NUS Curriculum
Introduction to Data Analytics
- What is Data Analytics?
- Types of Data Analytics
- Data in Data Analytics
- Decision Models
- Data Mining Process
- Overview of Predictive Analytics
Exploratory Data Analysis
- Data Visualisation
- Data Querying
- Statistical Methods for Summarizing Data
- Exploring Data Using Pivot Tables
Descriptive Statistical Measures
- What is Descriptive Analytics?
- Populations and Samples
- Measures of Location
- Measures of Dispersion
- Measures of Shape
- Measures of Association
Introduction to Python Data Science Libraries
- Numpy
- Scipy
- Matplotlib
- Sci-kit Learn
Introduction to Regression Analysis
- Simple Linear Regression
- Multi Linear Regression
- Stepwise Regression
- Coding Scheme for Categorical Variables
- Problems with Linear Regression
Introduction to Classification
- Decision Trees
- Bayesian Classifier
- Logistic Regression
- Multinomial Logistic Regression
- Support Vector Machine
- Separating Hyperplane
- Maximal Margin Classifier
- Support Vector Classifier
Introduction to Clustering
- Affinity Measures and Partition Methods
- K-means
- K-medoids
- Hierarchical Methods
Introduction to Association
• Structure and Representation of Association Rules
• Strong Association Rules and the Concept of Frequent Itemsets
• Apriori Algorithm
• FP Growth
• Time Series Analysis
Introduction to Text Mining
• Text Mining Terminologies
• Text Mining Concepts
• Text Mining Process
• Creating the Corpus
• Creating the Term-Document Matrix
• Extracting the Knowledge
• Knowledge Extraction Methods for Text Mining
• Classification
• Clustering
• Association
Overview of ANN
• Break-through Applications with ANN
• Why ANN?
• Problems of Logistic Regression
• Back-propagation
• Gradient Descent Algorithm (GD)
Difficulties of training ANN
• Poor Gradient
• Overfitting and Underfitting
Advanced GD algorithm
• Stochastic GD (SGD)
• Mini-batch SGD
• Momentum SGD
• RMSprop and Adam
Other Training Techniques of ANN
• Random Initialization
• ReLU
• Dropout
• Data Augmentation
HPE Curriculum
Pre-trained Models
- • Image classification
- • Object detection
- • Word embedding
Optimization and Tuning
- • Learning rate
- • Momentum
- • Optimization algorithm
- • Parameter initialization strategy
- • Data normalization
- • Batch normalization
- • Hyperparameter tuning strategy
- • Hardware acceleration
- • HPE Deep Learning Cookbook
Deep Learning Project Management
- • Deep learning project management
- • Data acquisition
- • Data preprocessing
- • Data labelling
- • Baselining
- • Data augmentation
- • Transfer learning
- • Performance measurement
- • Ensemble method
Applications of AI in
- • Fintech
- • Social Media
- • Security
Capstone Project
- • Apply deep learning to a real-life use case