Programme Outline
Learning Objectives and Structure
- Gain a good understanding of the underlying principles and concepts of fundamental data science models and algorithms, including regression, classification, clustering, and dimensionality reduction.
- Demonstrate proficiency in assessing the strengths and limitations of various data science models and algorithms given the characteristics of the dataset.
- Apply practical skills to effectively implement the appropriate data science models and algorithms to resolve business problems.
- Identify and interpret patterns, trends, and meaningful insights within datasets using the applied data science models and algorithms.
- Acquire the ability to analyze and compare the results obtained from different data science models and algorithms to extract and enhance insights and predictions.
- Understand healthcare case studies shared by SingHealth faculty members to gain insights into real-world scenarios.
- Utilise curated public healthcare datasets to perform hands-on activities and assignments, fostering practical experience and understanding of the subject matter.
Day 1
- Basics of Statistics
- Application of Statistics in Real World
- Introduction to Quantitative Intuition for Statistics
- Steps in Hypothesis Testing
- Z Test
- T Test
Day 2
- Overview of Data Science
- Data Science Pipeline
- What is Machine Learning Model?
- Understand what is required prior to using machine learning model
- Understand how data scientist trains machine learning model
- Data Preparation and Data Validation
- Train-Test Split and Cross Validation
- An introduction to Supervised and Unsupervised Learning
Day 3
- Data Preparation for Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
- Evaluating Linear Regression Models Performance
- An extension of regression on Correlation, Covariance and Multi-collinearity Issues
- Remedies for Multi-Collinearity
- Data Preparation for Logistic Regression
- Logistic Regression Model
- Evaluating Logistic Regression Models Performance
Day 4
- Data Preparation for Clustering
- K-Means Clustering
- Dimensionality Reduction
- Basics of Principal Component Analysis
- Interpreting Principal Component Analysis Results
Day 5 – Consultation / Project presentation
Project Consultation
Each group of participants will present the progress of their projects and have the opportunity to ask questions and clarify any doubts pertaining to their projects.
Project Presentation
Each group of participants will showcase their work and respond to questions during a Q&A session.