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.
ofrecido por
Data Modeling and Regression Analysis in Business
Universidad de Illinois en Urbana-ChampaignAcerca de este Curso
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 25 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 25 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad de Illinois en Urbana-Champaign
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.
Comienza a trabajar para obtener tu maestría
This curso is part of the 100% online Master of Science in Accountancy (iMSA) from Universidad de Illinois en Urbana-Champaign.
If you are admitted to the full program, your courses count towards your degree learning.
Programa - Qué aprenderás en este curso
8 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. 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.
8 horas para completar
14 videos (Total 93 minutos), 11 lecturas, 3 cuestionarios
14 videos
Rattle Installation Guidelines for Windows11m
R and Rattle Installation Instructions for Mac OS14m
Overview of Rattle 7m
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 27m
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
11 lecturas
Syllabus30m
About the Discussion Forums10m
Glossary30m
Brand Descriptions30m
Update Your Profile10m
Module 0 Agenda5m
Rattle Tutorials (Interface, Windows, Mac)30m
Frequent Asked Questions10m
Module 1 Overview20m
Module 1 Readings, Data Sets, and Slides1h
Module 1 Peer Review Assignment Answer Key20m
2 ejercicios de práctica
Orientation Quiz30m
Module 1 Graded Quiz30m
6 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 of variables in the population.
6 horas para completar
8 videos (Total 63 minutos), 3 lecturas, 3 cuestionarios
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
6 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 variables are discussed.
6 horas para completar
8 videos (Total 66 minutos), 3 lecturas, 4 cuestionarios
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
3 ejercicios de práctica
Module 3 Practice Problems (A)40m
Module 3 Practice Problems (B)20m
Module 3 Graded Quiz30m
5 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 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.
5 horas para completar
7 videos (Total 67 minutos), 3 lecturas, 3 cuestionarios
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
Reseñas
Principales reseñas sobre DATA MODELING AND REGRESSION ANALYSIS IN BUSINESS
por AS10 de ago. de 2019
The course was easy to understand and fun to practice at home which made it exciting and useful at the same time.
por BC21 de ago. de 2019
this course is easier to understand the statistical modeling, no need to learn the deep algorithm
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
- es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
- Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
Is financial aid available?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el vínculo de Ayuda económica que está debajo del botón 'Inscribirse' a la izquierda. Se te pedirá que completes una solicitud y recibirás una notificación cuando se apruebe. Obtén más información.
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