Skip to content

Moataz-Elmesmary/Data-Science-Roadmap

Folders and files

NameName
Last commit message
Last commit date

Latest commit

ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 

Repository files navigation

โ€ƒโ€ƒโ€ƒโ€ƒ DATA SCIENCE ROADMAP ๐Ÿดโ€โ˜ ๏ธ 2024

Data Science Roadmap for anyone interested in how to break into the field!

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.


โ€ƒโ€ƒOur Previous Roadmap โ™ฅ๏ธ
โ€ƒโ€ƒ โš ๏ธ Before we start, โš ๏ธ

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

๐Ÿ“ŒThis Video is for you (Highly Recommended โœ”๏ธ)

Data Science vs Data Analytics vs Data Engineering - What's the Difference?


aaa

These terms are wrongly used interchangeably among people. There are distinct differences:

๐Ÿ”ธ Data Science ๐Ÿ”ธ Data Analytics ๐Ÿ”ธ Data Engineering
Is a multidisciplinary field that focuses on looking at raw and structured data sets and providing potential actionable insights. The field of Data Science looks at ensuring we are asking the right questions as opposed to finding exact answers. Data Scientist require skillsets that are centered on Computer Science, Mathematics, and Statistics. Data Scientist use several unique techniques to analyze data such as machine learning, trends, linear regressions, and predictive modeling. The tools Data Scientist use to apply these techniques include Python and R.
Focuses on looking at existing data sets and creating solutions to capture data, process data, and finally organize data to draw actionable insights. This field looks at finding general process, business, and engineering improvements we can make based on questions we don't know the answers to. Data Analytics require skillsets that are centered on Statistics, Mathematics, and high level understanding of Computer Science. It involves data cleaning, data visualization, and simple modeling. Common Data Analytic tools used include Microsoft Power Bi, Tableau, and SQL.
Focuses on creating the correct infrastructure and tools required to support the business. Data Engineers look at what are the optimal ways to store and extract data and involves writing scripts and building data warehouses. Data Engineering require skillsets that are centered on Software Engineering, Computer Science and high level Data Science. The tools Data Engineers utilize are mainly Python, Java, Scala, Hadoop, and Spark.

Prepare your workspace

Tip 1๏ธโƒฃ : Pick one and stick to it. (๐Ÿ“Click)


Anaconda: Itโ€™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)

a

Atom: A more advanced Python interface, highly recommended by experts.
Google Colab: Itโ€™s like a Jupyter Notebook but in the cloud. You donโ€™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.

Most learning platforms have integrated code exercises where you donโ€™t need to install anything locally. But to learn it right, you should have an IDE installed on your local machine. Suggestions will be a marketplace with many options and few improvements from one platform to another.

Tip 2๏ธโƒฃ : Focus on one course at least.

Tip 3๏ธโƒฃ : Donโ€™t chase certifications.

Tip 4๏ธโƒฃ : Donโ€™t rush for ML without having a good background in programming & maths.

This track is divided into 3 phases โฌ‡๏ธ :

โ€ƒ 1. Beginner: you get a basic understanding of data analysis, tools and techniques.

โ€ƒ 2. Intermediate: dive deeper in more complex topics of ML, Math and data engineering.

โ€ƒ 3. Advanced: where we learn more advanced Math, DL and Deployment.

๐Ÿ”” 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:

Legend

  • ๐Ÿ“น Video Content
  • ๐Ÿ“• Online Article Content / Book

๐Ÿ’ก Roadmap Explanation โ–ถ๏ธ Youtube Video ๐ŸŽฅ


๐Ÿ”ฐ Beginner ๐Ÿ”ฐ

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 Statistics
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Intro to descriptive statistics | Same Course on YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Statistics Fundamentals - StatQuest - Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Online statistics education
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Intro to descriptive statistics Article1 & Article2
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic Course
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Intro to Inferential Statistics++
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Practical Statistics for Data Scientists

2. Probability
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Khan Academy
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic Course
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Probability and Statistics for AI and DS - Arabic (Dr.Hatem Elattar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Introduction to Probability

3. Programming Languages

โ€ƒ๐Ÿ”นR - good tool for visualization and statistical analysis.
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Introduction to R (Datacamp)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Data Science Specialization - coursera
โ€ƒโ€ƒโ€ƒ๐Ÿ“• An Introduction to R
โ€ƒโ€ƒโ€ƒ๐Ÿ“• R for Data Science

