fahad021 / data-science-roadmap

Roadmap to learn Data Science and related areas.

Data Science Roadmap

Self learning Data Science curriculum.

About

This repository intendend to provide a complete Data Science learning path to those who intersted in learning Data Science. In this repository, I gave preference to free resource. However, some valuable paid courses also included.

Explanation

• 📺 Video content.
• 💵 Paid content.
• 📰 Online article.
• 📁 GitHub repo.

Content

• Statistics & Probability
  • Descriptive Statistics
  • Probability
  • Combinations and Permutations
  • Distributions
  • Confidence Intervals
  • Hypothesis
• Linear Algebra
  • Vectors and Spaces
  • Dot Product
  • Matrix Transformations
  • Eigenvalues and Eigenvectors
  • Integrals
• Python Programming
• Numpy
• Pandas
• To-Do
• Contribution guideline

Statistics & Probability

Descriptive Statistics

• Intro to Descriptive Statistics Article 1 or Article 2

Probability

• 📺 Theoretical probability
• 📺 Sample spaces
• 📺 Set operations
• 📺 Addition rule
• 📺 Multiplication rule for independent events
• 📺 Multiplication rule for dependent events
• 📺 Conditional probability and independence

Combinations and Permutations

• 📺 Counting principle and factorial
• 📺 Permutations
• 📺 Combinations

Distributions

• 📺 Normal distribution and the Empirical rule
• 📺 Introduction to Sampling Distributions
• 📺 Sampling distribution of a sample proportion
• 📺 Sampling distribution of a sample mean

Confidence Intervals

• 📺 Confidence Intervals
• 📺 Estimating Sample Proportions
• 📺 Estimating Sample Means

Hypothesis

• 📺 Hypothesis Testing
• 📺 Error probabilities and power
• 📺 Tests about a population proportion
• 📺 Tests about a population mean

Linear Algebra

Vectors and Spaces

• 📺 Vectors
• 📺 Linear Combinations and Spans
• 📺 Linear Dependence and Independence
• 📺 Subspaces and the basis for a subspace

Dot Product

• 📺 Vector dot and cross products

Matrix Transformations

• 📺 Functions and Linear Transformations
• 📺 Transformations and Matrix Multiplications
• 📺 Inverse Functions and Transformations
• 📺 Inverses and Determinants
• 📺 Transpose of a Matrix

Eigenvalues and Eigenvectors

• 📺 Eigenvalues and Eigenvectors

Integrals

• 📺 Approximation with Riemann Sums
• 📺 Definite Integrals with Riemann Sums
• 📺 The Fundamental Theorem of Calculus and Accumulation Functions
• 📺 Properties of Definite Integrals
• 📺 The Fundamental Theorem of Calculus and Definite Integrals
• 📺 Reverse Power Rule
• 📺 Indefinite Integrals of Common Functions
• 📺 Definite Integrals of Common Functions

Python Programming

• 📰 Learn Python Tutorial

Basics

• 📰 Hello, World!
• 📰 Variables and Types
• 📰 Lists
• 📰 Basic Operators
• 📰 String Formatting
• 📰 Basic String Operations
• 📰 Conditions
• 📰 Loops
• 📰 Functions
• 📰 Classes and Objects
• 📰 Dictionaries
• 📰 Modules and Packages

Advanced

• 📰 Generators
• 📰 List Comprehensions
• 📰 Multiple Function Arguments
• 📰 Regular Expressions
• 📰 Exception Handling
• 📰 Sets
• 📰 Serialization
• 📰 Partial functions
• 📰 Code Introspection
• 📰 Closures
• 📰 Decorators
• 📰 Map, Filter, Reduce

More Resources

• 📰 Python 3 Tutorial
• 📺 Introduction to Python Video 1 or Video 2

Numpy

Basics

• 📰 An example
• 📰 Array Creation
• 📰 Printing Arrays
• 📰 Basic Operations
• 📰 Universal Functions
• 📰 Indexing, Slicing and Iterating

Shape Manipulation

• 📰 Changing the shape of an array
• 📰 Stacking together different arrays
• 📰 Splitting one array into several smaller ones

Copies and Views

• 📰 No Copy at All
• 📰 View or Shallow Copy
• 📰 Deep Copy
• 📰 Functions and Methods Overview

Less Basic

• 📰 Broadcasting rules

Advanced indexing and index tricks

• 📰 Indexing with Arrays of Indices
• 📰 Indexing with Boolean Arrays
• 📰 The ix_() function
• 📰 Indexing with strings

Linear Algebra

• 📰 Simple Array Operations

More Resources

• 📰 NumPy: the absolute basics for beginners
• 📰 NumPy Tutorial
• 📰 The Ultimate Beginner’s Guide to NumPy
• 📰 The Ultimate NumPy Tutorial for Data Science Beginners
• 📰 NumPy Tutorial: Your First Steps Into Data Science in Python
• 📰 101 NumPy Exercises for Data Analysis (Python)
• 📺 Complete Python NumPy Tutorial
• 📁 Python Numpy Tutorial (with Jupyter and Colab)
• 📁 100 numpy exercises

Pandas

• 📰 10 minutes to pandas
• 📰 Intro to data structures
• 📰 Essential basic functionality
• 📰 IO tools
• 📰 Indexing and selecting data
• 📰 MultiIndex / advanced indexing
• 📰 Merge, join, concatenate and compare
• 📰 Reshaping and pivot tables
• 📰 Working with text data
• 📰 Duplicate Labels
• 📰 Categorical data
• 📰 Nullable integer data type
• 📰 Nullable Boolean data type
• 📰 Visualization using pandas
• 📰 Computational tools
• 📰 Group by: split-apply-combine
• 📰 Windowing Operations
• 📰 Time series / date functionality
• 📰 Time deltas
• 📰 Styling
• 📰 Options and settings
• 📰 Cookbook

More Resources

• 📰 Learn Pandas Tutorials | Kaggle
• 📺 Python Pandas Tutorial
• 📺 Complete Python Pandas Data Science Tutorial
• 📰 101 Pandas Exercises for Data Analysis
• 📁 pandas_exercises

Matplotlib

Matplotlib Official Tutorials

• 📰 Sample plots in Matplotlib
• 📰 Customizing Matplotlib with style sheets and rcParams
• 📰 Styling with cycler
• 📰 Legend guide
• 📰 Specifying Colors
• 📰 Annotations

Other Resources

• 📰 Introduction to Matplotlib — Data Visualization in Python
• 📰 Python Plotting With Matplotlib (Guide)
• 📰 Matplotlib Tutorial
• 📰 Python Graph Gallery
• 📺 Python Matplotlib Tutorial | Edureka
• 📺 Matplotlib tutorial | Simplilearn

To-Do

• Seaborn
• Exploratory Data Analysis (EDA)
• SQL
• Machine Learning Concepts
• Scikit-Learn
• Projects
• Translation in different language
• Cheatsheets

FAQ

1. Which programming languages should I use? Python and R. However, I added materials on Python.

2. How to contribute? Check out contribution guidelines.

Contribution guideline

You can open an issue and give your suggestions as to how I can improve this guide, or what I can do to improve the learning experience.

You can also fork this repo and send a pull request to fix any mistakes that you have found.

If you want to suggest a new resource, send a pull request adding such resource to the extras section. The extras section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations.