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
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Years
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Semester
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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
- Python Programming and Data Structures
- Mathematics and Statistics for Data Science
- Data Manipulation and Preprocessing
- Exploration Data Analysis for Machine Learning
- 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
- Python code
- Project report documenting design, implementation
- Presentation showcasing project features and functionality
Project on Data Aquisition and Analysis
Semester 2: Machine Learning Fundamentals
- Introduction to Machine Learning
- Regression and classification models
- Feature Engineering and Data Analysis Techniques
- Unsupervised Learning
- 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
- 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
Optimizing Machine Learning Analytics
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
- 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
- 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
Deep Learning for Intelligent Data Analysis and Deployment
Semester 4: Advanced Diploma in comprehensive Development of Automotive and AUTOSAR Systems
- Advanced Deep Learning Models
- Optimization Techniques
- Model Interpretability and Explainability
- Ethical AI, Secure Data and Research-Driven Cloud Deployment
- 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
- 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.
Developing and Optimizing Advanced AI Models
NEWS & EVENTS
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GLIMPSE OF THE LAUNCH
