Data Analytics for Managerial Decision Making

Introduction:

This course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. This course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create 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

Targeted Groups:

• Professionals in management support roles
• Analysts who typically encounter data / analytical information regularly in their work environment
• Those who seek to derive greater decision making value from data analytics

Course Objectives:

At the end of this course the participants will be able to:

• Appreciate data analytics in a decision support role
• Explain the scope and structure of data analytics
• Apply a cross-section of useful data analytics
• Interpret meaningfully and critically assess statistical evidence
• Identify relevant applications of data analytics in practice

Targeted Competencies:

• Applications of data analytics in management
• Data analytics
• Applying data analytical methods through worked examples
• Focusing on management interpretation of statistical evidence
• Integrating the statistical thinking into the work domain

Course Content:
Unit 1: Setting the Statistical Scene in Management:

• 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

Unit 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

Unit 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

Unit 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

Unit 5: Predictive Decision Making - Statistical Modeling 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