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 Statistics 
📹 Intro to descriptive statistics | Same Course on YouTube
📹 Statistics Fundamentals - StatQuest - Youtube
📕 Online statistics education
📕 Intro to descriptive statistics Article1 & Article2
📹 Arabic Course
📹 Intro to Inferential Statistics++
📕 Practical Statistics for Data Scientists
2. Probability
📹 Khan Academy
📹 Arabic Course
📹 Probability and Statistics for AI and DS - Arabic (Dr.Hatem Elattar)
📕 Introduction to Probability
3. Programming Languages
🔹R - good tool for visualization and statistical analysis.
📹 Introduction to R (Datacamp)
📹 Data Science Specialization - coursera
📕 An Introduction to R
📕 R for Data Science
🔹Python💯
📹 Introduction to Python Programming
📹 OOP
📹 Arabic - Hassouna | Elzero
📹 Python Full Course - FreeCodeCamp on YouTube
📕 Intro to Python for CS and Data Science
more in OOP
4. Pandas
📹 Corey Schafer-Youtube
📕 Kaggle
📕 Docs
📹 Data School-Youtube
📹 Arabic Course
📹 PandasAI🐼1 - 2 Enhances the capabilities of Pandas by integrating Generative AI functionalities into it.
5. Numpy
📕 Kaggle 
📹 Arabic Course
📕 Tutorial
📕 Docs
6. Scipy
📕 Tutorial
📕 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
📹 Course 1
📕 Notebook1
📕 Notebook2
📕 Notebook3
📕 Kaggle Data cleaning
8. Data Visualization 📊
📹 Introduction to Data Visualization with Matplotlib or
📹 Corey Schafer - Playlist on Youtube or
📹 sentdex - Playlist on YouTube
📕 Kaggle to Data Visualization with Seaborn
📹 Playlist-Youtube
📹 Course1: Intro to Data Visualization with Seaborn
📹 Course2: Intermediate Data Visualization with Seaborn
📹 Course3: Understanding and Visualizing with Python
9. EDA
Note: it's already mentioned in the above probability course
📹 DataCamp-EDA in Python
📹 IBM-EDA for Machine Learning
10. Dashboards
Power BI
📹 Power BI - Youtube (Alex)
📹 Power BI training
📹 Arabic - Youtube (Zanoon)
📹 Arabic - Youtube
📹 Guy in a Cube - Youtube
Tableau 
📕 Tutorial
📹 docs
📹 course - datacamp
📹 Simplilearn - Youtube
11. SQL and DB
📹 SQL for Data Analysis (Udacity-notesl📋l or simplilearn)
📹 Intro to SQL or IBM (SQL for Data Science)
📹 Intro to Relational Databases in SQL
📹 Arabic Course (Theoritical - Practical) Eldesouki
📹 Arabic - ITI by Eng.Ramy Advanced - [Notes]📋- [Course Materials]
📹 Arabic - SQL for Data Analysis by Ahmed Sami
📹 365 Data Science - SQL
📹 CMU Intro to DB - Fall 2022 - <Schedule📅> - Book📕
📕 SQL for Data Analysis
📝 Practice HackerRank & DataLemur
12. DWH : A system used for reporting - A core component of business intelligence.
Mostly used by Data Engineers.
📕 The Data Warehouse Toolkit
📹 Data Warehousing Tutorial Videos
📹 Garage Education (Ar)
📹 Implementing Data Warehouse in Arabic (Ar)
📹 More in Arabic? (Ar)
📹 Data Warehouse - University of Colorado
📹 [SSIS] SQL Server Integration Services (Ar)
📹 Project - Building Sales Data Mart Using SSIS (Ar)
📹 Project - Building DWH Step by Step
📹 Project - Create DWH Fact and Dimensions (Ar)
