Effective Business Decisions Using Data Analysis

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.