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Data Science and Decision Making

Overview
Programme Outline
Course Fees and Funding
Instructors
Policies and Financing Options

Programme Outline

Learning Objectives and Structure

By the end of this module, participants would be able to:

  1. Develop skillsets for formulating and driving digital strategy and digital transformation
  2. Understand how change driven by digital transformation may affect your value chain and value generation
  3. Draw the “big picture” on different technological innovation drivers and business model disruptors
  4. Learn the key elements for creating and capturing value through digital strategies, data monetisation, and digital platforms business

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
  • Data strategy
  • Data as an economic good
  • Big data good
  • Data sharing
  • Data governance and data quality management
  • Data and analytics culture
  • Field definitions of analytics and machine learning
  • The three types of learning
  • Typical case scenarios in analytics
  • Models, accuracy, and generalisation
  • Case examples
  • Course summary
Day 2
  • Data Science Toolbox and applications: Descriptive and diagnostics
  • Group work: Application of diagnostic analytics in a case
  • Data Science Toolbox and applications: Predictive analytics pt 1.
  • Data Science Toolbox and applications: Predictive analytics pt 2: Model selection and value of prediction
  • Group work: Predictive analytics minicase
  • Data Science Toolbox and applications: Prescriptive analytics
  • Group discussion: From predictive to prescriptive analytics: identification of opportunities at your company
  • Effective decision making with data science
  • Group work: Analytics canvas application for your company case
  • Summary: Creating value with data science for business decisions

 

Day 3
  • Overview of the data science process
  • Applications of data science and decision making
  • Exercise: Discussion of case studies
  • Characteristics and types of data
  • Collecting, storing and representing data
  • Data pre-processing and wrangling
  • Exercise: Data collection and handling
  • Big data and analytics
  • Understanding trends from data
  • Identifying outliers
  • Exercise: Hands-on lab

 

Day 4
  • Best practices in data visualisation
  • Types of graphs and charts
  • Tools for visualisation
  • Exercise: Visualising and presenting results
  • Supervised learning: Classification and regression
  • Unsupervised learning: Clustering
  • Experimentation and evaluation process
  • Exercise: Hands-on lab on classification
  • Data science in different businesses
  • Social, ethical and legal considerations
  • Limitations of data science
  • Exercise: Application of data science for decision making

 

Day 5
  • Project Presentation

 

Assessment

Multiple methods of assessment are used to provide an opportunity for the participant to demonstrate their learning results with a variety of learning styles.

 

These include:

  • Pre-assignment due before in-class session
  • Class contribution
  • In-class assignments (group work and presentations during the module)
  • Take-home assignment due after in-class session

 

Subject Credits

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