📹 Implement SCD in SSIS Continue the playlist
📹 CDC in SSIS tutorial
13. Python Regular Expression
📕 Tutorial
14. Time Series Analysis
📹 Track - DataCamp
📹 Course - Coursera
📕 Book
📕 fbprohet
📹 Arabic Source Video1 & Video2
1. Math for ML: consists of Linear Algebra, Calculus and PCA.
📹 Mathematics for Machine Learning and Data Science - Andrew Ng
📹 Specialization
📹 Mathematics for Machine Learning - Most of the needed basics
🔹Linear Algebra
📹 Khan Academy - Linear Algebra
📹 Mathematics for Machine Learning: Linear Algebra
📹 3Blue1Brown - Essence of Linear Algebra
🔹Calculus
📹 Multivariate Calculus - Coursera
📹 Essence of calculus - Youtube
🔹PCA
📹 PCA - Coursera
2. Machine Learning
📹 Coursera - Old Course by Andrew Ng (Octave/Matlab)
📹 Coursera Andrew`s new ML Specialization (Python)
📹 Machine Learning - StatQuest - YouTube
📹 Machine Learning Stanford Full Course on YouTube by Andrew
📹 CS480/680 Intro to Machine Learning - Spring 2019 - University of Waterloo
📹 SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)
📹 Machine Learning for Engineers 2022 / (YouTube)
📹 Introduction to Machine Learning Course - Udacity
📹 Hesham Asem - Arabic content
📹 IBM ML with Python
📹 Machine Learning From Scratch - YouTube (Python Engineer)
📕 Hands On ML (1st & 2nd & 3rd) Editions | Code:
📹 ML Algorithms in Practice
📹 ML scientist
📹 Project
3. Web Scraping/APIs
📹 course
📕 intro2
📕 Tutorial
📕 Book for both topics
APIs
📕 Tutorial
📕 Article
📕 Tutorial
4. Stats.
📕 This stats - Book
📕 Think Bayes - Book
5. Advanced SQL
📹 Joining Data in SQL - DataCamp
📹 Intermediate SQL - DataCamp
📹 More advanced SQL
7. Feature Engineering
📕 Tutorial
📕 Article
📕 Book
8. interpret Shapley-based explanations of ML models.
📕 SHAP
📕 Kaggle ML explainability
Read this book, please 📖 Introduction to Statistical Learning with Applications in R بقولك اقرأه
1. Deep Learning
📹 Deep Learning Fundamentals
📹 Introduction to
Deep Learning - MIT
📹 Specialization
📕 Dive into Deep Learning (En) | (Ar) version ➡️Part1 & Part2
📹 Deep Learning UC Berkely
📕 github of Dive into DL
📹 Stanford Lecture - Convolutional Neural Networks for Visual Recognition
📹 University of Waterloo - ML / DL
📕 Deep Learning for coders with fastai & PyTorch
2. Tensorflow
📹 Specialization
📹 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
📹 PyTorch (UC Berkeley - Youtube) - Lec3 (The 5 parts)
📹 PyTorch - Dr. Data Science - Youtube
📹 Pytorch Tutorial - Aladdin - Youtube
📹 PyTorch Course (2022) - Youtube
📕 Deep Learning With Pytorch
📕 Machine Learning with PyTorch and Scikit-Learn -2022
4. Advanced Data Science
📹 Advanced Data Science with IBM Specialization Includes Apache Spark
☠️Advanced ML Topics🧠 | Lecs (YouTube)
📹 Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022 - Materials
📹 18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
☠️ML based Computer Vision | Lecs (YouTube)
📹 CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)
📹 NOC:Deep Learning For Visual Computing - IIT Kharagpur
📹 Deep Learning for Computer Vision - Michigan
5. NLP
📹 Specialization - Coursera
📹 Arabic - Ahmed El Sallab
📹 Stanford CS224N Lectures - Winter 2021- YouTube
📹 Stanford XCS224U Lectures - Spring 2021- YouTube
📹 Introduction to Natural Language Processing in Python
🔸LLMS What`s Large Language Model?
