Data Analytics for
Managerial Decision Making

Effective and decisive decision-making is crucial for successful managers. Utilizing evidence-based information is a key factor in achieving this. Professionals can enhance their decision-making capabilities by acquiring skills in data analytics and quantitative reasoning.

The Anderson training course emphasizes the importance of data analytics in management decision-making. It provides a managerial perspective on leveraging data analytics to support strategic initiatives, inform policy development, and enhance operational decision-making. The course focuses on practical applications of data analytics in management, emphasizing the accurate interpretation of analytical findings.

By attending this Anderson training course, delegates will be able to:

• Understand the power of data analytics as a decision support tool in management
• Critically assess the validity of statistically-generated business information
• Understand the purpose of the different data analytic tools and which one/s to use in given management scenarios
• Know how to meaningfully and validly interpret statistical evidence in a management context
• Be in a position to communicate intelligently with data analytic professionals

This Anderson training course is suitable for a wide range of middle and senior managers who have the responsibility of divisional and organisational success through their decision making. It is also suitable for consultants and professionals who support them. In particular:

• HR managers
• Marketing managers
• Operations and logistics managers
• Financial managers
• Policy support personnel
• Engineers / Technical Specialists

• 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

• 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            

• 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

• 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

• 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

Anderson Certificate of Completion will be provided to delegates who attend and complete the course.