This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.
ofrecido por
Predictive Analytics and Data Mining
Universidad de Illinois en Urbana-ChampaignAcerca de este Curso
48,882 vistas recientes
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. 24 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Habilidades que obtendrás
Predictive AnalyticsDecision-Making SoftwareGeodemographic SegmentationValidated Learning
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. 24 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
9 horas para completar
Module 0: Get Ready & Module 1: Drowning in Data, Starving for Knowledge
This module will introduce you to the most common and important unsupervised learning technique – Clustering. You will have an understanding of different applications of clustering analysis after this module. You will also learn when we need clustering and why it is important. Then, you will be introduced to a variety of clustering methods.
9 horas para completar
13 videos (Total 104 minutos), 11 lecturas, 4 cuestionarios
13 videos
Meet Professor Sridhar Seshadri1m
Rattle Installation Guidelines for Windows11m
R and Rattle Installation Instructions for Mac OS14m
Overview of Rattle7m
Lecture 1-1: Introduction to Clustering11m
Lecture 1-2: Applications of Clustering7m
Lecture 1-3: How to Cluster10m
Lecture 1-4: Introduction to K Means8m
Lecture 1-5: Hierarchical (Agglomerative) Clustering8m
Lecture 1-6: Measuring Similarity Between Clusters10m
Lecture 1-7: Real World Clustering Example6m
Lecture 1-8: Clustering Practice and Summary3m
11 lecturas
Syllabus30m
About the Discussion Forums10m
Glossary10m
Brand Descriptions10m
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 30m
Module 1 Peer Review Assignment Answer Key10m
3 ejercicios de práctica
Orientation Quiz30m
Module 1 Practice Problems10m
Module 1 Graded Quiz30m
5 horas para completar
Module 2: Decision Trees
In this module, we will discuss how to use decision trees to represent knowledge. The module concludes with a presentation of the Random Forest method that overcomes some of the limitations (such as high variance or low precision) of a single decision tree constructed from data.
5 horas para completar
7 videos (Total 65 minutos), 3 lecturas, 3 cuestionarios
7 videos
Lecture 2-2: Model Complexity7m
Lecture 2-3: Rule Based Classifiers9m
Lecture 2-4: Entropy and Decision Trees14m
Lecture 2-5: Classification Tree Example7m
Lecture 2-6: Regression Tree Example8m
Lecture 2-7: Introduction to Forests and Spam Filter Exercise9m
3 lecturas
Module 2 Overview20m
Module 2 Readings, Data Sets, and Slides30m
Module 2 Peer Review Assignment Answer Key10m
2 ejercicios de práctica
Module 2 Practice Problems30m
Module 2 Graded Quiz30m
5 horas para completar
Module 3: Rules, Rules, and More Rules
This module will focus on three key topics, namely rules, nearest neighbor methods, and Bayesian methods. Over the course of this module, you will be exposed to how rules factor into the world of data and how they play a role in the analysis of data. The second and third topics focus on the classification of data.
5 horas para completar
8 videos (Total 65 minutos), 3 lecturas, 3 cuestionarios
8 videos
Lecture 3-2: K-Nearest Neighbor9m
Lecture 3-3: K-Nearest Neighbor Classifier3m
Lecture 3-4: Selecting the Best K in Rstudio12m
Lecture 3-5: Bayes' Rule7m
Lecture 3-6: The Naïve Bayes Trick13m
Lecture 3-7: Employee Attrition Example5m
Lecture 3-8: Employee Attrition Example in Rstudio, Exercise, and Summary9m
3 lecturas
Module 3 Overview20m
Module 3 Readings, Data Sets, and Slides30m
Module 3 Peer Review Assignment Answer Key10m
2 ejercicios de práctica
Module 3 Practice Problems10m
Module 3 Graded Quiz30m
5 horas para completar
Module 4: Model Performance and Recommendation Systems
In this module, you will study tools for recognizing what to recommend, and identify cross-sell or upsell opportunities. As the last module of the course, we will wrap up the content so far and you will get an opportunity to practice on your own and learn how to adapt these models to drive business impact in your own organizations.
5 horas para completar
8 videos (Total 68 minutos), 3 lecturas, 3 cuestionarios
8 videos
Lecture 4-2: Classification Tree Example11m
Lecture 4-3: True and False Negatives8m
Lecture 4-4: Clock Example Exercise2m
Lecture 4-5: Making Recommendations13m
Lecture 4-6: Association Rule Mining6m
Lecture 4-7: Collaborative Filtering7m
Lecture 4-8: Recommendation Example in Rstudio and Summary12m
3 lecturas
Module 4 Overview20m
Module 4 Readings, Data Sets, and Slides1h
Module 4 Peer Review Assignment Answer Key10m
2 ejercicios de práctica
Module 4 Practice Problems10m
Module 4 Graded Quiz30m
Reseñas
Principales reseñas sobre PREDICTIVE ANALYTICS AND DATA MINING
por PA7 de oct. de 2020
I have successfully completed the course and it has helped me to upgrade my skills. I am sure that this is going to help me to carry out my future carrier in Data Analytics efficiently.
por SK3 de ago. de 2019
Prof. Seshadri was amazing. Learned how to model and analyze data for various use cases. Excellent course for people looking for a good understanding of data modeling and data mining.
por SK4 de mar. de 2020
not bad, but I think if you could provide us much more materials about the R scripts about the useful program packages.
por VC6 de ago. de 2019
Professor Seshadri is a master of the Data Analytics art! Great teacher. Learned a lot and loved the course.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si compro el Certificado?
Is financial aid available?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
