Path to Full Stack Data Science

Online Resources for Beginners

Full Stack Data Science has become one of the hottest industries in the field of computer science. Starting from traditional mathematics to advance concepts like data engineering, this industry demands a breadth of knowledge and expertise. Its demand has seen an exponential rise of online resources, books and tutorials; and for beginners, its overwhelming to say the least. Most of the time beginners start with either a python course or a machine learning course or some basic mathematics course. But many a times, a big number of them do not know where to start from. And with so many resources to go to, many of them keep scraping through resources. Moving between Udemy, Edx, Coursera and, YouTube; many hours are lost.

Figure: Subject Matters involved with Data Science

The Goal of this Article is not to list out the required syllabus but rather list out some of the prominent online resources for each subject area in the End-to-End Data Science domain. It will help the beginners start their data science journey without wasting their precious time. I have tried to put down the resources in as much order as possible. But it might vary to a great extent depending upon the individual’s expertise and requirements. The focus of this article is solely the listing out of some of the thorough and in-depth online courses and tutorials available out there for domains comprising full stack data science. I have tried to keep the list as short as possible so that it helps the starters get started with their learning without much selection.

Resources are Provided for the Following Segments

Mathematics — Linear Algebra, Calculus, Probability, Statistics & Convex Optimization

Python Programming — Fundamentals, OOP Concepts, Algorithms, Data Structures & Data Science Applications

R Programming — Fundamentals, Data Science & Web Applications

Core DS Concepts — Database Programming, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Data Visualization, Model Deployment & Big Data

C/C++ Programming — Fundamentals, Problem Solving, OOP Concepts, Algorithms & Data Structures

Computer Science Fundamentals — Introduction, Algorithms, Data Structures, Discrete Mathematics, Operating System, Computer Architecture, Database Concepts, Git & Github


Linear Algebra

1. Instructor: Grant Sanderson / Channel: 3Blue1Brown

Course: Essence of Linear Algebra

2. Instructor: Prof. Gilbert Strang / MIT OpenCourseWare

Course: Linear Algebra / Youtube

3. Instructor: Kaare Brandt Petersen & Michael Syskind Pedersen

Book: Matrix Algebra


1. Instructor: Grant Sanderson / Channel: 3Blue1Brown

Course: Essence of Calculus

2. Instructor: Prof. David Jerison / MIT OpenCourseWare

Course: Single Variable Calculus / YouTube

3. Instructor: Prof. Denis Auroux / MIT OpenCourseWare

Course: Multi Variable Calculus / YouTube

Probability & Statistics

1. Instructor: Khan Academy

Course: Probability

2. Instructor: Khan Academy

Course: Statistics

3. Instructor: Joshua Starmer

Course: Statistics Fundamentals

4. Instructor: Prof. John Tsitsiklis / MIT OpenCourseWare

Course: Probabilistic Methods

5. Instructor: Allen B. Downey

Book: Think Stats

Note: Use this book after completing the fundamentals of python & statistics

Convex Optimization (Advanced Concept)

1. Instructor: Prof. Stephen Boyd / Stanford

Course: Introduction to Convex Optimization

Python Programming

Python Fundamentals

  1. Python For Everybody: Course / Book / Web
  2. Learn Python The Hard Way: Book
  3. Think Python: Book
  4. Python Programming by Krish Naik: Course
  5. Complete Python Bootcamp: Course

Algorithms & OOP with Python

  1. Problem Solving & OOP with Python: Course
  2. Grokking Algorithms: Book
  3. Automate the Boring Stuff with Python: Course
  4. (Advanced)Social Network Analysis for Startups: Book

Data Science with Python

  1. Python Data Science Handbook: Book
  2. Python for Data Science: freecodecamp course
  3. Introduction to Computational Thinking & Data Science: Course
  4. Applied Data Science with Python: Course

R Programming

  1. R for Data Science: Book
  2. Hands on Machine Learning with R: Book
  3. Interactive Web Apps using R Shiny: Tutorial

Database Programming

  1. Fundamentals of Database Systems: Book
  2. SQL vs NoSQL| MySQL vs MongoDB: Tutorial / Tutorial
  3. Full Database Design Course: Tutorial
  4. SQL using MySQL: Course
  5. PostgreSQL: Course
  6. PostgreSQL for Everybody: Course
  7. SQLite with Python: Course
  8. Popular Database: Tutorial

Data Visualization

  1. Power BI Full Course by Edureka: Course
  2. Power BI Full Course by Simplilearn: Course
  3. Tableau Full Course by Edureka: Course
  4. Tableau Full Course by Simplilearn: Course
  5. Tableau Crash Course by Course

Machine Learning

Beginner Courses

  1. Instructor: Prof. Andrew Ng
  2. Instructor: Prof. Abu Yaser Mustafa
  3. Instructor: Krish Naik
  4. AI Introduction: / Edureka
  5. Artificial Intelligence by MIT: Course

Applied Machine Learning Course with Python

  1. Machine Learning A-Z: Course
  2. Practical Machine Learning with Python: Course

Books for Hands on Machine Learning

  1. Hands on Machine Learning with Scikit-Learn, Keras & TensorFlow: Book
  2. The 100 Page ML Book: Book
  3. Learning from Data: Book

