الدورة الــشاملة في عــلم البيانــات والذكــاء الاصطناعي
D A T A   S C I E N C E   &   A I

Course Overview:

By the end of this comprehensive program, students will be able to write practical Python programs, apply appropriate data structures, and design algorithmic solutions that form a solid foundation for advanced fields. They will master data preprocessing and analysis, implement and evaluate machine learning algorithms, and use Python-based ML tools to solve real-world problems in domains such as business, healthcare, and cybersecurity. In addition, students will gain the ability to design and implement deep learning models and apply them to diverse applications such as image recognition, natural language processing, and predictive analytics — opening the door to advanced research and professional applications.

Learning Objectives:

By the end of this comprehensive program in Data Science & Artificial Intelligence, students will be able to:

• Develop strong programming skills in Python, including data structures, functions, object-oriented programming, and practical use of popular libraries such as NumPy and Pandas.

• Apply problem-solving and algorithmic thinking to design and implement effective computational solutions.

• Preprocess, clean, and analyze datasets using exploratory data analysis (EDA) and statistical methods.

• Implement, evaluate, and optimize a variety of machine learning algorithms, including supervised and unsupervised methods, while understanding model validation and performance metrics.

• Gain practical experience with Python ML tools such as scikit-learn, Matplotlib, and Pandas to solve real-world problems across domains like business, healthcare, and cybersecurity.

• Design, train, and evaluate deep learning models using modern frameworks such as TensorFlow, Keras, and PyTorch.

• Work with advanced architectures, including CNNs for image processing, RNNs and LSTMs for sequence data, and transfer learning models such as ResNet and BERT.

• Explore cutting-edge techniques such as regularization, optimization, generative models (GANs, Autoencoders), and pretrained model integration.

• Complete capstone projects in both ML and DL, applying end-to-end workflows to real-world datasets and presenting practical AI-driven solutions.

What You Will Learn:

 1. Introduction to Data Science:
Introduction

• Overview of the course and its topics

• Importance of data in modern business and decision-making

• The role of data science in today’s business world

What is Data?

• Definition and types of data (qualitative vs. quantitative)

• Structured vs. unstructured data

• Examples of data in daily life and business

• The role of data in analytics and decision-making

What is Data Science?

• Definition of data science

• Evolution and history of data science

• Interdisciplinary nature (mathematics, statistics, computer science, domain knowledge)

• Key applications and industries using data science

Data Science Process

• Steps in the data science workflow: problem formulation, data collection, data cleaning, exploratory analysis, modeling, evaluation, deployment

• Other process models: CRISP-DM

• Iterative nature of the data science process

Data Science Skills

• Technical skills: Python, R, SQL, data processing, machine learning

• Knowledge of mathematics and statistics

• Communication and storytelling with data

• Critical thinking and problem-solving

• Collaboration tools such as Jupyter and Git

Importance of Data Science

• Data as a strategic asset

• Data-driven decision-making

• Gaining competitive advantage through analytics

• Real-world impact across sectors such as healthcare, finance, marketing, and more

Data Science Tools

• Overview of common tools

• Choosing the right tools for the right tasks

Applications of Data Science

• Predictive analytics

• Recommendation systems

• Fraud detection

• Image and speech recognition

• Customer segmentation

Benefits of Data Science

• Improved decision-making

• Increased operational efficiency

• Personalized services for users

• Automation and predictive capabilities

• Innovation and new product development

2. Programming Language Using Python:

This course is designed to provide students with the fundamental knowledge and skills in computer programming using Python, one of the most popular and beginner-friendly programming languages. The course focuses on programming concepts, algorithm development, and problem-solving techniques using Python. The main areas of study include:

• Introduction to Python – understanding the language, development environment, and writing the first programs.

• Data Types and Variables – working with basic data types and variables.

• Operators and Expressions – applying arithmetic and logical operations in programs.

• Control Structures – using conditional statements (if) and loops (for, while) to control program flow.

• Functions and Modules – defining reusable functions and working with modules.

• Data Structures – handling lists, dictionaries, sets, and strings.

• File Handling – reading from and writing to files.

• Error Handling and Exceptions – managing errors using try and except.

• Object-Oriented Programming (OOP) in Python – understanding classes, objects, and inheritance.

• Libraries and Applications – exploring popular Python libraries such as NumPy and Pandas for practical use cases.

3. Machine Learning (ML) using Python:

This course introduces students to the fundamental concepts, techniques, and tools of Machine Learning (ML) using Python. It provides both theoretical foundations and hands-on practice, enabling students to build intelligent systems capable of learning from data. The course emphasizes applying ML algorithms using Python libraries to solve real-world problems. The main areas of study include:

• Introduction to Machine Learning – understanding ML concepts, types of learning (supervised, unsupervised, reinforcement), and applications.

• Data Preprocessing – cleaning, transforming, and preparing data for analysis.

• Exploratory Data Analysis (EDA) – using visualization and statistics to understand datasets.

• Supervised Learning Algorithms – implementing and evaluating models such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM).

• Unsupervised Learning Algorithms – clustering methods (K-Means, Hierarchical) and dimensionality reduction (PCA).

• Model Evaluation and Validation – applying metrics such as accuracy, precision, recall, F1-score, confusion matrices, and cross-validation.

• Overfitting and Regularization – improving model generalization.

• Neural Networks and Deep Learning Basics – introduction to perceptrons, multilayer networks, and activation functions.

• Python ML Libraries – using libraries such as scikit-learn, NumPy, Pandas, and Matplotlib for practical ML tasks.

• Capstone Project – applying ML techniques to a real-world dataset using Python.

4. Deep Learning (DL) using Python:

This course provides students with a comprehensive introduction to Deep Learning (DL) using Python, focusing on both theoretical foundations and practical applications. Students will learn how to design, train, and evaluate deep neural networks using Python libraries, gaining hands-on experience in solving real-world problems. The main areas of study include:

• Introduction to Deep Learning – understanding neural networks, perceptrons, and the difference between ML and DL.

• Neural Network Fundamentals – concepts of layers, weights, biases, activation functions, and loss functions.

• Training Deep Neural Networks – gradient descent, backpropagation, and optimization techniques.

• Regularization and Optimization – dropout, batch normalization, and advanced optimizers such as Adam and RMSprop.

• Convolutional Neural Networks (CNNs) – architectures for image classification and computer vision tasks.

• Recurrent Neural Networks (RNNs) – sequence modeling and applications such as text and speech processing.

• Long Short-Term Memory (LSTM) and GRU Networks – handling long-term dependencies in sequential data.

• Transfer Learning and Pretrained Models – leveraging existing models such as VGG, ResNet, and BERT.

• Generative Models – introduction to Autoencoders and Generative Adversarial Networks (GANs).

• Python Deep Learning Libraries – practical implementation using TensorFlow, Keras, and PyTorch.

• Capstone Project – developing and presenting a deep learning solution for a real-world dataset.