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.