This course is part of the Excel to MySQL: Analytic Techniques for Business Specialization

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Excel to MySQL: Analytic Techniques for Business SpecializationDuke University

About this Course

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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.

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Earn a Certificate upon completion

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Course 2 of 5 in the

Excel to MySQL: Analytic Techniques for Business Specialization

Approx. 21 hours to complete

English

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Skills you will gain

Binary Classification
Data Analysis
Microsoft Excel
Linear Regression

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Excel to MySQL: Analytic Techniques for Business Specialization

Approx. 21 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.14/5 (214 Ratings)

Jana Schaich Borg

Assistant Research Professor

Social Science Research Institute

695,788 Learners

7 Courses

Daniel Egger

Executive in Residence and Director, Center for Quantitative Modeling

Pratt School of Engineering, Duke University

1,009,314 Learners

8 Courses

Offered by

Duke University

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Syllabus - What you will learn from this course

Content Rating93%(40,888 ratings)

Week1

Week 1

1 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.

1 hour to complete

2 videos (Total 11 min), 2 readings

2 hours to complete

Excel Essentials for Beginners

2 hours to complete

8 videos (Total 52 min), 1 reading, 2 quizzes

Week2

Week 2

2 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.

2 hours to complete

6 videos (Total 46 min), 1 reading, 2 quizzes

Week3

Week 3

2 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.

2 hours to complete

7 videos (Total 42 min), 1 reading, 2 quizzes

Week4

Week 4

3 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.

3 hours to complete

11 videos (Total 73 min), 1 reading, 3 quizzes

11 videos

Introducing the Gaussian1mIntroduction to Standardization4mStandard Normal Probability Distribution in Excel7mCalculating Probabilities from Z-scores4mCentral Limit Theorem3mAlgebra with Gaussians6mMarkowitz Portfolio Optimization12mStandardizing x and y Coordinates for Linear Regression6mStandardization Simplifies Linear Regression9mModeling Error in Linear Regression10mInformation Gain from Linear Regression5m

1 reading

Tips for Success10m

3 practice exercises

The Gaussian (practice)30mRegression Models and PIG (practice)45mParametric Models for Regression (graded)45m

Week5

Week 5

1 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.

1 hour to complete

4 videos (Total 37 min), 1 reading, 1 quiz

Week6

Week 6

10 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.

10 hours to complete

2 videos (Total 14 min), 3 readings, 5 quizzes

2 videos

Final Project Information: Part 13mFinal Project Information: Part 210m

3 readings

Final Project Information20mSummary of Learning Points for Final Project: Quiz 110mSummary of Learning Points for Final Project: Quiz 210m

4 practice exercises

Part 1: Building your Own Binary Classification Model1hPart 2: Should the Bank Buy Third-Party Credit Information?2hPart 3: Comparing the Information Gain of Alternative Data and Models2hPart 4: Modeling Profitability Instead of Default2h

Reviews

4.2

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5 stars
56.28%
4 stars
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3 stars
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2 stars
5.23%
1 star
5.23%

TOP REVIEWS FROM MASTERING DATA ANALYSIS IN EXCEL

by PSMay 7, 2020

Quite comprehensive on the usage of concepts taught in the course. However, bit of diversion seems to come in assignments and quizzes. Overall, very challenging and fulfilling.

by JEOct 30, 2015

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.

by TRNov 26, 2017

A challenging course that rewards conscientious effort. Heavy on math, both practical and theoretical. Highly recommend comfort with college level statistics before taking this course.

by KKSep 17, 2017

An in depth deep dive into the mathematical concepts needed to be understood for data analysis tools and techniques. An all round good introduction into the field of data analytics.

View all reviews

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.

Excel to MySQL: Analytic Techniques for Business

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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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