets on healthcare, finance, climate, and more. -
World Bank Data
Rich datasets for economic and development-related projects. -
Awesome Data Science Projects (GitHub)
A curated list of hands-on projects, tutorials, and open-source data science examples.
Career-Focused & Applied Learning
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Springboard Free Resources
Guides, templates, and roadmaps for data science career preparation. -
Towards AI (Medium Publication)
Covers AI, ML, NLP, and data science insights from professionals worldwide. -
Workera Skills Assessments
Free data science and AI skill assessments to benchmark your knowledge. -
Analytics Vidhya Free Courses
Free short courses on data visualization, SQL, and ML basics.
Action Plan for Learners
- Start with basics → Python, Statistics, Intro courses (Udacity, IBM, Microsoft).
- Advance → Applied ML (mlcourse.ai, Caltech, UMich).
- Deep dive into AI → fast.ai, NYU Deep Learning, Full Stack DL.
- Master the math → MIT Linear Algebra, Coursera Math for ML.
- Build a portfolio → Kaggle, DrivenData, UCI Datasets.
- Stay updated → Podcasts, Towards AI, GitHub projects.
These rare, highly valuable free resources create a clear path from beginner to expert. With consistent study, project building, and community participation, you can build a strong portfolio and industry-ready skills entirely without paid programs.
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