| Topics for Practical |
1. Introduction to Business Analytics
Collect data from a real-life business scenario & perform exploratory data analysis (EDA) to gain insights into the dataset.
Analyze customer data to identify trends and patterns that can be used for business decision-making. |
2. Describing the Distribution of a Variable
Obtain a dataset and calculate descriptive statistics (mean, median, mode,variance, etc.) for a specific variable of interest
Create visualizations (histograms, box plots) to depict the distribution of a variable and analyze its characteristics |
3. Finding Relationships Among Variables
Use a dataset with multiple variables and perform correlation analysis to determine the strength and direction of relationships between pairs of variables
Apply regression analysis to identify the relationship between an independent variable (e.g., advertising expenditure) & a dependent variable (e.g., salesrevenue) |
4. Business Intelligence Tools for Data Analysis
Utilize a business intelligence tool (e.g., Tableau, Power BI) to extract insights from a dataset and create interactive visualizations for effective data analysis |
5. Probability and Probability Distributions
Simulate a probability experiment (e.g., rolling dice) using programming and calculate the probabilities of different outcomes
Generate random numbers from various probability distributions (normal, uniform, exponential) and analyze their properties |
6. Decision Making under Uncertainty
Develop a decision tree model to make business decisions considering uncertainties and associated probabilities at each decision point
Apply the concept of expected value to evaluate different decision alternatives and select the optimal one. |
7. Sampling and Sampling Distributions
Conduct a survey and collect data from a sample population, ensuring proper sampling techniques are employed
Use the Central Limit Theorem to analyze the sampling distribution of a sample mean and estimate population parameters |
8. Confidence Interval Estimation
Calculate confidence intervals for population means or proportions using sample data and interpret the results in a business context
Apply bootstrapping techniques to estimate confidence intervals for nonparametric statistics |
9. Hypothesis Testing
Formulate null and alternative hypotheses related to a business problem, conduct a hypothesis test using appropriate statistical tests, and interpret the results
Perform A/B testing on a website or marketing campaign to evaluate the effectiveness of different strategies and make data-driven decisions |
10. Regression Analysis and Time Series Analysis
Develop a regression model to predict future sales based on historical data, assess model performance, and interpret the significance of predictor variables
Apply time series analysis techniques (e.g., ARIMA, exponential smoothing) to forecast future demand for a product or service, and evaluate the accuracy of the forecasts. |
11. Optimization Modeling and Simulation Modeling
Formulate an optimization model (e.g., linear programming, integer programming) to solve a real-world business problem and analyze the optimal solution
Use simulation modeling to evaluate different business scenarios, such as capacity planning, inventory management, or pricing strategies, and assess their impact on performance metrics |
12. Analysis of Variance and Experimental Design
Design and conduct an experiment to study the effects of different factors on a specific response variable, analyze the results using analysis of variance (ANOVA), and draw conclusions
Implement a factorial experiment and analyze the main effects and interaction
effects of factors using statistical techniques |