Self learning Data Science curriculum.
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.
- 📺 Video content.
- 💵 Paid content.
- 📰 Online article.
- 📁 GitHub repo.
- Statistics & Probability
- Linear Algebra
- Python Programming
- Numpy
- Pandas
- To-Do
- Contribution guideline
- 📺 Theoretical probability
- 📺 Sample spaces
- 📺 Set operations
- 📺 Addition rule
- 📺 Multiplication rule for independent events
- 📺 Multiplication rule for dependent events
- 📺 Conditional probability and independence
- 📺 Normal distribution and the Empirical rule
- 📺 Introduction to Sampling Distributions
- 📺 Sampling distribution of a sample proportion
- 📺 Sampling distribution of a sample mean
- 📺 Hypothesis Testing
- 📺 Error probabilities and power
- 📺 Tests about a population proportion
- 📺 Tests about a population mean
- 📺 Vectors
- 📺 Linear Combinations and Spans
- 📺 Linear Dependence and Independence
- 📺 Subspaces and the basis for a subspace
- 📺 Functions and Linear Transformations
- 📺 Transformations and Matrix Multiplications
- 📺 Inverse Functions and Transformations
- 📺 Inverses and Determinants
- 📺 Transpose of a Matrix
- 📺 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
- 📰 Hello, World!
- 📰 Variables and Types
- 📰 Lists
- 📰 Basic Operators
- 📰 String Formatting
- 📰 Basic String Operations
- 📰 Conditions
- 📰 Loops
- 📰 Functions
- 📰 Classes and Objects
- 📰 Dictionaries
- 📰 Modules and Packages
- 📰 Generators
- 📰 List Comprehensions
- 📰 Multiple Function Arguments
- 📰 Regular Expressions
- 📰 Exception Handling
- 📰 Sets
- 📰 Serialization
- 📰 Partial functions
- 📰 Code Introspection
- 📰 Closures
- 📰 Decorators
- 📰 Map, Filter, Reduce
- 📰 Python 3 Tutorial
- 📺 Introduction to Python Video 1 or Video 2
- 📰 An example
- 📰 Array Creation
- 📰 Printing Arrays
- 📰 Basic Operations
- 📰 Universal Functions
- 📰 Indexing, Slicing and Iterating
- 📰 Changing the shape of an array
- 📰 Stacking together different arrays
- 📰 Splitting one array into several smaller ones
- 📰 Indexing with Arrays of Indices
- 📰 Indexing with Boolean Arrays
- 📰 The ix_() function
- 📰 Indexing with strings
- 📰 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
- 📰 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
- 📰 Learn Pandas Tutorials | Kaggle
- 📺 Python Pandas Tutorial
- 📺 Complete Python Pandas Data Science Tutorial
- 📰 101 Pandas Exercises for Data Analysis
- 📁 pandas_exercises
- 📰 Sample plots in Matplotlib
- 📰 Customizing Matplotlib with style sheets and rcParams
- 📰 Styling with cycler
- 📰 Legend guide
- 📰 Specifying Colors
- 📰 Annotations
- 📰 Introduction to Matplotlib — Data Visualization in Python
- 📰 Python Plotting With Matplotlib (Guide)
- 📰 Matplotlib Tutorial
- 📰 Python Graph Gallery
- 📺 Python Matplotlib Tutorial | Edureka
- 📺 Matplotlib tutorial | Simplilearn
- Seaborn
- Exploratory Data Analysis (EDA)
- SQL
- Machine Learning Concepts
- Scikit-Learn
- Projects
- Translation in different language
- Cheatsheets
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Which programming languages should I use? Python and R. However, I added materials on Python.
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How to contribute? Check out contribution guidelines.
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.