โ€ƒ๐Ÿ”นPython๐Ÿ’ฏ
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Introduction to Python Programming
โ€ƒโ€ƒโ€ƒ๐Ÿ“น OOP
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic - Hassouna | Elzero
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Python Full Course - FreeCodeCamp on YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Intro to Python for CS and Data Science
โ€ƒโ€ƒโ€ƒmore in OOP
4. Pandas
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Corey Schafer-Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Kaggle
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Docs
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Data School-Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic Course
โ€ƒโ€ƒโ€ƒ๐Ÿ“น PandasAI๐Ÿผ1 - 2 Enhances the capabilities of Pandas by integrating Generative AI functionalities into it.
5. Numpy
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Kaggle โ€ƒnumpy
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic Course
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Docs
6. Scipy
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“• 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.
โ€ƒโ€ƒโ€ƒRead this
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Course 1
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Notebook1
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Notebook2
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Notebook3
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Kaggle Data cleaning
8. Data Visualization ๐Ÿ“Š
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Introduction to Data Visualization with Matplotlib or
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Corey Schafer - Playlist on Youtube or
โ€ƒโ€ƒโ€ƒ๐Ÿ“น sentdex - Playlist on YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Kaggle to Data Visualization with Seaborn
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Playlist-Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Course1: Intro to Data Visualization with Seaborn
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Course2: Intermediate Data Visualization with Seaborn
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Course3: Understanding and Visualizing with Python

9. EDA Note: it's already mentioned in the above probability course
โ€ƒโ€ƒโ€ƒ๐Ÿ“น DataCamp-EDA in Python
โ€ƒโ€ƒโ€ƒ๐Ÿ“น IBM-EDA for Machine Learning

10. Dashboards

โ€ƒPower BI
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Power BI - Youtube (Alex)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Power BI training
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic - Youtube (Zanoon)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic - Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Guy in a Cube - Youtube
โ€ƒTableau tableau
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“น docs
โ€ƒโ€ƒโ€ƒ๐Ÿ“น course - datacamp
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Simplilearn - Youtube

11. SQL and DB
โ€ƒโ€ƒโ€ƒ๐Ÿ“น SQL for Data Analysis (Udacity-notesl๐Ÿ“‹l or simplilearn)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Intro to SQL or IBM (SQL for Data Science)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Intro to Relational Databases in SQL
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic Course (Theoritical - Practical) Eldesouki
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic - ITI by Eng.Ramy Advanced - [Notes]๐Ÿ“‹- [Course Materials]
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic - SQL for Data Analysis by Ahmed Sami
โ€ƒโ€ƒโ€ƒ๐Ÿ“น 365 Data Science - SQL
โ€ƒโ€ƒโ€ƒ๐Ÿ“น CMU Intro to DB - Fall 2022 - <Schedule๐Ÿ“…> - Book๐Ÿ“•
โ€ƒโ€ƒโ€ƒ๐Ÿ“• SQL for Data Analysis
โ€ƒโ€ƒโ€ƒ๐Ÿ“ Practice HackerRank & DataLemur

12. DWH : A system used for reporting - A core component of business intelligence.
โ€ƒโ€ƒโ€ƒโ€ƒ Mostly used by Data Engineers.
โ€ƒโ€ƒโ€ƒ๐Ÿ“• The Data Warehouse Toolkit
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Data Warehousing Tutorial Videos
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Garage Education (Ar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Implementing Data Warehouse in Arabic (Ar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น More in Arabic? (Ar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Data Warehouse - University of Colorado
โ€ƒโ€ƒโ€ƒ๐Ÿ“น [SSIS] SQL Server Integration Services (Ar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Project - Building Sales Data Mart Using SSIS (Ar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Project - Building DWH Step by Step
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Project - Create DWH Fact and Dimensions (Ar)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Implement SCD in SSIS Continue the playlist
โ€ƒโ€ƒโ€ƒ๐Ÿ“น CDC in SSIS tutorial

13. Python Regular Expression
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
14. Time Series Analysis
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Track - DataCamp
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Course - Coursera
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Book
โ€ƒโ€ƒโ€ƒ๐Ÿ“• fbprohet
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic Source Video1 & Video2

At The end of the Beginner phase apply what you've learned on a project.


