BA: Business Analytics

Topics covered

• Data dashboarding

  • Purpose of Dashboard and use in business decision-making.
  • KPIs (Key Performance Indicators) and business metrics.
  • Presenting data through tables, charts and summary indicators.

• Data summarization

  • Converting raw data into useful summary.
  • Mean, median, mode and standard deviation.
  • Minimum, maximum, range and quartiles.

• Business insights

  • Interpreting sales, revenue, profit and customer-related metrics.
  • Identifying trends and unusual observations.
  • Extracting initial answers to business questions from summary statistics.

Topics covered

• Descriptive statistics

  • Summary of numerical and categorical variables.
  • Frequency tables, proportions and percentages.
  • Measures of central tendency and dispersion.

• Data distribution

  • Histogram, box plot and frequency distribution.
  • Symmetric and skewed distributions.
  • Outliers and unusual observations.

• Comparing groups

  • Comparison of mean, median and spread of different groups.
  • Identifying patterns from group-wise summaries.

• Statistical interpretation

  • Insights gained from sample data.
  • Difference between descriptive summary and statistical conclusion.
  • Limitations of conclusions based on summary.

Topics covered

• Categorical data analysis

  • Categorical variables and frequency distributions.
  • Contingency tables and cross-tabulation.
  • Row percentages and column percentages.

• Comparing preferences

  • City-wise comparison of brand preference.
  • Observed and expected frequencies.
  • Concept of association and independence.

• Hypothesis testing

  • Null hypothesis and alternative hypothesis.
  • Chi-square test of independence.
  • Test statistic, degrees of freedom and p-value.

• Business interpretation

  • Analysis of city-wise differences in brand preference.
  • Difference between statistical significance and practical significance.

Topics covered

• Regression introduction

  • Purpose and business applications of Regression.
  • Dependent and independent variables.
  • Difference between correlation and regression.

• Simple linear regression

  • Regression equation: ŷ = b₀+b₁x.
  • Interpretation of intercept and slope.
  • Basic idea of least squares method.

• Model fitting

  • Actual values and predicted values.
  • Residuals and residual sum of squares.
  • Interpretation of R² and model fit.

• Stock return example

  • Explanatory variables to predict stock returns.
  • Interpretation of regression output in business context.
  • Difference between prediction and causal relationship.

Topics covered

• Multiple regression

  • Use of more than one independent variable.
  • Multiple regression equation.
  • Interpretation of regression coefficients, other variables held constant.

• Regression diagnostics

  • Residual analysis.
  • Assumptions of linearity and constant variance.
  • Residual plots and unusual observations.
  • Basic idea of multicollinearity.

• Regression inference

  • Significance tests of coefficients.
  • Interpretation of confidence intervals.
  • The difference between the overall fit of the model and individual predictors.

• Path variables

  • The idea of direct and indirect relationships between variables.
  • Representing possible pathways in a business scenario.
  • Interpreting relationships through regression coefficients.

Topics covered

• Classification problem

  • Difference between continuous prediction and class prediction.
  • Binary outcomes: yes/no, success/failure, positive/negative.
  • Classification of business and healthcare applications.

• Logistic regression

  • Concept of probability prediction.
  • Logistic function and sigmoid curve.
  • Log-odds and interpretation of odds.
  • Meaning of Logistic regression coefficients.

• Classification decisions

  • Predicted probability and classification threshold.
  • Confusion matrix.
  • Basic idea of accuracy, sensitivity, specificity and precision.

• Model interpretation

  • Risk prediction and decision support.
  • Consequences of false positives and false negatives.
  • Do not consider prediction as a causal claim.

Topics covered

• Repeated measures data

  • Multiple observations from the same participants or units.
  • Difference between repeated observations and independent observations.
  • Measurement across time or treatment conditions.

• ANOVA fundamentals

  • Purpose of Analysis of Variance.
  • Between-group and within-group variation.
  • Basic interpretation of F-statistic and p-value.

• Repeated measures ANOVA

  • Repeated measures design.
  • The need to account for repeated observations.
  • Comparison of means at different time points or exposure levels.

• Advertising example

  • Repeated ad exposure and customer response.
  • To compare responses at different levels of exposure.
  • Statistical significance and business relevance.

Topics covered

• Time-series data

  • Observations collected in time order.
  • Difference between cross-sectional data and time-series data.
  • Business applications: sales, demand, prices and stock returns.

• Time-series components

  • Trend.
  • Seasonality.
  • Cyclical variation and irregular fluctuations.

• Time-series summarization

  • Time plots.
  • Moving averages.
  • Basic idea of lagged observations and autocorrelation.

• Forecasting

  • Estimating future values from historical patterns.
  • Simple forecasting methods.
  • Forecast errors and model performance.

• Business interpretation

  • Demand and sales planning.
  • Forecast uncertainty.
  • Limitations of historical patterns.