Data Science Modelling with Excel

Programme outline

Learning Objectives and Structure

By the end of this module, participants will be able to

1. Appreciate the use of basic model by junior data scientist in a general setting
2. Adopt a statistical approach to solving ambiguous issues
3. Apply computational thinking to solve business problem
4. Leverage on Basic Supervised and Unsupervised Learning Model appropriately for Business use case
5. Conduct statistical test on Business use case
6. Experiment with various machine learning model specific to business context
7. Evaluate various parameters based on the consequence of the predicted model
8. Decipher and deconstruct convoluted patterns into meaningful insights

Programme Structure: Participants will go through 4 days of training. Class will reconvene on the 5th day for a presentation as part of the course assessment.

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

• Project Presentation

Assessment

Participants will be assessed via group based project presentation on the 5th session of the course. There will also be formative assessment and case studies to assess a participant’s understanding and competency.

Subject Credits

Upon completion and satisfying the requirements of passing this course, learners will be awarded 12 subject credits.