AI Data Scientist

Go from your first line of Python to building machine learning and deep learning models. It’s a complete path to a data scientist career, in Arabic, with 200+ hours of content and a portfolio of real projects.

About this track:

Data scientists are among the most in-demand roles in Egypt, the Gulf and worldwide. Companies want people who can do more than describe data: they want people who can predict, classify and automate with it. This track takes you there step by step, even if you’ve never written a line of code.

The track follows the path of a real data scientist, in three stages:

Stage 1: Python for Data Engineering & Data Science
You start from zero with Python. You learn NumPy and Pandas, data cleaning, visualization with Matplotlib, Seaborn and Plotly, statistical analysis and A/B testing, working with databases and APIs, and automating reports and ETL pipelines. The stage ends with an end-to-end capstone project for your GitHub portfolio.

Stage 2: Machine Learning Scientist
With Python in hand, you move into machine learning: regression and classification algorithms, model evaluation and selection, unsupervised learning, feature engineering, NLP, and time series forecasting. You also learn to explain your models’ decisions with interpretability techniques. Then you build ML pipelines ready for production. You’ll work on 12 hands-on projects and a final end-to-end ML capstone.

Stage 3: Deep Learning with TensorFlow & Keras
Finally, you build neural networks. You cover CNNs for computer vision, transfer learning, RNNs and sequence models, NLP with deep learning, transformers, and generative deep learning. You learn to write custom training loops, interpret models responsibly and deploy them to production. You finish with 4 real-world capstone projects.

When you complete the track, you’ll have solid Python skills, a working toolkit of machine learning and deep learning techniques, and a portfolio of projects to show employers.

Courses included:

1. Python for Data Engineering & Data Science (62 hours)
2. Machine Learning Scientist (75 hours)
3. Deep Learning with TensorFlow & Keras (75+ hours)

Track at a glance:

• 200+ hours of video content
• 350+ lessons
• 15+ hands-on and capstone projects
• 10 bonus modules
• Goes from beginner to advanced

What will I learn:

• Write clean, efficient Python for data work
• Load, clean, transform and analyze data with NumPy and Pandas
• Build charts and interactive dashboards with Matplotlib, Seaborn, Plotly and Dash
• Apply statistics, hypothesis testing and A/B testing to business problems
• Connect to SQL databases and APIs, and automate reports and ETL pipelines
• Build, evaluate and tune regression and classification models
• Use clustering and other unsupervised techniques to find patterns
• Forecast time series and work with text data (NLP)
• Engineer features and explain model decisions with interpretability tools
• Build ML pipelines ready for production
• Design and train deep neural networks with TensorFlow and Keras
• Build CNNs for computer vision and use transfer learning with pre-trained models
• Work with RNNs, transformers and modern NLP
• Explore generative deep learning
• Deploy deep learning models to production
• Build a portfolio of end-to-end projects for your GitHub