Data Science and AI Diploma
243 Hours
A complete career journey into data, machine learning, and artificial intelligence
This diploma is designed for learners who want to move from programming fundamentals to advanced AI systems and real-world projects
You will gain deep technical skills and hands-on experience that prepare you for roles such as Data Scientist, Data Engineer, and Machine Learning Engineer
Why Data Science and AI now
🚀 Data and AI are driving decision-making across every industry
🚀 Companies need professionals who can analyze data, build models, and deploy intelligent solutions
🚀 This diploma delivers end-to-end skills, from data preparation to advanced deep learning and AI projects
Core skills you will master
🐍 Master Python for AI and data science applications
📊 Apply data science methodologies for real-world problem solving
📈 Build predictive analytics and machine learning models
🧠 Develop deep learning systems using TensorFlow, Keras, and PyTorch
🗣 Work with natural language processing and sequence-based models
🔍 Analyze, clean, visualize, and interpret complex datasets
🏗 Design complete AI pipelines from raw data to deployed models
Learning experience and deliverables
🎟 Ticket system for continuous instructor support
🛠 2 major hands-on projects
⏱ 249 intensive training hours with strong practical depth
📝 Assignments that reinforce concepts and technical mastery
Quality, licensing, and recognition
🏛 Licensed by the Ministry of Communications and Information Technology
🏢 Registered member of the Information Technology Industry Development Agency ITIDA
📜 ISO 9001:2015 certified quality management system
🔧 Training programs accredited by the Egyptian Appliances Syndicate
👷 Training programs accredited by the Engineers Syndicate
⚙ Training programs accredited by the Applied Professions Syndicate
What you will study
📘 Comprehensive Data Science and AI topics that provide learners with deep theoretical understanding and strong practical expertise
Diploma curriculum
Python Fundament
📌 Variables and Data Types
📌 Operators and Expressions
📌 Strings
📌 Conditional Statements
📌 Loops
📌 Functions and Modules
Data Analysis, Engineering & Visualization with Python
📌 NumPy & Pandas Foundations
📌 Data Cleaning & Preparation
📌 Data Quality & Validation
📌 Data Manipulation
📌 Exploratory Data Analysis (EDA)
📌 Data Visualization
📌 Projects & Capstone
SQL for Data Analysis & Analytics Engineering
📌 SQL Foundations
📌 Aggregation & Grouping
📌 Joining Tables
📌 Subqueries & CTEs
📌 Window Functions
📌Time & Date Analysis
📌 SQL for Analytics
Statistics & Probability for Data
📌 Orientation
📌 Descriptive Stats
📌 Variability & Shape
📌 Distributions
📌 Theorems
Applied Machine
📌 Foundations
📌 Regression
📌 Regularization
📌 Linear Classifiers & Distance Based
📌 Probabilistic & Advanced Metrics
📌 Decision Trees
📌 Ensembles (Bagging) and Clustering
📌 Dim. Reduction
📌 Tuning
📌 Pipeline & Explainability
Deep Learning Fundamentals & Neural Networks
📌 Intro to DL
📌 Stabilization
📌 Advanced Keras
📌 Advanced Keras
Applied Computer Vision & CNNs
📌 Digital Images
📌 Intro to OpenCV
📌 Why CNNs?
📌 The Convolve Block
📌 The Pooling Block
📌 Architectures History
📌 Training CNNs
📌 Transfer Learning Concepts
📌 Going Beyond Classification
📌 Object Detection
📌 Segmentation
Applied NLP & Transformers
📌 Preprocessing
📌 Visualization
📌 Vectorization
📌 Modelling
📌 Vector Space
📌 Algorithms
📌 Transfer Learning
📌 RNNs
📌 LSTMs
📌 Why Transformers?
📌 Attention
📌 Architecture
📌 The Library
📌 BERT
📌 Advanced Tasks
Applied Reinforcement Learning
📌The Ecosystem Key Concepts
📌 Bellman Equation
📌 Q-Learning
📌 Why Deep RL?
📌 DQN Architecture
📌 Beyond Q-Learning
Generative AI, LLMs & Prompt Engineering
📌Foundations: The Generative AI Landscape
📌 The Ecosystem: The Generative AI Landscape
📌 Core Techniques: Advanced Prompt Engineering
📌 Why RAG?
📌 RAG Components
📌 RAG Frameworks
📌 Fine-Tuning
📌 LoRA
📌 Agents
📌 Ethics
MLOps & Model Deployment
📌 Best Practices: From Notebook to Production Code
📌 Version Control: From Notebook to Production Code
📌 The Problem: Experiment Tracking (MLflow)
📌 MLflow Core: Experiment Tracking
📌 Concepts: Containerization (Docker)
📌 Docker Engine: Containerization
📌 API Basics: Model Serving & APIs
📌 FastAPI: Model Serving & APIs
📌 Deployment: Deployment & CI/CD
📌 Automation: Deployment & CI/CD
Who can join
💻 You must own a laptop
🔥 Passion to learn data, AI, and advanced technologies
🎓 No prior experience required, the diploma takes you from foundations to professional level
Program overview
This diploma is one of the most comprehensive Data Science and AI programs offered by AMIT Learning. Over a 6-month intensive journey, you will study Python, data science fundamentals, machine learning, deep learning, and real-world AI applications.
You will learn the difference between data science and data engineering, master core tools such as Pandas, NumPy, and Anaconda, and apply machine learning operations to real projects.
By the end of the diploma, you will complete multiple graduation-level projects in areas such as medical diagnostics, financial analysis, and data-driven decision-making, significantly strengthening your CV and positioning you strongly in the job market
The ticket system ensures continuous support throughout the diploma, including instructor guidance and private support sessions, helping you achieve the highest possible learning outcome
