This interactive Data Analytics for Managerial Decision Making training 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 training on data analytics for managerial decision making 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.

COURSE OBJECTIVES

By the end of the course, participants should 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

WHO SHOULD ATTEND

This training on data analytics for managerial decision making is suitable to a wide range of professionals but will greatly benefit:

• 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 CONTENT

Day One: 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 Two: 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 Three: 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 Four: 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 Five: 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