Structure of the Program

Lego based structure
Each level has many modules. Each Module has many topics.

Program Duration:

- The Program duration including the introductory session is about 16 weeks (33 sessions)
- The last two weeks will be dedicated to projects from real world example

Program Contents and Syllabus

Data Science: Overview and Career Path

An introductory session about Data Science. Managers, fresh graduates, students and interested audience are invited to attend to know more about this strongly evolving field and how they can make use of it for the progress of own individual career or for the expansion of the enterprise business.

Data Processing (4 weeks)

Linux and computing (1 session)
You will learn the basics of Linux and its major tools. You will also learn the basics of computing and programming.

Python (5 sessions)
As programming language, you will learn Python. You will learn the language structure, and it major data types, functions, and libraries.

Data Manipulation: In this module, you will learn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc. You will also learn how to clean data, impute data, scale data, import and export data, and scrap data from the internet.

Data Visualization:  You will learn how to visualize your data by plotting meaningful curves that summarizes data and help you gain insights from data to further analyze it. You will learn different tools for data visualization including: matplotlib in python, ggplot2 in R.

Feature Engineering: You will learn the basic methods of feature transformation, handling missing values, handling outliers. In addition, you will learn the main techniques for feature selection, feature extraction, and dimensionality reduction.

Learning Methods (6 weeks)

Introduction to Machine Learning:
You will learn the basic concepts of machine learning and example application of its methods. 

Descriptive and Inferential Statistics:
You will learn data science basics including probability and descriptive statistics. You learn inferential statistics and hypothesis testing.

You will learn the two main approaches of supervised learning. You will learn the main classification algorithms such as: linear classification models, decision trees, and support vector machines. You will also know linear regression as an example for regression. 

You will learn the main clustering algorithms and how to use them on different problems. For example, you will learn hierarchical clustering and partitioning-based clustering.

You will learn the basics of neural networks, its architecture, and how to design a neural network for solving a real world classification problem. You will also learn the basics of deep learning using its python frameworks.  

Prediction and Decisions (6 weeks)

You will learn advanced clustering methods such as: Density based clustering (DBScan) and affine propagation. 

You will learn the fundamentals of reinforcement learning including sequential decision process and Markov decision process. You will also learn the basic reinforcement learning algorithms such as: value iteration, policy iteration, and Q-learning. You will learn about deep reinforcement learning as well. 

In this module, you will learn the basic concepts of time series analysis which is needed if you have a time varying data such as: stock price market. You will learn some basic time series models: exponential smoothing, Auto-Regressive Integrated Moving Average (ARIMA), and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH). In addition, you will know seasonality and trends and how to extract them from time series data. 

Different projects will be organized among teams of the trainees to cover different interesting real life topics.

Join the Program


Systems and Biomedical Engineering Dept.
Faculty of Engingeering, 
Cairo University, Egypt.


Phone: +2 (0) 000 0000 001
Fax: +2 (0) 000 0000 002


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