Advanced Diploma in Data Science and AI-ML in Collaboration with
FPT Academy International

The Advanced Diploma in Data Science and Artificial Intelligence/Machine Learning (AI-ML) is a comprehensive, industry-aligned program developed in partnership with FPT Academy International. This diploma prepares students to excel in the rapidly expanding global AI-ML market, which is projected to grow from USD 44.58 billion in 2024 to USD 2.57 trillion by 2037 with a remarkable CAGR of over 36.6%.

Global Embedded Market Analysis

The global Data Science and Artificial Intelligence/Machine Learning (AI-ML) market is expected to grow from USD 44.58 billion in 2024 to USD 2.57 trillion by 2037, with a CAGR of over 36.6% during this period.
Another report estimates the market will reach USD 1,799.6 billion by 2034, growing from USD 70.3 billion in 2024, with a CAGR of 38.3% between 2025 and 2034.

Program Details

Hours

Years

Semester

Program Modules

Semester 1: Foundational Programming and Data Manipulation

Python Programming and Data Structures
Mathematics and Statistics for Data Science
Data Manipulation and Preprocessing
Exploration Data Analysis for Machine Learning

Semester 3 – Deep Learning and Advanced Analytics

Deep Learning Foundations
Computer Vision
NLP and Text Analytics
Advanced Data Analytics
MLOPs and AI Deployment for Data Science

Semester 2 – Machine Learning Fundamentals

Introduction to Machine Learning
Regression and classification models
Feature Engineering and Data Analysis Techniques
Unsupervised Learning

Semester 4 – Applied AI and Advanced Specialization

Advanced Deep Learning Models
Optimization Techniques
Model Interpretability and Explainability
Ethical AI, Secure Data and Research-Driven Cloud Deployment

Semester 1: Foundational Programming and Data Manipulation

Project Modules

Python Programming and Data Structures
Mathematics and Statistics for Data Science
Data Manipulation and Preprocessing
Exploration Data Analysis for Machine Learning

Project Outcomes

Strong Foundation in Python Programming
Object-Oriented Programming (OOP) Expertise
Proficiency in Advanced Data Structures and Algorithms
Hands-on Problem Solving & Algorithmic Thinking
Foundational Knowledge of Probability & Statistics
SQL & Database Management
Data Cleaning & Preprocessing, EDA & Visualization
Introduction to Machine Learning
Working with Jupyter & Anaconda for Data Science
Industry-Ready Skills & Project-Based Learning

Project Highlights

Python code
Project report documenting design, implementation
Presentation showcasing project features and functionality

Projects

Project on Data Aquisition and Analysis

Semester 2: Machine Learning Fundamentals

Project Modules

Introduction to Machine Learning
Regression and classification models
Feature Engineering and Data Analysis Techniques
Unsupervised Learning

Project Outcomes

Understanding of Machine Learning
Expertise in Data Preprocessing & Feature Engineering
Regression & Classification Techniques
Advanced Machine Learning Algorithms
Model Evaluation & Hyperparameter Tuning
Handling Imbalanced Datasets & Resampling Techniques
Time Series Analysis & Forecasting
Clustering & Unsupervised Learning
Dimensionality Reduction & Feature Extraction

Project Highlights

Train machine learning model for identifying anomalies
Project report documenting design, implementation, and testing
Presentation showcasing project features and functionality
Demonstration of working ML model

Projects

Optimizing Machine Learning Analytics

Semester 3: Deep Learning and Advanced Analytics

Project Modules

Deep Learning Foundations
Computer Vision
NLP and Text Analytics
Advanced Data Analytics
MLOPs and AI Deployment for Data Science

Project Outcomes

Fundamentals of Neural Networks & Deep Learning
Convolutional Neural Networks (CNNs) & Image Processing
Natural Language Processing (NLP) & Text Analytics
Advanced Machine Learning & Model Optimization
Time Series Forecasting
Model Deployment & MLOps
AI Agents & Intelligent Automation

Project Highlights

Train machine learning model for identifying anomalies
Project report documenting design, implementation, and testing
Presentation showcasing project features and functionality
Demonstration of working ML model

Projects

Deep Learning for Intelligent Data Analysis and Deployment

Semester 4: Advanced Diploma in comprehensive Development of Automotive and AUTOSAR Systems

Project Modules

Advanced Deep Learning Models
Optimization Techniques
Model Interpretability and Explainability
Ethical AI, Secure Data and Research-Driven Cloud Deployment

Project Outcomes

Convolutional Neural Networks (CNNs) & Computer Vision
Recurrent Neural Networks (RNNs) & Sequence Models
Transfer Learning & Model Optimization
Ensemble Learning & Model Stacking
Model Evaluation & Explainability
AI Security & Ethical AI
Scalable AI Deployment & Cloud Computing
AI Research & Experimental Design

Project Highlights

Ensemble model source code implementing bagging, boosting, and stacking methods.
Project report documenting the design, implementation, model selection, and evaluation metrics.
Presentation showcasing the ensemble methods, model performance, and real-world medical applications.
Demonstration of working model with real medical data, including image or diagnostic predictions and explainability outputs.
Test plan and results, including evaluation on various metrics such as accuracy, precision, recall, AUC-ROC, and fairness assessments.

Projects

Developing and Optimizing Advanced AI Models