๐Ÿ”ฐ Intermediate ๐Ÿ”ฐ

1. Math for ML: consists of Linear Algebra, Calculus and PCA.
๐Ÿ“น Mathematics for Machine Learning and Data Science - Andrew Ng
๐Ÿ“น Specialization
๐Ÿ“น Mathematics for Machine Learning - Most of the needed basics

๐Ÿ”นLinear Algebra
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Khan Academy - Linear Algebra
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Mathematics for Machine Learning: Linear Algebra
โ€ƒโ€ƒโ€ƒ๐Ÿ“น 3Blue1Brown - Essence of Linear Algebra
๐Ÿ”นCalculus
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Multivariate Calculus - Coursera
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Essence of calculus - Youtube
๐Ÿ”นPCA
โ€ƒโ€ƒโ€ƒ๐Ÿ“น PCA - Coursera

2. Machine Learning
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Coursera - Old Course by Andrew Ng (Octave/Matlab)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Coursera Andrew`s new ML Specialization (Python)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Machine Learning - StatQuest - YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Machine Learning Stanford Full Course on YouTube by Andrew
โ€ƒโ€ƒโ€ƒ๐Ÿ“น CS480/680 Intro to Machine Learning - Spring 2019 - University of Waterloo
โ€ƒโ€ƒโ€ƒ๐Ÿ“น SYDE 522 โ€“ Machine Intelligence (Winter 2018, University of Waterloo)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Machine Learning for Engineers 2022 / (YouTube)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Introduction to Machine Learning Course - Udacity
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Hesham Asem - Arabic content
โ€ƒโ€ƒโ€ƒ๐Ÿ“น IBM ML with Python
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Machine Learning From Scratch - YouTube (Python Engineer)
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Hands On ML (1st & 2nd & 3rd) Editions | Code: View on Github
โ€ƒโ€ƒโ€ƒ๐Ÿ“น ML Algorithms in Practice
โ€ƒโ€ƒโ€ƒ๐Ÿ“น ML scientist
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Project

3. Web Scraping/APIs
โ€ƒโ€ƒโ€ƒ๐Ÿ“น course
โ€ƒโ€ƒโ€ƒ๐Ÿ“• intro2
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Book for both topics
APIs
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Article
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
4. Stats.
โ€ƒโ€ƒโ€ƒ๐Ÿ“• This stats - Book
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Think Bayes - Book
5. Advanced SQL
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Joining Data in SQL - DataCamp
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Intermediate SQL - DataCamp
โ€ƒโ€ƒโ€ƒ๐Ÿ“น More advanced SQL

7. Feature Engineering
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Article
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Book
8. interpret Shapley-based explanations of ML models.
โ€ƒโ€ƒโ€ƒ๐Ÿ“• SHAP
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Kaggle ML explainability

After finishing this level apply to 2 or 3 good sized projects.

Read this book, please ๐Ÿ“– Introduction to Statistical Learning with Applications in R ุจู‚ูˆู„ูƒ ุงู‚ุฑุฃู‡


๐Ÿ”ฐ Advanced ๐Ÿ”ฐ

1. Deep Learning
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Deep Learning Fundamentals
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Introduction to Deep Learning - MIT
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Specialization
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Dive into Deep Learning (En) | (Ar) version โžก๏ธPart1 & Part2
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Deep Learning UC Berkely
โ€ƒโ€ƒโ€ƒ๐Ÿ“• github of Dive into DL
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Stanford Lecture - Convolutional Neural Networks for Visual Recognition
โ€ƒโ€ƒโ€ƒ๐Ÿ“น University of Waterloo - ML / DL
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Deep Learning for coders with fastai & PyTorch

2. Tensorflow
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Specialization
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Youtube
โ€ƒโ€ƒโ€ƒ 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
โ€ƒโ€ƒโ€ƒ๐Ÿ“น PyTorch (UC Berkeley - Youtube) - Lec3 (The 5 parts)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น PyTorch - Dr. Data Science - Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Pytorch Tutorial - Aladdin - Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น PyTorch Course (2022) - Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Deep Learning With Pytorch
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Machine Learning with PyTorch and Scikit-Learn -2022

4. Advanced Data Science
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Advanced Data Science with IBM Specialization Includes Apache Spark
โ€ƒโ˜ ๏ธAdvanced ML Topics๐Ÿง  | Lecs (YouTube)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022 - Materials
โ€ƒโ€ƒโ€ƒ๐Ÿ“น 18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
โ€ƒโ˜ ๏ธML based Computer Vision | Lecs (YouTube)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)
โ€ƒโ€ƒโ€ƒ๐Ÿ“น NOC:Deep Learning For Visual Computing - IIT Kharagpur
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Deep Learning for Computer Vision - Michigan