📹 Generative AI for Everyone (Andrew Nj) - Coursera🆕
📹 Generative AI with LLMs
📹 Stanford CS236: Deep Generative Models I 2023 - YouTube
📹 Stanford CS25 - Transformers United 2023 - YouTube
📹 Recent Advances on Foundation Models - Winter 2024 - University of Waterloo
📹 Understanding LLMs Foundations and Safety UC Berkeley - Spring 2024 - YouTube
📹 LLM Foundations
📹 How ChatGPTs / Transformers work?1 - 2 - 3 overview & Maths behind
📹 Prompt Engineering | (Ar) If you want to get the most out of LLMs
📹 LLMOps A Lec going through the entire LLM pipeline
6. Inferential Statistics
📹 Specialization, 2nd & 3rd courses
📹 course
7. Bayesian Statistics
📹 1 - From Concept to Data Analysis
📹 2 - Techniques and Models
📹 3 - Mixture Models
8. Model Deployment
📕 Flask tutorial
📹 TensorFlow: Data and Deployment Specialization
📹 Deploy Models with TensorFlow Serving and Flask
📹 How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke
if you`re interested in more deployment methods, search for (FastAPI - Heroku - chitra)
9. MLOps : is a combination of Model Deployment, Model Serving, Model Monitoring, and Model Maintenance.
🔗 MLOps-zoomcamp
🔗 MLOps-guide
📕 Practical MLOps
10. Probabilistic Graphical Models
📹 Specialization - Coursera
📹 Spring 2016, University of Utah - YouTube
🌟 Read these books, they will be beneficial to you.
📖 Bayesian Reasoning and Machine Learning
📖 The Elements of Statistical Learning
📖 Pattern Recognition and Machine Learning - Bishop (Advanced)
Recommended by Eng.Mohamed Hammad.
🎥Deena Gergis - End to end Project
🎥Machine Learning Projects - Youtube
💻Top 10 Data Science Projects for Beginners
💻12 Data Science Projects for Beginners and Experts
💻Data Science Projects & Ideas
💻Top 310+ Machine Learning Projects for 2023
💻10 End-to-End Guided Data Science Projects
🎥Real-World ML Tutorial w/ Scikit Learn
💻Python Codes in Data Science
🎥End To End ML Project With Dockers,Github Actions And Deployment
💻12 free Data Science projects to practice Python and Pandas (resolve interactive online)
| English | Arabic | Book |
|---|---|---|
| 🎥 Git - Udacity | 🎥 شخبط وانت مطمن 🚀 | 📕 Pro Git |
| 📖 w3schools | 🎥 almadrasa | |
| 🎥 Elzero |
📌 More Books 
📌 Check This!
📕 🔥 12 Free Important Books 🔥
📕 Mathematics for Machine Learning
📕 An Introduction to Statistical Learning
📕 Understanding ML: From Theory to Algorithms
📕 Probabilistic Machine Learning: An Introduction
📕 storytelling with data ✔️Important data visualization guide.
