Cambridge
Academy
In London

Introduction:

This interactive, application-driven 5-days 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; inform on policy information; and 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.

Course Objectives

By the end of this 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

Target Audience

This course is designed for:

·        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 Outlines

Day 1: Setting the Statistical Scene in Management:

·        Introduction: The quantitative landscape in the 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 2: 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 3: 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 4: 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 5: 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