À propos de ce cours

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

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Approx. 29 heures pour terminer

Recommandé : 6 weeks, 8-10 hours per week...


Sous-titres : Anglais

Compétences que vous acquerrez

Binary ClassificationData AnalysisMicrosoft ExcelLinear Regression

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Approx. 29 heures pour terminer

Recommandé : 6 weeks, 8-10 hours per week...


Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

1 heures pour terminer

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 vidéos (Total 11 min), 2 lectures
2 vidéos
Introduction to Mastering Data Analysis in Excel6 min
2 lectures
Specialization Overview10 min
Course Overview10 min
2 heures pour terminer

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 vidéos (Total 52 min), 1 lecture, 2 quiz
8 vidéos
Basic Excel Vocabulary; Intro to Charting7 min
Arithmetic in Excel2 min
Functions on Individual Cells3 min
Functions on a Set of Numbers10 min
Functions on Ordered Pairs of Data8 min
Sorting Data in Excel5 min
Introduction to the Solver Plug-in8 min
1 lectures
Tips for Success10 min
2 exercices pour s'entraîner
Excel Essentials Practice30 min
Excel Essentials30 min
2 heures pour terminer

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 vidéos (Total 46 min), 1 lecture, 2 quiz
6 vidéos
Bombers and Seagulls: Confusion Matrix8 min
Costs Determine Optimal Threshold4 min
Calculating Positive and Negative Predictive Values5 min
How to Calculate the Area Under the ROC Curve11 min
Binary Classification with More than One Input Variable7 min
1 lectures
Tips for Success10 min
2 exercices pour s'entraîner
Binary Classification (practice)30 min
Binary Classification (graded)45 min
2 heures pour terminer

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 vidéos (Total 42 min), 1 lecture, 2 quiz
7 vidéos
Probability and Entropy7 min
Entropy of a Guessing Game7 min
Dependence and Mutual Information3 min
The Monty Hall Problem8 min
Learning from One Coin Toss, Part 15 min
Learning From One Coin Toss, Part 28 min
1 lectures
Tips for Success10 min
2 exercices pour s'entraîner
Using the Information Gain Calculator Spreadsheet (practice)30 min
Information Measures (graded)45 min
3 heures pour terminer

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 vidéos (Total 73 min), 1 lecture, 3 quiz
11 vidéos
Introduction to Standardization4 min
Standard Normal Probability Distribution in Excel7 min
Calculating Probabilities from Z-scores4 min
Central Limit Theorem3 min
Algebra with Gaussians6 min
Markowitz Portfolio Optimization12 min
Standardizing x and y Coordinates for Linear Regression6 min
Standardization Simplifies Linear Regression9 min
Modeling Error in Linear Regression10 min
Information Gain from Linear Regression5 min
1 lectures
Tips for Success10 min
3 exercices pour s'entraîner
The Gaussian (practice)30 min
Regression Models and PIG (practice)45 min
Parametric Models for Regression (graded)45 min
1 heures pour terminer

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 vidéos (Total 37 min), 1 lecture, 1 quiz
4 vidéos
Some Important and Frequently Encountered PDFs7 min
Linear Regression with More than One Input Variable4 min
Understanding Why Over-fitting Happens14 min
1 lectures
AUC Calculator Explanation and Spreadsheet10 min
1 exercices pour s'entraîner
Probability, AUC, and Excel Linest Function20 min
10 heures pour terminer

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 vidéos (Total 14 min), 3 lectures, 5 quiz
2 vidéos
Final Project Information: Part 210 min
3 lectures
Final Project Information20 min
Summary of Learning Points for Final Project: Quiz 110 min
Summary of Learning Points for Final Project: Quiz 210 min
4 exercices pour s'entraîner
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
634 avisChevron Right


a commencé une nouvelle carrière après avoir terminé ces cours


a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par 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.

par 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

À propos de Université Duke

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....

À propos de la Spécialisation De Excel à MySQL: techniques d'analyse pour l'entreprise

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....
De Excel à MySQL: techniques d'analyse pour l'entreprise

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

  • 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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