-
Pandas
-
Machine Learning Cheat Sheets (Recommended Guide) راجع المواضيع اللي في الشيت دي يا عزيزي وشوف اللي ناقصك
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 Science Podcasts: 🎙️
The Best Way to Stay Up-to-Date on the Latest Data Science Trends and Developments
| Podcasts | About | Produced by |
|---|---|---|
| Data Science at Home | A podcast that provides practical advice and tutorials on data science topics. | Greg Linhardt, a data scientist and machine learning engineer at Google AI |
| Data Stories | An interview-driven podcast that tells the stories of data scientists and how they're using their skills to make a difference in the world. | Kirill Eremenko, a data scientist and machine learning engineer at Netflix |
| O'Reilly Data Show | A podcast that covers a wide range of data science topics, from machine learning to artificial intelligence to big data. | Ben Lorica, the Chief Data Scientist at O'Reilly |
| Learning Machines 101 | Mathematics, statistics, and algorithms that power the machine learning systems that we rely on every day. | Richard Golden, a machine learning engineer and researcher at Google AI |
| Data Engineering Podcast | Tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation. | Tobias Macey, a data engineer at Netflix |
| Data Science Mixer | A great resource for anyone who wants to learn more about data science and the latest trends in the field. It is also a great way to get inspired by the work of other data scientists and machine learning engineers. | Alteryx, a data science and analytics software company |
| Chai Time Data Science Show | Interviews top data scientists, practitioners, and researchers from around the world. | Sanyam Bhutani, a data scientist and machine learning engineer at Google AI. |
| Becoming a Data Scientist | Podcast that interviews data scientists about their journey to becoming a data scientist. | Renee Teate, a data scientist and machine learning engineer at Google AI. |
| AI Today Podcast | Explores the latest trends and developments in artificial intelligence. | Ron Schmelzer and Kathleen Walch |
| Gradient Dissent | A weekly podcast that explores the latest research in machine learning and artificial intelligence. | Chris Olah, a machine learning engineer at Google AI |
| Data Skeptic | A podcast that challenges the conventional wisdom in data science and asks tough questions about the ethics and implications of data-driven decision making. | Kyle Polich, a data scientist and machine learning engineer |
| Linear Digressions | A podcast that covers a wide range of data science topics, from the technical to the theoretical. | Ben Recht and Noah Smith, two machine learning researchers at the University of California, Berkeley |
| The Data Engineering Show | For data engineering and BI practitioners to go beyond theory, and learn from the biggest influencers in tech about their practical day to day data challenges. | Eldad Farkash and Benjamin Wagner, who are both data engineering experts with experience at companies like Firebolt and Sisense |
| DataTalks.Club | A weekly online community of data enthusiasts and practitioners that learn from each other and share their knowledge and experiences through meetups, workshops, and a podcast. | A rotating cast of data experts |
| Datacast | Top data scientists and practitioners in the data and AI infrastructure space. | James Le, who is a data infrastructure expert with experience at companies like Google and Netflix |
| How to Get an Analytics Job Podcast | A great resource for anyone who is interested in a career in analytics. The guests share their insights and advice on how to get started in analytics and how to succeed in an analytics career. | John David Ariansen, an analytics agency owner and career coach |
| The Analytics Power Hour | Five awesome people, an occasional guest, and drinks all around tackling the hottest data and analytics topics of the day. | Tim Wilson, Michael Helbling, Josh Crowhurst, and Val Kroll. They are all analytics experts from different companies |
👀 Arabic Podcasts??
شايفك ياللي زهقان في المواصلات 📻Arabic Data Podcast | Spotify by Eng. Kareem Abdelsalam
📻lإلي البيانات وما بعدها by Eng. Youssef Hosni
📻Garage Education by Eng. Mostafa Alaa
📻Data Science بالعربي
📌 Data Analysis Recommendations.
Books (📕 The Data Analysis Workshop &
📕 Head First Data Analysis)
FWD - (The 3 Levels)
Google Data Analytics Professional Certificate
IBM Data Analyst Professional Certificate
Google Advanced Data Analytics Professional Certificate 🆕
Alex The Analyst - YouTube📺
Note: A good knowledge & projects in just Excel, SQL & Power BI / Tableau can bring you great opportunities.
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Excel More Resources: (Arabic 1📹 - Arabic 2📹 - Books 📄 and cheat sheets for revising)
📌 Data Engineering Recommendations.
Books (📕 Fundamentals of Data Engineering &
📕 Designing Data-Intensive Applications)
Arabic Podcast, Starting a Career in Data Engineering.
For Arab, I recommend 2 YouTube Channels: (Garage Education & Big Data بالعربي)
Roadmap 1 - (Recommended)
Roadmap 2
Roadmap 3
IBM Data Engineering Professional Certificate
Note: A good knowledge & projects in SQL, Python, Apache Spark/Hadoop, Data Modeling and [Data Warehouse - {Arabic-Starting from the 7th video} can bring you great opportunities. Start with them then go for the other tools,concepts and cloud platforms.
📁 CV / Resumes 📝
- 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 1
📹Video 2
📌 Data & AI Companies in Egypt - AI/ML Driven Companies In Egypt
