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

• :tv: Video content.
• :dollar: Paid content.
• :newspaper: Online article.
• :file_folder: 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

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

Combinations and Permutations

• :tv: Counting principle and factorial
• :tv: Permutations
• :tv: Combinations

Distributions

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

Confidence Intervals

• :tv: Confidence Intervals
• :tv: Estimating Sample Proportions
• :tv: Estimating Sample Means

Hypothesis

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

Linear Algebra

Vectors and Spaces

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

Dot Product

• :tv: Vector dot and cross products

Matrix Transformations

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

Eigenvalues and Eigenvectors

• :tv: Eigenvalues and Eigenvectors

Integrals

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

Python Programming

• :newspaper: Learn Python Tutorial

Basics

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

Advanced

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

More Resources

• :newspaper: Python 3 Tutorial
• :tv: Introduction to Python Video 1 or Video 2

Numpy

Basics

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

Shape Manipulation

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

Copies and Views

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

Less Basic

• :newspaper: Broadcasting rules

Advanced indexing and index tricks

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

Linear Algebra

• :newspaper: Simple Array Operations

More Resources

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

Pandas

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

More Resources

• :newspaper: Learn Pandas Tutorials | Kaggle
• :tv: Python Pandas Tutorial
• :tv: Complete Python Pandas Data Science Tutorial
• :newspaper: 101 Pandas Exercises for Data Analysis
• :file_folder: pandas_exercises

Matplotlib

Matplotlib Official Tutorials

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

Other Resources

• :newspaper: Introduction to Matplotlib — Data Visualization in Python
• :newspaper: Python Plotting With Matplotlib (Guide)
• :newspaper: Matplotlib Tutorial
• :newspaper: Python Graph Gallery
• :tv: Python Matplotlib Tutorial | Edureka
• :tv: 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.