By the end of this course delegates 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

Setting the Statistical Scene in Management

Competency Description: As a manager you need to develop quantitative reasoning skills to support evidence-based decision making.

 Key behaviors:

• Appreciate the role of data analytics in management decision making
• Understand the scope and structure of data analytics
• Understand the importance of data quality and data integrity
• Develop a practical ability to prepare data for statistical analysis
• Be able to interpret summary tables and graphs to extract key information

Topics to be covered:

• 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

Evidence-based Observational Decision Making

Competency Description: As a manager you need to interpret summarized numerical sample evidence to support management decision making.

Key behaviors:

• Be able to interpret numeric sample descriptive measures to profile sample evidence
• Distinguish between different central location measures
• Understand how to quantify and interpret variability in data
• Recognize data outliers and their impact on data validity
• Identify influencing factors on key measures performance

Topics to be covered:

• 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

Statistical Decision Making – Drawing Inferences from Sample Data

Competency Description: As a manager you need to distinguish between chance and genuine occurrences or relationships in practice based on sample evidence.

Key behaviors:

• Appreciate the fundamental concepts to infer sample evidence to business practice
• Understand how to manage (measure and interpret) business uncertainty
• Prepare and interpret confidence interval estimates of key performance measures

Topics to be covered:

• 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

Testing Statistical Decision Making – Drawing Inferences from Hypotheses

Competency Description: As a manager you need to demonstrate an ability to base management decisions on rigorously tested sample evidence.

Key behaviors:

• Be able to formulate management questions as testable statistical hypotheses
• Understand the principle and methodology of testing statistical hypotheses
• Be able to interpret statistical hypotheses conclusions in a management context
• Choose the appropriate hypotheses test for on a given management scenario

Topics to be covered:

• 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

Predictive Decision Making - Statistical Modeling and Data Mining

Competency Description: As a manager you need to prepare forecasts or future estimates of key performance measures based on identified influencing factors.

Key behaviors:

• Understand the model-building environment
• Be able to identify significant influencing factors on a key performance measure
• Interpret the relative importance of each significant factor on the key performance measure
• Prepare future estimates / forecasts based on the identified relationships
• Appreciate the strategic value of mining large data sets of business activities
• Distinguish between goal-directed data mining and descriptive data mining

Topics to be covered:

• 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
• Descriptive data mining – applications in management

This course 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