5. NLP
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Specialization - Coursera
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Arabic - Ahmed El Sallab
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Stanford CS224N Lectures - Winter 2021- YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Stanford XCS224U Lectures - Spring 2021- YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Introduction to Natural Language Processing in Python
โ€ƒ๐Ÿ”ธLLMS What`s Large Language Model?
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Generative AI for Everyone (Andrew Nj) - Coursera๐Ÿ†•
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Generative AI with LLMs
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Stanford CS236: Deep Generative Models I 2023 - YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Stanford CS25 - Transformers United 2023 - YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Recent Advances on Foundation Models - Winter 2024 - University of Waterloo
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Understanding LLMs Foundations and Safety UC Berkeley - Spring 2024 - YouTube
โ€ƒโ€ƒโ€ƒ๐Ÿ“น LLM Foundations
โ€ƒโ€ƒโ€ƒ๐Ÿ“น How ChatGPTs / Transformers work?1 - 2 - 3 overview & Maths behind
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Prompt Engineering | (Ar) If you want to get the most out of LLMs
โ€ƒโ€ƒโ€ƒ๐Ÿ“น LLMOps A Lec going through the entire LLM pipeline

6. Inferential Statistics
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Specialization, 2nd & 3rd courses
โ€ƒโ€ƒโ€ƒ๐Ÿ“น course
7. Bayesian Statistics
โ€ƒโ€ƒโ€ƒ๐Ÿ“น 1 - From Concept to Data Analysis
โ€ƒโ€ƒโ€ƒ๐Ÿ“น 2 - Techniques and Models
โ€ƒโ€ƒโ€ƒ๐Ÿ“น 3 - Mixture Models
8. Model Deployment
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Flask tutorial
โ€ƒโ€ƒโ€ƒ๐Ÿ“น TensorFlow: Data and Deployment Specialization
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Deploy Models with TensorFlow Serving and Flask
โ€ƒโ€ƒโ€ƒ๐Ÿ“น How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke
โ€ƒโ€ƒโ€ƒif 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.
โ€ƒโ€ƒโ€ƒ๐Ÿ”— MLOps-zoomcamp
โ€ƒโ€ƒโ€ƒ๐Ÿ”— MLOps-guide
โ€ƒโ€ƒโ€ƒ๐Ÿ“• Practical MLOps
10. Probabilistic Graphical Models
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Specialization - Coursera
โ€ƒโ€ƒโ€ƒ๐Ÿ“น Spring 2016, University of Utah - YouTube

๐ŸŒŸ Read these books, they will be beneficial to you.
โ€ƒ ๐Ÿ“– Bayesian Reasoning and Machine Learning
โ€ƒ ๐Ÿ“– The Elements of Statistical Learning
โ€ƒ ๐Ÿ“– Pattern Recognition and Machine Learning - Bishop (Advanced)

โ€ƒโ€ƒ Recommended by Eng.Mohamed Hammad.

๐Ÿ“ŒPROJECTS โฌ


โ€ƒโ€ƒโ€ƒ๐ŸŽฅDeena Gergis - End to end Project
โ€ƒโ€ƒโ€ƒ๐ŸŽฅMachine Learning Projects - Youtube
โ€ƒโ€ƒโ€ƒ๐Ÿ’ปTop 10 Data Science Projects for Beginners
โ€ƒโ€ƒโ€ƒ๐Ÿ’ป12 Data Science Projects for Beginners and Experts
โ€ƒโ€ƒโ€ƒ๐Ÿ’ปData Science Projects & Ideas
โ€ƒโ€ƒโ€ƒ๐Ÿ’ปTop 310+ Machine Learning Projects for 2023
โ€ƒโ€ƒโ€ƒ๐Ÿ’ป10 End-to-End Guided Data Science Projects
โ€ƒโ€ƒโ€ƒ๐ŸŽฅReal-World ML Tutorial w/ Scikit Learn
โ€ƒโ€ƒโ€ƒ๐Ÿ’ปPython Codes in Data Science
โ€ƒโ€ƒโ€ƒ๐ŸŽฅEnd To End ML Project With Dockers,Github Actions And Deployment
โ€ƒโ€ƒโ€ƒ๐Ÿ’ป12 free Data Science projects to practice Python and Pandas (resolve interactive online)


๐Ÿ“Œ Common Tools โคต๏ธ


English Arabic Book
๐ŸŽฅ Git - Udacity ๐ŸŽฅ ุดุฎุจุท ูˆุงู†ุช ู…ุทู…ู† ๐Ÿš€ ๐Ÿ“• Pro Git
๐Ÿ“– w3schools ๐ŸŽฅ almadrasa
โ€ƒ ๐ŸŽฅ Elzero

๐Ÿ“Œ More Books :atom::atom: ๐Ÿ“Œ Check This!

โ€ƒโ€ƒ๐Ÿ“• ๐Ÿ”ฅ 12 Free Important Books ๐Ÿ”ฅ
โ€ƒโ€ƒ๐Ÿ“• Mathematics for Machine Learning
โ€ƒโ€ƒ๐Ÿ“• An Introduction to Statistical Learning
โ€ƒโ€ƒ๐Ÿ“• Understanding ML: From Theory to Algorithms
โ€ƒโ€ƒ๐Ÿ“• Probabilistic Machine Learning: An Introduction
โ€ƒโ€ƒ๐Ÿ“• storytelling with data โœ”๏ธImportant data visualization guide.


