This repository is intended to provide a free Self-Learning Roadmap to learn the field of Data Science. I provide some of the best free resources.
If you Dont know What`s Data Science or Projects Life Cycle (starting from Business Understanding to Deployment) or Which Programming Language you should go for or Job Descriptions or the required Soft & Hard Skills needed for this field or Data Science Applications or the Most Common Mistakes, then
📌This Video is for you (Highly Recommended ✔️)
Anaconda: It’s a tool kit that fulfills all your necessities in writing and running code. From Powershell prompt to Jupyter Notebook and PyCharm, even R Studio (if interested to try R)
Atom: A more advanced Python interface, highly recommended by experts.
Google Colab: It’s like a Jupyter Notebook but in the cloud. You don’t need to install anything locally. All the important libraries are already installed. For example NumPy, Pandas, Matplotlib, and Sci-kit Learn
PyCharm: PyCharm is another excellent IDE that enables you to integrate with libraries such as NumPy and Matplotlib, allowing you to work with array viewers and interactive plots.
Thonny: Thonny is an IDE for teaching and learning programming. Thonny is equipped with a debugger, and supports code completion, and highlights syntax errors.
🔔 For Data Camp courses, github student pack gives 3 free months. Google how to get it.
if you already used it, do not hesitate to contact us to have an account with free access.:hibiscus:
- 📹 Video Content
- 📕 Online Article Content / Book
1. Descriptive Stats.
:video_camera: Intro to descriptive statistics
:closed_book: Online statistics education
:closed_book: Intro to descriptive statistics Article1 & Article2
:video_camera: Arabic Course
:video_camera: Intro to Inferential Statistics++
2. Probability
:video_camera: Khan Academy
:video_camera: Arabic Course
:closed_book: Introduction to Probability
3. Python
:video_camera: Introduction to Python Programming
:video_camera: OOP
:video_camera: Arabic Course
:video_camera: Python Full Course - FreeCodeCamp on YouTube
more in OOP
4. Pandas
:video_camera: Playlist-Youtube
:closed_book: Kaggle
:closed_book: Docs
:video_camera: Playlist-Youtube
:video_camera: Arabic Course
5. Numpy
:closed_book: Kaggle
:video_camera: Arabic Course
:closed_book: Tutorial
:closed_book: Docs
6. Scipy
:closed_book: Tutorial
:closed_book: Docs
7. Data Cleaning: One of the MOST important skills that you need to master to become a good data scientist, you need to practice on many datasets to master it.
