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推奨:6 weeks, 8-10 hours per week...

英語

字幕:英語

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Binary ClassificationData AnalysisMicrosoft ExcelLinear Regression

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約29時間で修了

推奨:6 weeks, 8-10 hours per week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
1時間で修了

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.

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2件のビデオ (合計11分), 2 readings
2件のビデオ
Introduction to Mastering Data Analysis in Excel6 分
2件の学習用教材
Specialization Overview10 分
Course Overview10 分
2時間で修了

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.

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8件のビデオ (合計52分), 1 reading, 2 quizzes
8件のビデオ
Basic Excel Vocabulary; Intro to Charting7 分
Arithmetic in Excel2 分
Functions on Individual Cells3 分
Functions on a Set of Numbers10 分
Functions on Ordered Pairs of Data8 分
Sorting Data in Excel5 分
Introduction to the Solver Plug-in8 分
1件の学習用教材
Tips for Success10 分
2の練習問題
Excel Essentials Practice30 分
Excel Essentials30 分
2
2時間で修了

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.

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6件のビデオ (合計46分), 1 reading, 2 quizzes
6件のビデオ
Bombers and Seagulls: Confusion Matrix8 分
Costs Determine Optimal Threshold4 分
Calculating Positive and Negative Predictive Values5 分
How to Calculate the Area Under the ROC Curve11 分
Binary Classification with More than One Input Variable7 分
1件の学習用教材
Tips for Success10 分
2の練習問題
Binary Classification (practice)30 分
Binary Classification (graded)45 分
3
2時間で修了

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.

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7件のビデオ (合計42分), 1 reading, 2 quizzes
7件のビデオ
Probability and Entropy7 分
Entropy of a Guessing Game7 分
Dependence and Mutual Information3 分
The Monty Hall Problem8 分
Learning from One Coin Toss, Part 15 分
Learning From One Coin Toss, Part 28 分
1件の学習用教材
Tips for Success10 分
2の練習問題
Using the Information Gain Calculator Spreadsheet (practice)30 分
Information Measures (graded)45 分
4
3時間で修了

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.

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11件のビデオ (合計73分), 1 reading, 3 quizzes
11件のビデオ
Introduction to Standardization4 分
Standard Normal Probability Distribution in Excel7 分
Calculating Probabilities from Z-scores4 分
Central Limit Theorem3 分
Algebra with Gaussians6 分
Markowitz Portfolio Optimization12 分
Standardizing x and y Coordinates for Linear Regression6 分
Standardization Simplifies Linear Regression9 分
Modeling Error in Linear Regression10 分
Information Gain from Linear Regression5 分
1件の学習用教材
Tips for Success10 分
3の練習問題
The Gaussian (practice)30 分
Regression Models and PIG (practice)45 分
Parametric Models for Regression (graded)45 分
4.2
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人気のレビュー

by 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.

by 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.

講師

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Jana Schaich Borg

Assistant Research Professor
Social Science Research Institute
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Daniel Egger

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

デューク大学(Duke University)について

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....

Excel` から MySQL: ビジネスのための分析テクニックの専門講座について

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` から MySQL: ビジネスのための分析テクニック

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