The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior.
Data Modeling and Regression Analysis in Business
伊利诺伊大学香槟分校
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中級
完成時間大約為20 小時
建議：4-6 hours/week
英語（English）
字幕：英語（English）
教學大綱 - 您將從這門課程中學到什麼
完成時間為 8 小時
Module 0: Get Ready & Module 1: Introduction to Analytics and Evolution of Statistical Inference
This session is an overview of the business data analytics process and its components. We introduce you to different modeling paradigms and invite you to match problems to modeling paradigms. The module concludes with an overview of Rattle (an interface for the statistical package R) and its use for univariate analysis.
完成時間為 8 小時
14 個視頻 （總計 86 分鐘）, 10 個閱讀材料, 3 個測驗
14 個視頻
Rattle Installation Guidelines for Windows6分鐘
Rattle Installation Guideline for MacOS11分鐘
Rattle Interface for Windows9分鐘
Lecture 1-1: Introduction to Analytics and Evolution of Statistical Inference3分鐘
Lecture 1-2: From Data to Decisions9分鐘
Lecture 1-3: The Evolution of Intelligent Machines3分鐘
Lecture 1-4: Common Paradigms6分鐘
Lecture 1-5-1: Examples of Paradigms – Part 14分鐘
Lecture 1-5-2: Examples of Paradigms – Part 26分鐘
Lecture 1-6: Introduction to Rattle4分鐘
Lecture 1-7: Importing Datasets in Rattle8分鐘
Lecture 1-8: Plotting Data and Creating Graphs in Rattle7分鐘
Lecture 1-9: Rattle Practice and Summary3分鐘
10 個閱讀材料
Syllabus30分鐘
About the Discussion Forums10分鐘
Glossary30分鐘
Brand Descriptions30分鐘
Update Your Profile10分鐘
Module 0 Agenda5分鐘
Rattle Tutorials (Interface, Windows, Mac)30分鐘
Module 1 Overview20分鐘
Module 1 Readings, Data Sets, and Slides1小時
Module 1 Peer Review Assignment Answer Key20分鐘
2 個練習
Orientation Quiz10分鐘
Module 1 Graded Quiz30分鐘
完成時間為 6 小時
Module 2: Dating with Data
This session focuses on identifying relationships between dependent and independent variables using a regression model. The goal is to find the best fitted model to the data to learn about the underlying relationship of variables in the population.
完成時間為 6 小時
8 個視頻 （總計 63 分鐘）, 3 個閱讀材料, 3 個測驗
8 個視頻
Lecture 2-2: Developing and Estimating a Model3分鐘
Lecture 2-3: Univariate and Bivariate Plots8分鐘
Lecture 2-4: Bivariate Correlation5分鐘
Lecture 2-5-1: Estimating With Simple Models - Part 18分鐘
Lecture 2-5-2: Estimating With Simple Models - Part 210分鐘
Lecture 2-6: Improving the Model12分鐘
Lecture 2-7: Model Improvement Practice and Summary8分鐘
3 個閱讀材料
Module 2 Overview20分鐘
Module 2 Readings, Data Sets, and Slides1小時
Module 2 Peer Review Assignment Answer Key20分鐘
2 個練習
Module 2 Practice Problems40分鐘
Module 2 Graded Quiz30分鐘
完成時間為 6 小時
Module 3: Model Development and Testing with Holdout Data
This session introduces the student to use of a holdout data set for evaluating model performance. Methods of improving the model are discussed with emphasis on variable selection. Nuances of modeling discrete predictor variables and response variables are discussed.
完成時間為 6 小時
8 個視頻 （總計 66 分鐘）, 3 個閱讀材料, 4 個測驗
8 個視頻
Lecture 3-2: Introducing Root Mean Square Error14分鐘
Lecture 3-3: Variable Selection8分鐘
Lecture 3-4: Variable Selection with R Scripts8分鐘
Lecture 3-5: Introduction to Mallow's CP6分鐘
Lecture 3-6-1: Modeling Example – Part 18分鐘
Lecture 3-6-2: Modeling Example – Part 29分鐘
Lecture 3-7: Example Wrap-Up and Summary6分鐘
3 個閱讀材料
Module 3 Overview20分鐘
Module 3 Readings, Data Sets, and Slides1小時
Module 3 Peer Review Assignment Answer Key20分鐘
3 個練習
Module 3 Practice Problems (A)40分鐘
Module 3 Practice Problems (B)20分鐘
Module 3 Graded Quiz30分鐘
完成時間為 5 小時
Module 4: Curse of Dimensionality
There has been a tremendous increase in the way data generation via sensors, digital platforms, user-generated content, etc. are being used in the industry. For example, sensors continuously record data and store it for analysis at a later point. In the way data gets captured, there can be a lot of redundancy. With more variables, comes more trouble! There may be very little (or no) incremental information gained from these sources. This is the problem of a high number of unwanted dimensions. To avoid this pitfall, data transformation and dimension reduction comes to the rescue by examining and extracting fewer dimensions while ensuring that it conveys the full information concisely.
完成時間為 5 小時
7 個視頻 （總計 67 分鐘）, 3 個閱讀材料, 3 個測驗
7 個視頻
Lecture 4-2: Curse of Dimensionality9分鐘
Lecture 4-3: Limitations of Scatterplots10分鐘
Lecture 4-4: Principle Component Analysis10分鐘
Lecture 4-5: Principle Component Analysis in Rattle9分鐘
Lecture 4-6: Principle Component Analysis in Rattle With Regression10分鐘
Lecture 4-7: Principle Component Analysis Exercise and Summary8分鐘
3 個閱讀材料
Module 4 Overview20分鐘
Module 4 Readings, Data Sets, and Slides1小時
Module 4 Peer Review Assignment Answer Key20分鐘
2 個練習
Module 4 Practice Problems
Module 4 Graded Quiz30分鐘
4.3
8 條評論
來自Data Modeling and Regression Analysis in Business的熱門評論
The course was easy to understand and fun to practice at home which made it exciting and useful at the same time.
講師
Sridhar Seshadri
Professor of Business AdministrationBusiness Administration
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此 課程 隸屬於 伊利诺伊大学香槟分校 提供的 100% 在線 Master of Science in Accountancy (iMSA)。如果您被錄取參加全部課程，您的課程將計入您的學位學習進程。
關於 伊利诺伊大学香槟分校
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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