Read this
:video_camera: Course 1
:closed_book: Notebook1
:closed_book: Notebook2
:closed_book: Notebook3
:closed_book: Kaggle Data cleaning
8. Data Visualization 📊
:video_camera: Introduction to Data Visualization with Matplotlib or
:video_camera: Corey Schafer - Playlist on Youtube or
:video_camera: sentdex - Playlist on YouTube
:closed_book: Kaggle to Data Visualization with Seaborn
:video_camera: Playlist-Youtube
:video_camera: Course1: Intro to Data Visualization with Seaborn
:video_camera: Course2: Intermediate Data Visualization with Seaborn
:video_camera: Course3: Understanding and Visualizing with Python
9. EDA
Note: it's already mentioned in the above probability course
:video_camera: DataCamp-EDA in Python
:video_camera: IBM-EDA for Machine Learning
10. SQL and DB
:video_camera: Intro to SQL or IBM
:video_camera: Intro to Relational Databases in SQL
:video_camera: Arabric Course
:video_camera: Joining Data in SQL
11. Python Regular Expression
:closed_book: Tutorial
12. Time Series Analysis
:video_camera: Track
:closed_book: Book
:closed_book: fbprohet
:video_camera: Arabic Source Video1 & Video2
1. Math for ML: consists of Linear Algebra, Calculus and PCA.
Specialization
🔹Linear Algebra
:video_camera: Khan Academy - Linear Algebra
:video_camera: Mathematics for Machine Learning: Linear Algebra
:video_camera: 3Blue1Brown - Essence of Linear Algebra
🔹Calculus
:video_camera: Multivariate Calculus - Coursera
:video_camera: Essence of calculus - Youtube
🔹PCA
:video_camera: PCA - Coursera
2. Machine Learning
:video_camera: Coursera Free Course by Andrew Ng (Octave/Matlab)
:video_camera: Coursera Andrew`s new ML Specialization (Python)
:video_camera: Machine Learning Stanford Full Course on YouTube by Andrew
:video_camera: Introduction to Machine Learning Course - Udacity
:video_camera: IBM ML with Python
:video_camera: Machine Learning From Scratch - YouTube (Python Engineer)
:closed_book: Hands on ML book
:video_camera: ML Algorithms in Practice
:video_camera: ML scientist
:video_camera: Project
3. Web Scraping/APIs
:video_camera: course
:closed_book: intro2
:closed_book: Tutorial
:closed_book: Book for both topics
APIs
:closed_book: Tutorial
:closed_book: Article
:closed_book: Tutorial
4. Stats.
:closed_book: This stats - Book
:closed_book: Think Bayes - Book
5. Advanced SQL
:video_camera: More advanced SQL
:video_camera: Joining Data in SQL
7. Feature Engineering
:closed_book: Tutorial
:closed_book: Article
:closed_book: Book
8. interpet Shapley-based explanations of ML models.
:closed_book: SHAP
:closed_book: Kaggle ML explainability
1. Deep Learning
:video_camera: Deep Learning Fundamentals
:video_camera: Introduction to
Deep Learning - MIT
:video_camera: Specialization
:closed_book: Dive into Deep Learning - Book
:video_camera: Deep Learning UC Berkely
:closed_book: github of Dive into DL
:video_camera: Stanford Lecture - Convolutional Neural Networks for Visual Recognition
:video_camera: University of Waterloo - ML / DL
2. Tensorflow
:video_camera: Specialization
:video_camera: Youtube
fast.ai's Deep Learning Courses
3. Advanced Data Science
:video_camera: Advanced Data Science with IBM Specialization
4. NLP
:video_camera: Specialization
:video_camera: Introduction to Natural Language Processing in Python
5. Inferential Statistics
:video_camera: Specialization, 2nd & 3rd courses
:video_camera: course
6. Bayesian Statistics
:video_camera: 1 - From Concept to Data Analysis
:video_camera: 2 - Techniques and Models
:video_camera: 3 - Mixture Models
7. Tableau
:closed_book: Tutorial
:video_camera: docs
:video_camera: course
8. Model Deployment
:closed_book: Flask tutorial
:video_camera: TensorFlow: Data and Deployment Specialization
:video_camera: Deploy Models with TensorFlow Serving and Flask
:video_camera: How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke
if you`re intersted in more deployment methods, search for (FastAPI - Heroku - chitra)
9. Probabilistic Graphical Models
:video_camera: Specialization
Tasks and Projects will be added soon. ⏳
📌 More Books ~ 📌 Check This!
:atom::atom::atom::atom::atom:
:closed_book: 🔥 65 Free Important Books 🔥
:closed_book: Mathematics for Machine Learning
:closed_book: An Introduction to Statistical Learning
:closed_book: Understanding Machine Learning: From Theory to Algorithms
:closed_book: Probabilistic Machine Learning: An Introduction
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Pandas
Competitions will make you even more proficient in Data Science.
When we talk about top data science competitions, Kaggle is one of the most popular platforms for data science. Kaggle has a lot of competitions where you can participate according to your knowledge level.
You can also check these platforms for data science competitions-
- Driven Data
- Codalab
- Iron Viz
- Topcoder
- CrowdANALYTIX Community
- Bitgrit
📌 Data Analysis Recommendations.
FWD - (The 3 Levels)
Google Data Analytics Professional Certificate
IBM Data Analyst Professional Certificate
📌 Data Engineering Recommendations.
Roadmap 1
Roadmap 2
📌 Data & AI Companies in Egypt