Deep Learning

Specialization Courses

  1. Instructor: Prof. Andrew Ng / YouTube
  2. Instructor: Krish Naik
  3. Instructor: Yann Le’Cun
  4. Instructor: MIT

Applied Deep Learning with Python & TensorFlow

  1. Deep Learning A-Z: Hands-On Artificial Neural Networks: Course
  2. TensorFlow Complete Course by Course
  3. DeepLearning.AI TensorFlow Developer Professional Certificate: Course
  4. TensorFlow Data & Deployment: Course

Books for Hands on Deep Learning

  1. Deep Learning Book: Book
  2. Fundamentals of Deep Learning: Book

Natural Language Processing

  1. NLP Specialization by : Course
  2. NLP with Deep Learning by Stanford: Course / YouTube
  3. Complete NLP by Krish Naik: Course

Computer Vision

  1. Convolutional Neural Networks for Visual Recognition: Course
  2. Complete CV by Krish Naik: Course
  3. Full OpenCV by Course

Reinforcement Learning

  1. Reinforcement Learning by DeepMind: Course
  2. Reinforcement Learning by Stanford: Course
  3. Reinforcement Learning by University of Alberta: Course

Web Development

  1. Django Tutorial by Corey Schafer: Course
  2. Django for Everybody: Course
  3. Flask Tutorial by Corey Schafer: Course
  4. Web Development by Traversy Media: Web Link / YouTube
  5. Full Stack Web Development Guide: Tutorial
  6. Web Design for Everybody: Course
  7. Web Applications for Everybody: Course

Git & Github

  1. Crash course by Course
  2. Crash course by Traversy Media: Course
  3. Full Course by Edureka: Course
  4. Git Tutorial for Beginners by Mosh: Course
  5. Git and Github tutorial by Amigoscode: Course


  1. AWS Certifications: Tutorial
  2. AWS Tutorial for Beginners: Course
  3. AWS Basics for Beginners: Course
  4. AWS Certified Cloud Practitioner Training: Course
  5. AWS Certified Solutions Architect — Associate Training: Course
  6. AWS Certified Developer — Associate Training: Course
  7. AWS SysOps Administrator-Associate Training: Course

Model Deployment

  1. Instructor: Krish Naik
  2. Instructor: Daniel Bourke
  3. Live End-to-End Model Deployment: Tutorial
  4. Model Deployment using Amazon Sagemaker: Tutorial
  5. Model Deployment using Azure: Tutorial

Big Data

  1. Introduction to Big Data by CrashCourse: Tutorial
  2. Introduction to Big Data by Edureka: Tutorial
  3. Big Data Intro by Simplilearn: Tutorial
  4. Big Data & Hadoop by Edureka: Course
  5. Apache Spark by Edureka: Course

C/C++ Programming for Problem Solving

Tutorials & Courses

  1. Full C Tutorial by Mike: Course
  2. Full C++ Tutorial by Caleb Curry: Course
  3. Full C++ Tutorial by Suldina Nurak: Course
  4. C++ OOPS Concepts: Course
  5. Problem Solving & OOP using C++: Course
  6. Pointers in C++: Course
  7. STL using C++: Course
  8. Data Structure using C/C++: Course


  1. The C++ Programming Language by Bjarne Stroustrup: Book
  2. The C Programming Language by Dennis Ritchie: Book

Algorithms & Data Structure

  1. Introduction to Algorithms by MIT: Course
  2. Design & Analysis of Algorithms by MIT: Course
  3. Advanced Algorithms by MIT: Course
  4. Competitive Programming Guide by GeeksforGeeks: Web Link
  5. Introduction to Algorithms by Thomas H. Cormen: Book

Fundamentals of Computer Science

  1. Missing Semester of Computer Science: Course
  2. Computer System Architecture by CMU: Course
  3. Computer System Architecture by MIT: Course
  4. Operating System by Neso Academy: Course
  5. Operating System by UC Berkely: Course
  6. Basics of Software Engineering: Course

I have tried to provide specific resources (courses/tutorials/books) which are in depth, prominent on the web and have proved to be quite beneficial to a large number of learners in the data science arena. I have tried to be as specific as possible and listed those which I have familiarity with. It goes without saying, many great resources have also been left out. As such, this list should not be considered an expert guide by any means. Rather, it picks out some of the highlighted courses to make the learning journey easier for the beginners. I will finish off by providing some of the topmost YouTube channels which have tons of learning materials and some pretty good guidance in regards to the subject matter.

Top YouTube Channels for Data Science

  1. Krish Naik
  2. Sentdex
  3. 3Blue1Brown
  5. StatQuest with Joshua Starmer
  6. Python Programmer
  7. Corey Schafer
  8. Tech with Tim
  9. Two Minute Papers
  10. Data School
  11. Caleb Curry
  12. Andreas Kretz
  13. Traversy Media
  14. Stanford Online
  15. Yannic Kilcher
  16. GeeksforGeeks
  17. Numberphile
  18. DeepLearning.AI
  19. mycodeschool
  20. Art of Visualization

Machine Learning Engineer | Writer | Writing Stories with Data, Technology & Events