Mastering Data Analysis in Excel Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a practical guide to mastering data analysis techniques in Excel for informed business decision-making. Designed for beginners with basic Excel knowledge, it spans approximately 7 hours of content across six modules. You'll learn to apply statistical concepts, build predictive models, and complete a hands-on final project in credit risk modeling. The course emphasizes real-world applications and decision frameworks used in business analytics.
Module 1: Introduction to Mastering Data Analysis in Excel
Estimated time: 0.6 hours
- Overview of course objectives and structure
- Introduction to the role of data analysis in business contexts
Module 2: Excel Essentials for Beginners
Estimated time: 2 hours
- Basic Excel functions and data operations
- Data sorting and filtering techniques
- Data visualization using charts and graphs
- Introduction to Solver plug-in and its usage
Module 3: Binary Classification and Predictive Modeling
Estimated time: 1 hour
- Understanding binary classification problems
- Setting up classification models in Excel
- Minimizing classification errors using Excel tools
Module 4: Information Theory and Entropy Measures
Estimated time: 1 hour
- Introduction to entropy and information gain
- Measuring uncertainty in data
- Improving model accuracy using entropy metrics
Module 5: Linear Regression and Confidence Intervals
Estimated time: 1.5 hours
- Performing linear regression analysis in Excel
- Interpreting confidence intervals
- Evaluating model fit and predictive power
Module 6: Final Project
Estimated time: 1 hour
- Create a predictive model for credit card applicants
- Balance risk minimization and profit maximization
- Submit a data-driven decision framework
Prerequisites
- Familiarity with basic Excel navigation and functions
- No prior statistics knowledge required
- Access to Microsoft Excel (any recent version)
What You'll Be Able to Do After
- Design and implement predictive models using Excel
- Apply statistical concepts like classification error rates and entropy
- Perform linear regression analysis and interpret confidence intervals
- Quantify uncertainty in business decision-making processes
- Build frameworks for data-driven decision making