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.
Our Previous Roadmap
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
💡 Roadmap Explanation ▶️ Youtube Video 🎥
Algorithms Book Every piece of code could be called an algorithm, but this book covers the
more interesting bits.
Specializations (data structures-algorithms)
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++
:closed_book: Practical Statistics for Data Scientists
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 - Hassouna | Elzero
:video_camera: Python Full Course - FreeCodeCamp on YouTube
:closed_book: Intro to Python for CS and Data Science
more in OOP
4. Pandas
:video_camera: Corey Schafer-Youtube
:closed_book: Kaggle
:closed_book: Docs
:video_camera: Data School-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. Dashboards
Tableau
:closed_book: Tutorial
:video_camera: docs
:video_camera: course
Power BI
:video_camera: Power BI Desktop - Coursera
:video_camera: Power BI training
:video_camera: Arabic - Youtube
11. SQL and DB
:video_camera: SQL for Data Analysis (simplilearn or Udacity)
:video_camera: Intro to SQL or IBM (SQL for Data Science)
:video_camera: Intro to Relational Databases in SQL
:video_camera: Arabic Course
:video_camera: Joining Data in SQL
:pencil: Practice HackerRank & DataLemur
12. Python Regular Expression
:closed_book: Tutorial
13. 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
📹 Mathematics for Machine Learning - Most of the needed basics
🔹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 - Old 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: CS480/680 Intro to Machine Learning - Spring 2019 - University of Waterloo
:video_camera: SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)
:video_camera: Introduction to Machine Learning Course - Udacity
:video_camera: Hesham Asem - Arabic content
:video_camera: IBM ML with Python
:video_camera: Machine Learning From Scratch - YouTube (Python Engineer)
:closed_book: Hands On ML (1st & 2nd & 3rd) Editions | example code 'Notebooks'
: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
Read this book, please 📖 Introduction to Statistical Learning with Applications in R بقولك اقرأه
1. Deep Learning
:video_camera: Deep Learning Fundamentals
:video_camera: Introduction to
Deep Learning - MIT
:video_camera: Specialization
:closed_book: Dive into Deep Learning (En) | (Ar) version ➡️Part1 & Part2
: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
TensorFlow beats PyTorch in visualization capabilities and deploying trained models. Go for PyTorch if you want flexibility, debugging capabilities, and short training duration.
3. PyTorch
:video_camera: PyTorch (UC Berkeley - Youtube) - Lec3 (The 5 parts)
:video_camera: PyTorch - Dr. Data Science - Youtube
:video_camera: PyTorch Course (2022) - Youtube
:closed_book: Deep Learning With Pytorch
4. Advanced Data Science
:video_camera: Advanced Data Science with IBM Specialization
5. NLP
:video_camera: Specialization
:video_camera: Arabic - Ahmed El Sallab
:video_camera: Introduction to Natural Language Processing in Python
6. Inferential Statistics
:video_camera: Specialization, 2nd & 3rd courses
:video_camera: course
7. Bayesian Statistics
:video_camera: 1 - From Concept to Data Analysis
:video_camera: 2 - Techniques and Models
:video_camera: 3 - Mixture Models
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. ⏳
Anaconda
Git
Course - Udacity
Arabic - Youtube
📌 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
:closed_book: storytelling with data Important data visualization guide.
-
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 Science Interview Questions:
- (7) 30 days of interview preparation📖
📌 Data Analysis Recommendations.
Books (:closed_book: The Data Analysis Workshop &
:closed_book: Head First Data Analysis)
FWD - (The 3 Levels)
Google Data Analytics Professional Certificate
IBM Data Analyst Professional Certificate
Note: A good knowledge & projects in just Excel, SQL & Power BI / Tableau can bring you great opportunities
📌 Data Engineering Recommendations.
Roadmap 1
Roadmap 2
IBM Data Engineering Professional Certificate
📁
- Common mistakes by Yehia Arafa Mostafa
- CV Tips by Omar Yasser
- This Is What A GOOD Resume Should Look Like by careercup
- After you have made your beta-version resume, check those reviews from Mostafa Nageeb
- After Graduation by Yasser Alaa
- How to make Data Science Resume
- Data Science Resume Guide
- Resume/CV building for Data Jobs (Arabic)
:video_camera:Video 1
:video_camera:Video 2
📌 Data & AI Companies in Egypt - AI/ML Driven Companies In Egypt
