This interactive, applications-driven 5-day 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. The 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.
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?
IT Support Staff
Any member of an IT team involved in the delivery of IT Services.
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