About this Course

51,949 recent views
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English

Skills you will gain

Predictive AnalyticsDecision-Making SoftwareGeodemographic SegmentationValidated Learning
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English

Offered by

Placeholder

University of Illinois at Urbana-Champaign

Start working towards your Master's degree

This course is part of the 100% online Master of Business Administration (iMBA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1

Week 1

9 hours to complete

Module 0: Get Ready & Module 1: Drowning in Data, Starving for Knowledge

9 hours to complete
13 videos (Total 104 min), 11 readings, 4 quizzes
13 videos
Meet Professor Sridhar Seshadri1m
Rattle Installation Guidelines for Windows11m
R and Rattle Installation Instructions for Mac OS14m
Overview of Rattle7m
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
11 readings
Syllabus30m
About the Discussion Forums10m
Glossary10m
Brand Descriptions10m
Update Your Profile10m
Module 0 Agenda5m
Rattle Tutorials (Interface, Windows, Mac)30m
Frequent Asked Questions10m
Module 1 Overview20m
Module 1 Readings, Data Sets, and Slides1h 30m
Module 1 Peer Review Assignment Answer Key10m
3 practice exercises
Orientation Quiz30m
Module 1 Practice Problems10m
Module 1 Graded Quiz30m
Week
2

Week 2

5 hours to complete

Module 2: Decision Trees

5 hours to complete
7 videos (Total 65 min), 3 readings, 3 quizzes
7 videos
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
3 readings
Module 2 Overview20m
Module 2 Readings, Data Sets, and Slides30m
Module 2 Peer Review Assignment Answer Key10m
2 practice exercises
Module 2 Practice Problems30m
Module 2 Graded Quiz30m
Week
3

Week 3

5 hours to complete

Module 3: Rules, Rules, and More Rules

5 hours to complete
8 videos (Total 65 min), 3 readings, 3 quizzes
8 videos
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
3 readings
Module 3 Overview20m
Module 3 Readings, Data Sets, and Slides30m
Module 3 Peer Review Assignment Answer Key10m
2 practice exercises
Module 3 Practice Problems10m
Module 3 Graded Quiz30m
Week
4

Week 4

5 hours to complete

Module 4: Model Performance and Recommendation Systems

5 hours to complete
8 videos (Total 68 min), 3 readings, 3 quizzes
8 videos
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
3 readings
Module 4 Overview20m
Module 4 Readings, Data Sets, and Slides1h
Module 4 Peer Review Assignment Answer Key10m
2 practice exercises
Module 4 Practice Problems10m
Module 4 Graded Quiz30m

Reviews

TOP REVIEWS FROM PREDICTIVE ANALYTICS AND DATA MINING

View all reviews

Frequently Asked Questions

More questions? Visit the Learner Help Center.