Introduction

This intelligent 5-day course will feature the added value that data analytics can offer a professional as a decision support method in management decision making.

It will highlight the usage of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. The course will emphasise applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and build a clearer understanding of how to integrate quantitative reasoning into management decision making.

Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making.

Objectives

By the end of this course, participants will be able to:

– Appreciate data analytics in a decision support role.
– Explain the basics and structure of data analytics.
– Apply a cross-section of useful data analytics.
– Interpret meaningfully and critically assess statistical evidence.
– Recognise relevant applications of data analytics in practice.

Course Outline

Day 1

Setting the Statistical Scene in Management

– Introduction; The quantitative landscape in management.
– Thinking statistically about applications in management (identifying KPIs).
– The integrative elements of data analytics.
– Data: The raw material of data analytics (types, quality and data preparation).
– Exploratory data analysis using excel (pivot tables).
– Using summary tables and visual displays to profile sample data.

Day 2

Evidence-based Observational Decision Making

– Numeric descriptors to profile numeric sample data.
– Central and non-central location measures.
– Quantifying dispersion in sample data.
– Examine the distribution of numeric measures (skewness and bimodal).
– Exploring relationships between numeric descriptors.
– Breakdown analysis of numeric measures.

Day 3

Statistical Decision Making – Drawing Inferences from Sample Data

– The foundations of statistical inference.
– Quantifying uncertainty in data – the normal probability distribution.
– The importance of sampling in inferential analysis.
– Sampling methods (random-based sampling techniques).
– Understanding the sampling distribution concept.
– Confidence interval estimation.

Day 4

Statistical Decision Making – Drawing Inferences from Hypotheses Testing

– The rationale of hypotheses testing.
– The hypothesis testing process and types of errors.
– Single population tests (tests for a single mean).
– Two independent population tests of means.
– Matched pairs test scenarios.
– Comparing means across multiple populations.

Day 5

Predictive Decision Making – Statistical Modelling and Data Mining

– Exploiting statistical relationships to build prediction-based models.
– Model building using regression analysis.
– Model building process – the rationale and evaluation of regression models.
– Data mining overview – its evolution.
– Descriptive data mining – applications in management.
– Predictive (goal-directed) data mining – management applications.