Mastering Data Analysis in Excel
Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality. This course will prepare you to design and implement realistic predictive models based on data.
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- Level Foundation
- Duration 21 hours
- Course by Duke University
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Offered by
About
Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality. This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits. The two models should demonstrate to you in a practical, hands-on way the idea that your choice of business metric drives your choice of an optimal model. The second big idea this course seeks to demonstrate is that your data-analysis results cannot and should not aim to eliminate all uncertainty. Your role as a data-analyst is to reduce uncertainty for decision-makers by a financially valuable increment, while quantifying how much uncertainty remains. You will learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including classification error rates, entropy of information, and confidence intervals for linear regression. All the data you need is provided within the course, all assignments are designed to be done in MS Excel, and you will learn enough Excel to complete all assignments. The course will give you enough practice with Excel to become fluent in its most commonly used business functions, and you'll be ready to learn any other Excel functionality you might need in the future (module 1). The course does not cover Visual Basic or Pivot Tables and you will not need them to complete the assignments. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions. You will emerge with substantial vocabulary and practical knowledge of how to apply business data analysis methods based on binary classification (module 2), information theory and entropy measures (module 3), and linear regression (module 4 and 5), all using no software tools more complex than Excel.Modules
Introduction to the Specialization
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1
Videos
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1
Readings
1
Videos
- About This Specialization
1
Readings
- Specialization Overview
Introduction to this Course
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1
Videos
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1
Readings
1
Videos
- Introduction to Mastering Data Analysis in Excel
1
Readings
- Course Overview
Basic Excel Syntax
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4
Videos
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1
Readings
4
Videos
- Introduction to Using Excel in this Course
- Basic Excel Vocabulary; Intro to Charting
- Arithmetic in Excel
- Functions on Individual Cells
1
Readings
- Tips for Success
Functions on Arrays
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4
Videos
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2
Assignment
2
Assignment
- Excel Essentials Practice
- Excel Essentials
4
Videos
- Functions on a Set of Numbers
- Functions on Ordered Pairs of Data
- Sorting Data in Excel
- Introduction to the Solver Plug-in
Binary Classification and the Confusion Matrix
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6
Videos
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1
Readings
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2
Assignment
2
Assignment
- Binary Classification (practice)
- Binary Classification (graded)
6
Videos
- Introduction to Binary Classification
- Bombers and Seagulls: Confusion Matrix
- Costs Determine Optimal Threshold
- Calculating Positive and Negative Predictive Values
- How to Calculate the Area Under the ROC Curve
- Binary Classification with More than One Input Variable
1
Readings
- Tips for Success
Introduction to Measuring Uncertainty
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3
Videos
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1
Readings
3
Videos
- Quantifying the Informational Edge
- Probability and Entropy
- Entropy of a Guessing Game
1
Readings
- Tips for Success
New Data and Information Gain
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4
Videos
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2
Assignment
2
Assignment
- Using the Information Gain Calculator Spreadsheet (practice)
- Information Measures (graded)
4
Videos
- Dependence and Mutual Information
- The Monty Hall Problem
- Learning from One Coin Toss, Part 1
- Learning From One Coin Toss, Part 2
Introduction to Parametric Models
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7
Videos
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1
Readings
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1
Assignment
1
Assignment
- The Gaussian (practice)
7
Videos
- Introducing the Gaussian
- Introduction to Standardization
- Standard Normal Probability Distribution in Excel
- Calculating Probabilities from Z-scores
- Central Limit Theorem
- Algebra with Gaussians
- Markowitz Portfolio Optimization
1
Readings
- Tips for Success
Unpacking Linear Regression
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4
Videos
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2
Assignment
2
Assignment
- Regression Models and PIG (practice)
- Parametric Models for Regression (graded)
4
Videos
- Standardizing x and y Coordinates for Linear Regression
- Standardization Simplifies Linear Regression
- Modeling Error in Linear Regression
- Information Gain from Linear Regression
Concepts Needed for Final Project
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4
Videos
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1
Readings
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1
Assignment
1
Assignment
- Probability, AUC, and Excel Linest Function
4
Videos
- Describing Histograms and Probability Distributions Functions
- Some Important and Frequently Encountered PDFs
- Linear Regression with More than One Input Variable
- Understanding Why Over-fitting Happens
1
Readings
- AUC Calculator Explanation and Spreadsheet
Case Study: Modeling Credit Card Default Risk and Customer Profitability
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2
Videos
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3
Readings
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1
PeerReview
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4
Assignment
4
Assignment
- Part 1: Building your Own Binary Classification Model
- Part 2: Should the Bank Buy Third-Party Credit Information?
- Part 3: Comparing the Information Gain of Alternative Data and Models
- Part 4: Modeling Profitability Instead of Default
1
Peer Review
- Part 5: Modeling Credit Card Default Risk and Customer Profitability
2
Videos
- Final Project Information: Part 1
- Final Project Information: Part 2
3
Readings
- Final Project Information
- Summary of Learning Points for Final Project: Quiz 1
- Summary of Learning Points for Final Project: Quiz 2
Auto Summary
"Mastering Data Analysis in Excel" focuses on data-analysis concepts and methods within the realm of Data Science & AI, using Excel as a tool for calculations. Taught by Coursera, this foundational course spans 1260 minutes and emphasizes practical, hands-on learning, culminating in a project where you'll develop predictive models for a bank. The course covers binary classification, information theory, entropy measures, and linear regression, all within Excel. Ideal for beginners, it includes a Starter subscription option and prepares learners to apply business data analysis methods effectively.
Jana Schaich Borg
Daniel Egger