Business Analytics

MODULE 1: Introduction to Business Analytics

Overview of business analytics and its importance in decision-making
Types of analytics: descriptive, predictive, prescriptive
Introduction to Python programming language and libraries (NumPy, Pandas, Matplotlib)

MODULE 2: Data Exploration and Visualization

Data preprocessing techniques: cleaning, transformation, handling missing values
Exploratory data analysis (EDA) techniques
Data visualization using Matplotlib and Seaborn

MODULE 3: Descriptive Analytics

Measures of central tendency and dispersion
Data summarization techniques: mean, median, mode, variance, standard deviation
Introduction to probability distributions and inferential statistics

MODULE 4: Predictive Analytics – Regression

Linear regression: theory and implementation
Multiple linear regression
Model evaluation metrics: R-squared, adjusted R-squared, RMSE, MAE

MODULE 5: Predictive Analytics – Classification

Logistic regression: theory and implementation
Decision trees and ensemble methods (Random Forest, Gradient Boosting)
Model evaluation metrics: confusion matrix, ROC curve, precision, recall, F1-score

MODULE 6: Time Series Analysis

Introduction to time series data
Time series decomposition
Forecasting techniques: moving averages, exponential smoothing, ARIMA (AutoRegressive Integrated Moving Average)

MODULE 7: Prescriptive Analytics

Introduction to prescriptive analytics
Optimization techniques: linear programming, integer programming
Introduction to decision trees and decision analysis

MODULE 8: Application of Business Analytics

Case studies and real-world applications of business analytics in various industries (e.g., retail, finance, healthcare)
Group projects: students work on a real-world business problem applying the concepts learned throughout the course