In this module, students build competency in business statistics and data analysis. The focus is on understanding and applying statistical techniques to solve business problems and guide decision-making. Students learn how to collect data, summarize it meaningfully, and draw conclusions about larger trends or differences (inferential statistics). They will practice using statistical tools (such as spreadsheets or specialized software) to perform analyses like hypothesis tests and regression modeling. The integration of AI comes through exposure to how statistical foundations underlie modern analytics and machine learning – for example, how regression is used in predictive algorithms. By mastering statistics, students gain the quantitative reasoning skills essential for data-driven management in an AI-augmented business world.
Registered Title: (Statistics for Business (Classroom & Asynchronous))
TPGateway Code:
32:00
On Campus
Learning Outcome: Data types (categorical vs. numerical), data collection methods in business, frequency distributions, visualization tools (charts, graphs), measures of central tendency and dispersion.
Learning Outcome: Basic probability rules, discrete and continuous distributions commonly used in business (Binomial, Normal distribution), and their applications (e.g., quality control, risk assessment).
Learning Outcome: Sampling techniques, survey design principles, and the Central Limit Theorem – how it allows estimation about populations from samples.
Learning Outcome: Confidence interval construction for means/proportions, hypothesis testing procedure (null vs. alternative hypothesis, p-values) with business examples (e.g., A/B testing of a marketing campaign).
Learning Outcome: Introduction to correlation and linear regression analysis to identify relationships between variables (e.g., advertising spend vs. sales revenue), time series analysis basics for forecasting trends.
Learning Outcome: Practical exercises in Excel or a statistical software (such as Python with libraries or SPSS) to perform analysis; discussion of how these techniques scale in big data environments and feed into AI/machine learning models for business analytics.
