Course Description

The Integrated Programme in Business Analytics equips participants with essential skills to excel in the dynamic field of business analytics. It integrates foundational business concepts with advanced analytics techniques, fostering a holistic understanding of data-driven decision-making. Key components include:

• Core business disciplines that provide a basis for applying analytics in real-world contexts.
• Hands-on training in data manipulation, statistical analysis, and visualization tools.
• Advanced techniques for building predictive models and uncovering patterns in data.
• Practical experience through real-world projects, providing insights into analytics across diverse sectors.

Module 1

Describing and Summarizing Data

Recognize trends in data and detect outliers, summarize data sets concisely, and analyze relationships between variables.

Highlights

Histograms and Summarizing Data

Understand How to Interpret Outliers

Concepts

1. Visualizing Data
2. Descriptive Statistics
3. Relationship Between Two Variables

Featured Exercises

1. Create visual representations of data in Excel
2. Define and calculate descriptive statistics
3. Create scatter plots and calculate the correlation coefficient
   

Module 2

Sampling and Estimation

Create representative samples and draw conclusions about the larger population and craft sound survey questions.

Highlights

• Sampling
• Calculating Confidence Intervals

Concepts

1. Creating Representative and Unbiased Samples
2. The Normal Distribution
3. Confidence Intervals
4. Amazon's Inventory Sampling

Featured Exercises

• Calculate sample statistics and apply the properties of the normal distribution
• Calculate confidence intervals to estimate the accuracy of statistics

Module 3
Hypothesis Testing
Quantify the evidence in favor of or against your hypothesis in order to make managerial decisions.

Highlights

• Hypothesis Testing
• Interpreting A/B Tests

Concepts

1. Designing and Performing Hypothesis Tests
2. Improving the Customer Experience

Featured Exercises

• Develop and test hypotheses in Excel to assess the impact of changes on an entire population or estimate differences between populations
• Interpret the results of a series of website A/B tests

Module 4
Single Variable Linear Regression
Analyze the relationship between two variables and develop forecasts for values outside the data set.

Highlights

• Prediction Intervals
• Interpreting Regression Analysis
  
  

Concepts

1. The Regression Line
2. Forecasting
3. Interpreting the Regression Output
4. Performing Regression Analyses
5. Forecasting Home Video Units

Featured Exercises

• Identify the best fit line for a data set and interpret its equation through an analysis of housing data
• Perform a regression analysis of box office and home video sales using Excel and interpret the output

Module 5
Multiple Regression
Identify relationships among three or more variables to improve understanding of data and provide better forecasts.

Highlights

• Single Versus Multiple Regression
• Adjusted R-Squared
• Improving the Model

Concepts

1. The Multiple Regression Equation
2. Adapting Concepts from Single Regression
3. Performing Multiple Regression Analyses
4. New Concepts in Multiple Regression
5. The Caesars Staffing Problem

Featured Exercises

• Estimate the relative predictive power of different combinations of variables by performing and interpreting a multiple variable regression analysis using Excel
• Apply multiple regression analysis to a staffing challenge faced by a hotel
• Expand the range of your analysis by using dummy and lagged variables