Module #1 Introduction to Statistical Tools for Business Decision Making Overview of the role of statistics in business decision making, importance of data-driven decisions, and course objectives.
Module #2 Descriptive Statistics Measures of central tendency, variability, and data visualization techniques.
Module #3 Data Collection and Sampling Types of data, data collection methods, and sampling techniques.
Module #4 Probability and Risk Analysis Basic concepts of probability, probability distributions, and risk analysis in business.
Module #5 Confidence Intervals Construction and interpretation of confidence intervals for means and proportions.
Module #6 Hypothesis Testing Introduction to hypothesis testing, types of errors, and test statistics.
Module #7 One-Sample Tests Hypothesis testing for means and proportions, including z-tests and t-tests.
Module #8 Two-Sample Tests Hypothesis testing for differences between means and proportions, including t-tests and ANOVA.
Module #9 Regression Analysis Simple and multiple linear regression, coefficient interpretation, and model assumptions.
Module #10 Time Series Analysis Introduction to time series components, trend analysis, and forecasting techniques.
Module #11 Forecasting Methods Moving average, exponential smoothing, and ARIMA models for forecasting.
Module #12 Chi-Square Tests and Non-Parametric Tests Goodness-of-fit tests, tests for independence, and non-parametric tests.
Module #13 ANOVA and Experimental Design One-way and two-way ANOVA, randomized block designs, and factorial designs.
Module #14 Correlation Analysis Measures of correlation, correlation coefficients, and interpretation.
Module #15 Data Visualization Effective visualization techniques for business data, including charts, graphs, and plots.
Module #16 Statistical Software for Business Overview of popular statistical software packages, including R, Excel, and Python.
Module #17 Big Data and Statistical Analysis Challenges and opportunities of big data, and statistical techniques for big data analysis.
Module #18 Machine Learning for Business Introduction to machine learning, supervised and unsupervised learning, and model evaluation.
Module #19 Decision Trees and Random Forests Decision tree models, random forests, and feature importance.
Module #20 Clustering and Segmentation K-means clustering, hierarchical clustering, and market segmentation.
Module #21 Case Studies in Business Analytics Real-world examples of statistical tools and machine learning techniques in business.
Module #22 Effective Communication of Statistical Results Best practices for presenting statistical results to non-technical stakeholders.
Module #23 Ethics in Statistical Analysis Ethical considerations in data collection, analysis, and interpretation.
Module #24 Statistical Tools for Financial Analysis Statistical techniques for financial analysis, including time series analysis and risk modeling.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Statistical Tools for Business Decision Making career
