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
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中級
完成時間大約為18 小時
建議：4-6 hours/week
英語（English）
字幕：英語（English）
教學大綱 - 您將從這門課程中學到什麼
完成時間為 8 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 8 小時
13 個視頻 （總計 98 分鐘）, 10 個閱讀材料, 4 個測驗
13 個視頻
Meet Professor Sridhar Seshadri1分鐘
Rattle Installation Guidelines for Windows6分鐘
Rattle Installation Guideline for MacOS11分鐘
Rattle Interface for Windows9分鐘
Lecture 1-1: Introduction to Clustering11分鐘
Lecture 1-2: Applications of Clustering7分鐘
Lecture 1-3: How to Cluster10分鐘
Lecture 1-4: Introduction to K Means8分鐘
Lecture 1-5: Hierarchical (Agglomerative) Clustering8分鐘
Lecture 1-6: Measuring Similarity Between Clusters10分鐘
Lecture 1-7: Real World Clustering Example6分鐘
Lecture 1-8: Clustering Practice and Summary3分鐘
10 個閱讀材料
Syllabus30分鐘
About the Discussion Forums10分鐘
Glossary10分鐘
Brand Descriptions10分鐘
Update Your Profile10分鐘
Module 0 Agenda10分鐘
Rattle Tutorials (Interface, Windows, Mac)30分鐘
Module 1 Overview20分鐘
Module 1 Readings, Data Sets, and Slides1 小時 30 分
Module 1 Peer Review Assignment Answer Key10分鐘
3 個練習
Orientation Quiz10分鐘
Module 1 Practice Problems10分鐘
Module 1 Graded Quiz30分鐘
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
7 個視頻
Lecture 2-2: Model Complexity7分鐘
Lecture 2-3: Rule Based Classifiers9分鐘
Lecture 2-4: Entropy and Decision Trees14分鐘
Lecture 2-5: Classification Tree Example7分鐘
Lecture 2-6: Regression Tree Example8分鐘
Lecture 2-7: Introduction to Forests and Spam Filter Exercise9分鐘
3 個閱讀材料
Module 2 Overview20分鐘
Module 2 Readings, Data Sets, and Slides30分鐘
Module 2 Peer Review Assignment Answer Key10分鐘
2 個練習
Module 2 Practice Problems
Module 2 Graded Quiz30分鐘
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
8 個視頻
Lecture 3-2: K-Nearest Neighbor9分鐘
Lecture 3-3: K-Nearest Neighbor Classifier3分鐘
Lecture 3-4: Selecting the Best K in Rstudio12分鐘
Lecture 3-5: Bayes' Rule7分鐘
Lecture 3-6: The Naïve Bayes Trick13分鐘
Lecture 3-7: Employee Attrition Example5分鐘
Lecture 3-8: Employee Attrition Example in Rstudio, Exercise, and Summary9分鐘
3 個閱讀材料
Module 3 Overview20分鐘
Module 3 Readings, Data Sets, and Slides30分鐘
Module 3 Peer Review Assignment Answer Key10分鐘
2 個練習
Module 3 Practice Problems10分鐘
Module 3 Graded Quiz30分鐘
完成時間為 4 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 4 小時
8 個視頻 （總計 68 分鐘）, 3 個閱讀材料, 3 個測驗
8 個視頻
Lecture 4-2: Classification Tree Example11分鐘
Lecture 4-3: True and False Negatives8分鐘
Lecture 4-4: Clock Example Exercise2分鐘
Lecture 4-5: Making Recommendations13分鐘
Lecture 4-6: Association Rule Mining6分鐘
Lecture 4-7: Collaborative Filtering7分鐘
Lecture 4-8: Recommendation Example in Rstudio and Summary12分鐘
3 個閱讀材料
Module 4 Overview20分鐘
Module 4 Readings, Data Sets, and Slides1小時
Module 4 Peer Review Assignment Answer Key10分鐘
2 個練習
Module 4 Practice Problems10分鐘
Module 4 Graded Quiz30分鐘
4.2
12 條評論
來自Predictive Analytics and Data Mining的熱門評論
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.
Professor Seshadri is a master of the Data Analytics art! Great teacher. Learned a lot and loved the course.
講師
Sridhar Seshadri
Professor of Business AdministrationBusiness Administration
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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。
關於 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.
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