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
伊利诺伊大学香槟分校課程信息
您將獲得的技能
- Predictive Analytics
- Decision-Making Software
- Geodemographic Segmentation
- Validated Learning
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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.
授課大綱 - 您將從這門課程中學到什麼
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.
審閱
- 5 stars61.60%
- 4 stars25.60%
- 3 stars7.20%
- 2 stars0.80%
- 1 star4.80%
來自PREDICTIVE ANALYTICS AND DATA MINING的熱門評論
I have learned a lot by this course and Instructor, Thanks Coursera
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.
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.
syllabus designed is well and explained well by the trainers. thank you.
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