๐Ÿ“Œ Collection of the best Cheat sheets

  1. Importing Data

  2. Pandas

โ€ƒโ€ƒ - (1) โ€ƒโ€ƒ - (2) โ€ƒโ€ƒ - (3)

  1. Matplotlib

  2. Seaborn

  3. Probability

  4. Supervised Learning

  5. Unsupervised Learning

  6. Deep Learning

  7. Machine Learning Tips and Tricks

  8. Probabilities and Statistics

  9. Comprehensive Stanford Master Cheat Sheet

  10. Linear Algebra and Calculus

  11. Data Science Cheat Sheet

  12. Keras Cheat Sheet

  13. Deep Learning with Keras Cheat Sheet

  14. Visual Guide to Neural Network Infrastructures

  15. Skicit-Learn Python Cheat Sheet

  16. Scikit-learn Cheat Sheet: Choosing the Right Estimator

  17. Tensorflow Cheat Sheet

  18. Machine Learning Test Cheat Sheet

  19. Machine Learning Cheat Sheets (Recommended Guide) ุฑุงุฌุน ุงู„ู…ูˆุงุถูŠุน ุงู„ู„ูŠ ููŠ ุงู„ุดูŠุช ุฏูŠ ูŠุง ุนุฒูŠุฒูŠ ูˆุดูˆู ุงู„ู„ูŠ ู†ุงู‚ุตูƒ


The best way to practice is to take part in competitions.

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


๐Ÿ““ Data Science Interview Questions: โ–ถ๏ธ โ€ƒ - (1) โ€ƒ- (2) โ€ƒ- (3) โ€ƒ- (4) โ€ƒ- (5) โ€ƒ- (6) Arabic Podcast๐ŸŽง
โ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒโ€ƒ- (7) 30 days of interview preparation๐Ÿ“–


๐ŸŽงData Science Podcasts: ๐ŸŽ™๏ธ
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

โ€ƒโ€ƒโ€ƒ ๐Ÿ‘€ Arabic Podcasts??
โ€ƒโ€ƒโ€ƒโ€ƒ :trollface:ุดุงูŠููƒ ูŠุงู„ู„ูŠ ุฒู‡ู‚ุงู† ููŠ ุงู„ู…ูˆุงุตู„ุงุช

โ€ƒโ€ƒโ€ƒ๐Ÿ“ปArabic Data Podcast | Spotify by Eng. Kareem Abdelsalam
โ€ƒโ€ƒโ€ƒ๐Ÿ“ปlุฅู„ูŠ ุงู„ุจูŠุงู†ุงุช ูˆู…ุง ุจุนุฏู‡ุง by Eng. Youssef Hosni
โ€ƒโ€ƒโ€ƒ๐Ÿ“ปGarage Education by Eng. Mostafa Alaa
โ€ƒโ€ƒโ€ƒ๐Ÿ“ปData Science ุจุงู„ุนุฑุจูŠ


๐Ÿ“Œ Data Analysis Recommendations.
Books (๐Ÿ“• The Data Analysis Workshop & ๐Ÿ“• Head First Data Analysis)
FWD - (The 3 Levels)
Google Data Analytics Professional Certificate
IBM Data Analyst Professional Certificate
Google Advanced Data Analytics Professional Certificate ๐Ÿ†•
Alex The Analyst - YouTube๐Ÿ“บ
Note: A good knowledge & projects in just Excel, SQL & Power BI / Tableau can bring you great opportunities.
โ€ƒโ€ƒ-excel Excel More Resources: (Arabic 1๐Ÿ“น - Arabic 2๐Ÿ“น - Books ๐Ÿ“„ and cheat sheets for revising)

๐Ÿ“Œ Data Engineering Recommendations.
Books (๐Ÿ“• Fundamentals of Data Engineering & ๐Ÿ“• Designing Data-Intensive Applications)
Arabic Podcast, Starting a Career in Data Engineering.
For Arab, I recommend 2 YouTube Channels: (Garage Education & Big Data ุจุงู„ุนุฑุจูŠ)
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.


๐Ÿ“ CV / Resumes ๐Ÿ“ โ€ƒ

๐Ÿ“Œ Data & AI Companies in Egypt โ€ƒ - โ€ƒ AI/ML Driven Companies In Egypt


Contact Me ๐Ÿ“ฑ


Typing SVG