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
ofrecido por
Universidad de Illinois en Urbana-Champaign
Programa - Qué aprenderás en este curso
Semana
19 horas para completar
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. It introduces to different modeling paradigms and invites the student to match problems to modeling paradigms. The module concludes with an overview of Rattle an interface for R and its use for univariate analysis.
14 videos (Total 86 minutos), 10 readings, 4 quizzes
14 videos
Rattle Installation Guidelines for Windows6m
Rattle Installation Guideline for MacOS11m
Rattle Interface for Windows9m
Lecture 1-1: Introduction to Analytics and Evolution of Statistical Inference3m
Lecture 1-2: From Data to Decisions9m
Lecture 1-3: The Evolution of Intelligent Machines3m
Lecture 1-4: Common Paradigms6m
Lecture 1-5-1: Examples of Paradigms – Part 14m
Lecture 1-5-2: Examples of Paradigms – Part 26m
Lecture 1-6: Introduction to Rattle4m
Lecture 1-7: Importing Datasets in Rattle8m
Lecture 1-8: Plotting Data and Creating Graphs in Rattle7m
Lecture 1-9: Rattle Practice and Summary3m
10 lecturas
Syllabus30m
About the Discussion Forums10m
Glossary30m
Brand Descriptions30m
Update Your Profile10m
Module 0 Agenda5m
Rattle Tutorials (Interface, Windows, Mac)30m
Module 1 Overview20m
Module 1 Readings, Data Sets, and Slides1h
Module 1 Peer Review Assignment Answer Key20m
3 ejercicios de práctica
Orientation Quiz10m
Module 1 Practice Problems40m
Module 1 Graded Quiz30m
Semana
26 horas para completar
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 in the population.
8 videos (Total 63 minutos), 3 readings, 3 quizzes
8 videos
Lecture 2-2: Developing and Estimating a Model3m
Lecture 2-3: Univariate and Bivariate Plots8m
Lecture 2-4: Bivariate Correlation5m
Lecture 2-5-1: Estimating With Simple Models - Part 18m
Lecture 2-5-2: Estimating With Simple Models - Part 210m
Lecture 2-6: Improving the Model12m
Lecture 2-7: Model Improvement Practice and Summary8m
3 lecturas
Module 2 Overview20m
Module 2 Readings, Data Sets, and Slides1h
Module 2 Peer Review Assignment Answer Key20m
2 ejercicios de práctica
Module 2 Practice Problems40m
Module 2 Graded Quiz30m
Semana
36 horas para completar
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 variable are discussed.
8 videos (Total 66 minutos), 3 readings, 3 quizzes
8 videos
Lecture 3-2: Introducing Root Mean Square Error14m
Lecture 3-3: Variable Selection8m
Lecture 3-4: Variable Selection with R Scripts8m
Lecture 3-5: Introduction to Mallow's CP6m
Lecture 3-6-1: Modeling Example – Part 18m
Lecture 3-6-2: Modeling Example – Part 29m
Lecture 3-7: Example Wrap-Up and Summary6m
3 lecturas
Module 3 Overview20m
Module 3 Readings, Data Sets, and Slides1h
Module 3 Peer Review Assignment Answer Key20m
2 ejercicios de práctica
Module 3 Practice Problems20m
Module 3 Graded Quiz30m
Semana
45 horas para completar
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 high unwanted dimensions. And to avoid this trouble, data transformation and dimension reduction comes to the rescue by examining and extracting fewer dimensions while ensuring that it conveys the full information concisely.
7 videos (Total 67 minutos), 3 readings, 3 quizzes
7 videos
Lecture 4-2: Curse of Dimensionality9m
Lecture 4-3: Limitations of Scatterplots10m
Lecture 4-4: Principle Component Analysis10m
Lecture 4-5: Principle Component Analysis in Rattle9m
Lecture 4-6: Principle Component Analysis in Rattle With Regression10m
Lecture 4-7: Principle Component Analysis Exercise and Summary8m
3 lecturas
Module 4 Overview20m
Module 4 Readings, Data Sets, and Slides1h
Module 4 Peer Review Assignment Answer Key20m
2 ejercicios de práctica
Module 4 Practice Problems
Module 4 Graded Quiz30m
Instructor
Sridhar Seshadri
Professor of Business AdministrationBusiness Administration
Comienza a trabajar para obtener tu maestría
Este curso es parte del Master of Science in Accountancy (iMSA) completamente en línea de Universidad de Illinois en Urbana-Champaign.
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Acerca de Universidad de Illinois en Urbana-Champaign
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