Course Outline
Introduction and Overview
- Why data and data analysis is important?
- What is data and what is not – The types of data?
- Where of data – Public vs private data?
Data Driven Decision-making
- What is data driven decision-making?
- How to use data to drive decision-making?
Data and AI Landscape
- The role of technology
- AI – what, why and how and the role that data plays?
Application: Machine learning using no code.
Four Secrets to Creating Actionable Insights
- Interesting vs actionable data
- The phases to move between to create actionable insights
Application: Creating actionable insights at the individual and organisational level.
Industry Case Studies on Deriving Intelligence from Data
- How different industries are using intelligence and the benefits and short-falls?
- How different business units/divisions are using data for intelligence?
Application: Identifying areas that could use data for intelligence.
Data Analysis: Fundamentals
- Averages and pitfalls
- Median Mode
- Standard Deviation
Application: Analysing a dataset using Microsoft Excel tools such as Pivot table functionality.
Data Visualisation
- Types of data and the appropriate visualisations
- Rules of data visualisation
- Ethics of data visualisation
Application: Data visualisation based on data provided.
Tools Required for Dashboarding
- Camera tool Developer tab
- Data analysis tool Pak
Application: Activation and use of tools activated.
Dashboarding
- Dashboard vs report
- Rules, guidelines and framework for the development of a dashboard
- Static vs dynamic dashboards
Application: Development of a dynamic dashboard in Excel using tools activated.
Problem Solving Using data
- Introduction to problem solving framework and approach
- Root cause analysis
- Data collection
Application: Problem solving using data based on a case study.
Probabilities
- What are probabilities?
- How to think probabilistically?
- Calculation of probabilities – Expected Monetary Value
Application: Decision-tree development with probabilities.
Advanced Data Analysis (1)
- Tests for differences
- T-tests – When to use?
- Anova – When to use?
Application: Analysis of dataset – t-test and ANOVA.
Advanced Data Analysis (2)
- Tests of association
- Correlation vs causation
- Pearson correlation
- Spearman correlation
Application: Analysis of dataset – Pearson’s correlation.
Advanced Data Analysis (3)
- Test for prediction
- Regression
Application: Analysis of dataset – Regression analysis.
Strategy Formulation with Data
- What is strategy and how it changes in the data world?
- Framework for strategy development with data
- Capabilities required
Application: Evaluation of capabilities.
Recap of Course