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.
Predictive Analytics and Data Mining
伊利诺伊大学香槟分校課程信息
27,526 次近期查看
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完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
中級
完成時間大約為24 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
Predictive AnalyticsDecision-Making SoftwareGeodemographic SegmentationValidated Learning
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
中級
完成時間大約為24 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
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.
立即開始攻讀碩士學位
此 課程 隸屬於 伊利诺伊大学香槟分校 提供的 100% 在線 Master of Science in Accountancy (iMSA)。
如果您被錄取參加全部課程，您的課程將計入您的學位學習進程。
教學大綱 - 您將從這門課程中學到什麼
完成時間為 9 小時
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.
完成時間為 9 小時
13 個視頻 （總計 104 分鐘）, 11 個閱讀材料, 4 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
7 個視頻 （總計 65 分鐘）, 3 個閱讀材料, 3 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
8 個視頻 （總計 65 分鐘）, 3 個閱讀材料, 3 個測驗
完成時間為 5 小時
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.
完成時間為 5 小時
8 個視頻 （總計 68 分鐘）, 3 個閱讀材料, 3 個測驗
審閱
來自PREDICTIVE ANALYTICS AND DATA MINING的熱門評論
由 P 提供2020年10月7日
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.
由 SK 提供2019年8月3日
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.
由 VC 提供2019年8月6日
Professor Seshadri is a master of the Data Analytics art! Great teacher. Learned a lot and loved the course.
由 BH 提供2019年11月28日
I have learned a lot by this course and Instructor, Thanks Coursera
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