: Istanbul-Turkey: +905395991206 - Amman-Jordan: +962785666966 London-UK: +447481362802
-->

Management & Leadership Courses

Data Analytics for Managerial Decision Making




Introduction:

This 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. This 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

Targeted Groups:

  • 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 Objectives:

At the end of this course the 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

Targeted Competencies:

  • Applications of data analytics in management
  • Data analytics
  • Applying data analytical methods through worked examples
  • Focusing on management interpretation of statistical evidence
  • Integrating the statistical thinking into the work domain

Course Content:
Unit 1: Setting the Statistical Scene in Management:

  • 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

Unit 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

Unit 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

Unit 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

Unit 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