Effective Business Decisions Using Data Analysis

Jalan Conlay, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia

Description

Introduction

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.

This course will feature:

  • Discussions on applications of data analytics in management
  • The importance of data in data analytics
  • Applying data analytical methods through worked examples
  • Focusing on management interpretation of statistical evidence
  • How to integrate statistical thinking into the work domain

COURSE DETAILS

  • CODE MNG112
  • TIME    08:00 AM – 11:15 AM
  • DATE    Jan 9 2022 – Jan 13 2022
  • PLACE Kuala Lumpur , Jalan Conlay Kuala Lumpur
  • FEES PER PERSON 4250 GBP + VAT

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.

Contents

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

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