Effective Business Decisions using Data Analysis

What are the Goals?

By the end of this training course, 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

Who is this Training Course for?

This training 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   

Daily Agenda

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