Hi there!!

Welcome to my Data Science Hub!

I created this hub to help beginners get started with data science. The field is oftentimes overwhelming and complex, given that it combines multiple field together. This hub offers a special data science roadmap to help guide you and links my other book-sized interactive roadmaps.

Below are 100+ free courses for learning data science, SQL, data analytics, machine learning and AI. Dive in!

The General Data Science Roadmap

1. Programming (Python)

  1. Learn the Basics: Start with Python syntax, data types, variables, basic operators, and control flow (conditions and loops).
  2. Advanced Python Concepts: Understand list comprehensions, lambda functions, classes and objects, and module imports.
  3. Libraries and Tools: Get familiar with essential libraries like NumPy for numerical data, Pandas for data manipulation, and Matplotlib for basic plotting.
  4. Practice Coding Regularly: Work on small projects or exercises to enhance your coding skills.
  5. Debugging and Testing: Learn how to use debugging tools and write tests to check the correctness of your code.
  6. Environment Management: Use virtual environments (like venv or conda) to manage dependencies.
  7. Read Python Code: Study code from others in GitHub repositories or open-source projects.
  8. Participate in Coding Challenges: Engage in platforms like LeetCode, HackerRank to improve problem-solving skills.
  9. Project: Simple Python Project: Create a basic application like a calculator or a simple game to consolidate your learning.

2. Probability, Statistics, Linear Algebra

  1. Probability Theory: Learn about probability rules, random variables, distributions, and Bayesian thinking.
  2. Descriptive Statistics: Understand measures of central tendency, dispersion, correlation, and data summarization.
  3. Inferential Statistics: Get into hypothesis testing, confidence intervals, p-values, and ANOVA.
  4. Regression Analysis: Understand linear and logistic regression models.
  5. Linear Algebra: Focus on vectors, matrices, matrix operations, and eigenvalues/vectors.