Here you have the full roadmap divided into three levels as headlines and each one has some suggested courses..
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value......
⚠️ I have a knot in mathematics, Do I enter this field? Check this
💡 If You Don’t Know What’s the Difference Between A Data Scientist, Data Analyst, Data Engineer, ML Engineer? Watch this
📌 For Data Camp courses, the GitHub student pack gives 3 free months. How to get it || Register Here
1️⃣ :If you need to climb stairs, you must tread the first stair......So the “Computer science fundamentals” are the first stair in CS stairs, It will help you to understand how the computer works, how to deal with data, how to deal with code, and many other things you should know to go freely on your career. Whatever the CS technology you choose to learn you need these fundamentals.
Many people who decided to start directly learning one of CS technology suffer from a Lack of some skills and information and they waste their time to go back and learn what they need and the process is repeated continuously.
2️⃣ :Here you have the main topics and for each one, there are some suggested courses that you should take one or more of them..
3️⃣ :Notice that any roadmap is not sacred, you may find some courses better than the suggested ones from your perspective and that's completely okay, so Choose the courses that suit you but you should go through all the topics.
4️⃣ :To get a better result, it is preferable to read a pdf or a book from those below in parallel with the course you choose.
Anaconda is 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 guide toinstall anacondaa and run Jupyter Notebook.
- A guide to deal with Jupyter Notebook.
- Conda Essentials datacampp).
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.
- JetBrains student license can let you freely install the paid professional version of Pycharm for educational purposes only.
- A Guide to install and use Pycharm
- Running Jupyter Notebook from Pycharm
I will recommend
Python, although you may encounterRin more Data Analytics related jobs. Python mastery will come with time - learn enough basics to be able to read code and implement. Learning Python will require time to reiterate several times to understand a concept, & trust me it's worth it. As said learning Data science requires time and learning the hard way rather than shortcuts which will make you nowhere. So, don't get demotivated if you're not able to understand a concept, just keep trying and you'll get it.
| Topics | Resources & Links |
|---|---|
| 1.Git | 🎥Udacity course 📑Git Tutorial for Absolute Beginners from Zero to Hero 🎥Git (session) 🎥Arabic Youtube |
| 2. Web Scraping & APIs | 🎥Intro to web scraping (data camp) 🎥Intermediate Importing Data (data camp) 📑Web Scraping with Python Using Beautiful Soup 📑Getting Started with APIs 📑Medium 📑Rapidapi |
| 3.Time-Series | 📑Tutorial(Prophet) 🎥Time Series with Python(data camp) 🎥Arabic Course(Hesham Asem)1 |
| 4. Math for Machine Learning | 🎥Mathematics for Machine Learning Specialization 🎥Mathematics for Machine Learning 🎥Probability (Khanacademy) 🎥Probability MIT playlist 🎥Linear algebra for ML Coursera specialization 🎥 Multivariate Calculus |
| 5. Machine Learning | 🎥Machine learning andrew 🎥Machine learning Udacity 🎥Machine learning IBM 📕Hands on ML book(1st & 2nd & 3rd) Editions + Notebooks |
| 6. Feature Engineering | 📑Tutorial 📑Tutorial 📕Feature Engineering for Machine Learning |
| Topics | Resources & Links |
|---|---|
| 1. Deep Learning | 🎥Deep learning specialization Andrew 🎥Deep learning book |
| 2. Computer Vision | 🎥Stanford playlist 🎥Stanford |
| 3. NLP | 🎥NLP Stanford 🎥CS224n: Natural Language Processing with Deep Learning 🎥NLP Coursera specialization |
| 4. Spark | 🎥Spark (Udacity) |
| 5. Data Warehouse | 🎥Data Warehouse Concepts (Coursera) |
| 7. Inferential Statistics | 🎥Specialization, 2nd & 3rd courses 🎥Coursera 🎥Udacity |
| 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. Probabilistic Graphical Models | 🎥Specialization |