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.
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Predictive Analytics and Data Mining
Université de l'Illinois à Urbana-ChampaignÀ propos de ce cours
Compétences que vous acquerrez
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Université de l'Illinois à 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.
Commencez à travailler pour obtenir votre master
Programme du cours : ce que vous apprendrez dans ce cours
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.
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.
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.
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.
Avis
Meilleurs avis pour PREDICTIVE ANALYTICS AND DATA MINING
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.
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.
not bad, but I think if you could provide us much more materials about the R scripts about the useful program packages.
Professor Seshadri is a master of the Data Analytics art! Great teacher. Learned a lot and loved the course.
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