Business Analytics for Decision Making Notes
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.
Subject at a glance
4
Units
27
Topics
4
Credits
4
Infographics
Unit-wise Syllabus
4 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- 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
- 1Collect data
Primary and secondary sources
- 2Clean
Errors, duplicates, outliers
- 3Handle missing values
Delete or impute
- 4Explore (EDA)
Summaries and charts
- 5Reduce dimensions
Factor analysis, PCA
Study guide
This unit covers statistics in business decisions, data types and sources, preprocessing, cleaning, missing values, frequency distributions, SPSS, census versus sampling, sampling methods, exploratory analysis and dimension reduction. Practise cleaning a real data set in SPSS or Excel.
- 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
t-test
Difference in two means (small sample)
Do two branches differ in average sales?
ANOVA (F-test)
Difference in three or more means
Do four ad campaigns differ in response?
Chi-square
Association between categories
Is gender linked to product preference?
Z-test
Means or proportions, large samples
Has the defect rate changed?
Study guide
You study sampling distributions, hypothesis testing errors, Z, t, F, chi-square and ANOVA tests in SPSS, association, visualisation, clustering and PCA. Choose the correct test for each data situation.
- 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
Linear regression
A number
Next quarter's sales
Logistic regression
Probability of a class
Will this customer churn?
k-NN
Class from similar cases
Credit risk band
Decision tree
Class or number by rules
Loan approval
Study guide
You study forecasting, correlation and regression, assumption testing (multicollinearity, heteroscedasticity, autocorrelation), linear and logistic regression, k-NN, decision trees and model evaluation. Build and evaluate one regression model.
- 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
Trend (T)
Long-run direction
Seasonal (S)
Regular yearly pattern
Cyclical (C)
Business-cycle swings
Irregular (I)
Random shocks
Models
Additive Y = T + S + C + I; multiplicative Y = T × S × C × I
Study guide
You study time series components and methods, trend analysis, combining predictive and descriptive models and writing analytical reports with recommendations. Present findings in a short business report.
Why Business Analytics for Decision Making Matters
Analytics is among the most in-demand management skills, with roles in business analysis, consulting, marketing analytics and operations.
How to Prepare for Business Analytics for Decision Making
- Work on a real business data set from cleaning to recommendation.
- Learn to interpret test outputs, not only run them.
- Present results to a non-technical audience.
Business Analytics for Decision Making: Frequently Asked Questions
What is multicollinearity?
Multicollinearity occurs when predictor variables in a regression are highly correlated with each other, making their individual effects hard to estimate.