Business Decision-Making using Data Analysis

Introduction:

This interactive, application-driven five-day data analysis in business decision-making course will highlight the added value that data analytics can offer professionals as a decision-support tool in management decision-making. It will show how data analytics can support strategic initiatives, inform policy information, and direct operational decision-making.

The data analysis in business decision-making course will emphasize data analytics applications in management practice, focus on validly interpreting data analytics findings, and create a clearer understanding of integrating 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.

The Importance of Data Analytics in Business Decision-Making:

Understanding and utilizing data analytics for business decision-making is crucial in today's data-driven world. Incorporating data analysis in the business decision-making process helps leaders make informed, evidence-based decisions that drive strategic initiatives forward. By defining business decision-making within the context of data analytics, this course aims to develop your ability to apply data analytics effectively, enhancing your business decision-making skills.

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 data analysis in business decision-making 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:

Upon the end of this data analysis in business decision-making training, the target competencies will be able to:

• 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 Content:

Unit 1: 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.

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).
• Explore relationships between numeric descriptors.
• Breakdown analysis of numeric measures.

Unit 3: Statistical Decision Making – Drawing Inferences from Sample Data:

• The foundations of statistical inference.
• Quantify uncertainty in data – the normal probability distribution.
• The importance of sampling in inferential analysis.
• Sample methods (random-based sampling techniques).
• Understand the sampling distribution concept.
• Confidence interval estimation.

Unit 4: Statistical Decision Making – Drawing Inferences from Hypotheses Testing:

• The rationale of hypothesis 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.
• Compare means across multiple populations.

Unit 5: Predictive Decision Making – Statistical Modeling and Data Mining:

• Exploit 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.