About this Course

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권장: 6 weeks, 8-10 hours per week...


자막: 영어

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Binary ClassificationData AnalysisMicrosoft ExcelLinear Regression

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 29시간 필요

권장: 6 weeks, 8-10 hours per week...


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

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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
2개의 동영상
Introduction to Mastering Data Analysis in Excel6m
2개의 읽기 자료
Specialization Overview10m
Course Overview10m
완료하는 데 2시간 필요

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
8개의 동영상
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
1개의 읽기 자료
Tips for Success10m
2개 연습문제
Excel Essentials Practice30m
Excel Essentials30m
완료하는 데 2시간 필요

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
6개의 동영상
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
1개의 읽기 자료
Tips for Success10m
2개 연습문제
Binary Classification (practice)30m
Binary Classification (graded)45m
완료하는 데 2시간 필요

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
7개의 동영상
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
1개의 읽기 자료
Tips for Success10m
2개 연습문제
Using the Information Gain Calculator Spreadsheet (practice)30m
Information Measures (graded)45m
완료하는 데 3시간 필요

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
11개의 동영상
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
1개의 읽기 자료
Tips for Success10m
3개 연습문제
The Gaussian (practice)30m
Regression Models and PIG (practice)45m
Parametric Models for Regression (graded)45m
완료하는 데 1시간 필요

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
4개의 동영상
Some Important and Frequently Encountered PDFs7m
Linear Regression with More than One Input Variable4m
Understanding Why Over-fitting Happens14m
1개의 읽기 자료
AUC Calculator Explanation and Spreadsheet10m
1개 연습문제
Probability, AUC, and Excel Linest Function20m
완료하는 데 10시간 필요

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
2개의 동영상
Final Project Information: Part 210m
3개의 읽기 자료
Final Project Information20m
Summary of Learning Points for Final Project: Quiz 110m
Summary of Learning Points for Final Project: Quiz 210m
4개 연습문제
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
634개의 리뷰Chevron Right


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최상위 리뷰

대학: JEOct 31st 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.

대학: NCDec 20th 2016

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.



Jana Schaich Borg

Assistant Research Professor
Social Science Research Institute

Daniel Egger

Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University

듀크대학교 정보

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....

MySQL 전문가 과정: 비즈니스를 위한 분석 테크닉 전문 분야 정보

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....
MySQL 전문가 과정: 비즈니스를 위한 분석 테크닉

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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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