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
제공자:
Predictive Analytics and Data Mining
일리노이대학교 어버너-섐페인캠퍼스
About this Course
최근 조회 14,040회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 18시간 필요
권장: 4-6 hours/week
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 8시간 필요
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.
완료하는 데 8시간 필요
13개 동영상 (총 98분), 10 readings, 4 quizzes
13개의 동영상
Meet Professor Sridhar Seshadri1m
Rattle Installation Guidelines for Windows6m
Rattle Installation Guideline for MacOS11m
Rattle Interface for Windows9m
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
10개의 읽기 자료
Syllabus30m
About the Discussion Forums10m
Glossary10m
Brand Descriptions10m
Update Your Profile10m
Module 0 Agenda10m
Rattle Tutorials (Interface, Windows, Mac)30m
Module 1 Overview20m
Module 1 Readings, Data Sets, and Slides1시 30분
Module 1 Peer Review Assignment Answer Key10m
3개 연습문제
Orientation Quiz10m
Module 1 Practice Problems10m
Module 1 Graded Quiz30m
완료하는 데 5시간 필요
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.
완료하는 데 5시간 필요
7개 동영상 (총 65분), 3 readings, 3 quizzes
7개의 동영상
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
3개의 읽기 자료
Module 2 Overview20m
Module 2 Readings, Data Sets, and Slides30m
Module 2 Peer Review Assignment Answer Key10m
2개 연습문제
Module 2 Practice Problems
Module 2 Graded Quiz30m
완료하는 데 5시간 필요
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.
완료하는 데 5시간 필요
8개 동영상 (총 65분), 3 readings, 3 quizzes
8개의 동영상
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
3개의 읽기 자료
Module 3 Overview20m
Module 3 Readings, Data Sets, and Slides30m
Module 3 Peer Review Assignment Answer Key10m
2개 연습문제
Module 3 Practice Problems10m
Module 3 Graded Quiz30m
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
8개 동영상 (총 68분), 3 readings, 3 quizzes
8개의 동영상
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
3개의 읽기 자료
Module 4 Overview20m
Module 4 Readings, Data Sets, and Slides1h
Module 4 Peer Review Assignment Answer Key10m
2개 연습문제
Module 4 Practice Problems10m
Module 4 Graded Quiz30m
검토
4.2
PREDICTIVE ANALYTICS AND DATA MINING의 최상위 리뷰
SK 제공Aug 3, 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.
VC 제공Aug 6, 2019
Professor Seshadri is a master of the Data Analytics art! Great teacher. Learned a lot and loved the course.
BH 제공Nov 28, 2019
I have learned a lot by this course and Instructor, Thanks Coursera
JK 제공Aug 5, 2019
this class was intense but i have learned a lot, glad taking it
석사 학위 취득 시작
This 강좌 is part of the 100% online Master of Science in Accountancy (iMSA) from 일리노이대학교 어버너-섐페인캠퍼스.
If you are admitted to the full program, your courses count towards your degree learning.
일리노이대학교 어버너-섐페인캠퍼스 정보
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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