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.
This course is part of the Excel to MySQL: Analytic Techniques for Business Specialization
Mastering Data Analysis in Excel
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About this Course
106,946
106,946Skills you will gain
Binary ClassificationData AnalysisMicrosoft ExcelLinear Regression
Syllabus - What you will learn from this course
Week
11 hour to complete
About This Course
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, and all assignments are designed to be done in MS Excel. 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 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.
2 videos (Total 11 min), 2 readings
2 readings
Specialization Overview10m
Course Overview10m
2 hours to complete
Excel Essentials for Beginners
In this module, will explore the essential Excel skills to address typical business situations you may encounter in the future. The Excel vocabulary and functions taught throughout this module make it possible for you to understand the additional explanatory Excel spreadsheets that accompany later videos in this course.
8 videos (Total 52 min), 1 reading, 2 quizzes
8 videos
Basic Excel Vocabulary; Intro to Charting7m
Arithmetic in Excel2m
Functions on Individual Cells3m
Functions on a Set of Numbers10m
Functions on Ordered Pairs of Data8m
Sorting Data in Excel5m
Introduction to the Solver Plug-in8m
1 reading
Tips for Success10m
2 practice exercises
Excel Essentials Practice30m
Excel Essentials30m
Week
22 hours to complete
Binary Classification
Separating collections into two categories, such as “buy this stock, don’t but that stock” or “target this customer with a special offer, but not that one” is the ultimate goal of most business data-analysis projects. There is a specialized vocabulary of measures for comparing and optimizing the performance of the algorithms used to classify collections into two groups. You will learn how and why to apply these different metrics, including how to calculate the all-important AUC: the area under the Receiver Operating Characteristic (ROC) Curve.
6 videos (Total 46 min), 1 reading, 2 quizzes
6 videos
Bombers and Seagulls: Confusion Matrix8m
Costs Determine Optimal Threshold4m
Calculating Positive and Negative Predictive Values5m
How to Calculate the Area Under the ROC Curve11m
Binary Classification with More than One Input Variable7m
1 reading
Tips for Success10m
2 practice exercises
Binary Classification (practice)30m
Binary Classification (graded)45m
Week
32 hours to complete
Information Measures
In this module, you will learn how to calculate and apply the vitally useful uncertainty metric known as “entropy.” In contrast to the more familiar “probability” that represents the uncertainty that a single outcome will occur, “entropy” quantifies the aggregate uncertainty of all possible outcomes.
The entropy measure provides the framework for accountability in data-analytic work. Entropy gives you the power to quantify the uncertainty of future outcomes relevant to your business twice: using the best-available estimates before you begin a project, and then again after you have built a predictive model.
The difference between the two measures is the Information Gain contributed by your work.
7 videos (Total 42 min), 1 reading, 2 quizzes
7 videos
Probability and Entropy7m
Entropy of a Guessing Game7m
Dependence and Mutual Information3m
The Monty Hall Problem8m
Learning from One Coin Toss, Part 15m
Learning From One Coin Toss, Part 28m
1 reading
Tips for Success10m
2 practice exercises
Using the Information Gain Calculator Spreadsheet (practice)30m
Information Measures (graded)45m
Week
43 hours to complete
Linear Regression
The Linear Correlation measure is a much richer metric for evaluating associations than is commonly realized. You can use it to quantify how much a linear model reduces uncertainty. When used to forecast future outcomes, it can be converted into a “point estimate” plus a “confidence interval,” or converted into an information gain measure. You will develop a fluent knowledge of these concepts and the many valuable uses to which linear regression is put in business data analysis. This module also teaches how to use the Central Limit Theorem (CLT) to solve practical problems. The two topics are closely related because regression and the CLT both make use of a special family of probability distributions called “Gaussians.” You will learn everything you need to know to work with Gaussians in these and other contexts.
11 videos (Total 73 min), 1 reading, 3 quizzes
11 videos
Introduction to Standardization4m
Standard Normal Probability Distribution in Excel7m
Calculating Probabilities from Z-scores4m
Central Limit Theorem3m
Algebra with Gaussians6m
Markowitz Portfolio Optimization12m
Standardizing x and y Coordinates for Linear Regression6m
Standardization Simplifies Linear Regression9m
Modeling Error in Linear Regression10m
Information Gain from Linear Regression5m
1 reading
Tips for Success10m
3 practice exercises
The Gaussian (practice)30m
Regression Models and PIG (practice)45m
Parametric Models for Regression (graded)45m
Week
51 hour to complete
Additional Skills for Model Building
This module gives you additional valuable concepts and skills related to building high-quality models.
As you know, a “model” is a description of a process applied to available data (inputs) that produces an estimate of a future and as yet unknown outcome as output.
Very often, models for outputs take the form of a probability distribution. This module covers how to estimate probability distributions from data (a “probability histogram”), and how to describe and generate the most useful probability distributions used by data scientists. It also covers in detail how to develop a binary classification model with parameters optimized to maximize the AUC, and how to apply linear regression models when your input consists of multiple types of data for each event.
The module concludes with an explanation of “over-fitting” which is the main reason that apparently good predictive models often fail in real life business settings. We conclude with some tips for how you can avoid over-fitting in you own predictive model for the final project – and in real life.
4 videos (Total 37 min), 1 reading, 1 quiz
4 videos
Some Important and Frequently Encountered PDFs7m
Linear Regression with More than One Input Variable4m
Understanding Why Over-fitting Happens14m
1 reading
AUC Calculator Explanation and Spreadsheet10m
1 practice exercise
Probability, AUC, and Excel Linest Function20m
Week
610 hours to complete
Final Course Project
The final course project is a comprehensive assessment covering all of the course material, and consists of four quizzes and a peer review assignment. For quiz one and quiz two, there are learning points that explain components of the quiz. These learning points will unlock only after you complete the quiz with a passing grade. Before you start, please read through the final project instructions. From past student experience, the final project which includes all the quizzes and peer assessment, takes anywhere from 10-12 hours.
2 videos (Total 14 min), 3 readings, 5 quizzes
3 readings
Final Project Information20m
Summary of Learning Points for Final Project: Quiz 110m
Summary of Learning Points for Final Project: Quiz 210m
4 practice exercises
Part 1: Building your Own Binary Classification Models
Part 2: Should the Bank Buy Third-Party Credit Information?s
Part 3: Comparing the Information Gain of Alternative Data and Modelss
Part 4: Modeling Profitability Instead of Defaults
4.2
634 Reviews28%
started a new career after completing these courses
25%
got a tangible career benefit from this course
Top Reviews
The course deserves a 5-star rating because: (1) content is relevant, (2) the professor is concise and possesses great teaching skills, and (3) the learning modules are applicable to daily problems.
Overall, the course material is good with many example. Need a general knowledge with mathematical and statistical from the beginning to pass the exam, because course slide is a little bit fast.
Instructors
Jana Schaich Borg
Assistant Research ProfessorSocial Science Research Institute
Daniel Egger
Executive in Residence and Director, Center for Quantitative ModelingPratt School of Engineering, Duke University
About Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
About the Excel to MySQL: Analytic Techniques for Business Specialization
Formulate data questions, explore and visualize large datasets, and inform strategic decisions.
In this Specialization, you’ll learn to frame business challenges as data questions. You’ll use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. In the final Capstone Project, you’ll apply your skills to explore and justify improvements to a real-world business process.
The Capstone Project focuses on optimizing revenues from residential property, and Airbnb, our Capstone’s official Sponsor, provided input on the project design. Airbnb is the world’s largest marketplace connecting property-owner hosts with travelers to facilitate short-term rental transactions. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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