Business Analytics for Decision Making
Subject Overview
Business Analytics for Decision Making develops statistical and analytical techniques for data-driven decisions — data preparation and sampling using SPSS, hypothesis testing and descriptive analytics, predictive modeling (regression, KNN, decision trees) and model evaluation, and time series forecasting and analytical report presentation. A 4-credit core theory paper.
Unit-wise Syllabus
4 units — click WhatsApp below to get the full notes for each
Unit 1: Data preparation and sampling
Importance and applications of statistics in business decisions, data types and sources, data preprocessing and cleaning, handling missing values, classification and frequency distributions, introduction to SPSS, census vs sampling, probability and non-probability sampling, exploratory data analysis and dimension reduction
Unit 2: Hypothesis testing and descriptive analytics
Sampling distributions and standard error, hypothesis testing errors, Z-test/t-test/F-test/Chi-square test/ANOVA/goodness of fit using SPSS, association of attributes, data visualization techniques, introduction to clustering and PCA
Unit 3: Predictive modeling
Business forecasting methods, correlation and regression analysis, testing assumptions (multicollinearity, heteroscedasticity, autocorrelation), linear regression, logistic regression, K-nearest neighbors, decision trees, model evaluation techniques using SPSS
Unit 4: Time series and reporting
Components and methods of time series, trend analysis using least squares, integration of predictive and descriptive models for forecasting and performance measurement, preparation and presentation of analytical reports and managerial recommendations