4.4
stars
126 ratings

Sridhar Seshadri

10,251 already enrolled

Offered By

Predictive Analytics and Data Mining

University of Illinois at Urbana-Champaign

About this Course

13,417 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 24 hours to complete

English

Subtitles: French, Portuguese (European), Russian, English, Spanish

Skills you will gain

Predictive Analytics
Decision-Making Software
Geodemographic Segmentation
Validated Learning

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 24 hours to complete

English

Subtitles: French, Portuguese (European), Russian, English, Spanish

Instructor

Instructor rating

4.22/5 (30 Ratings)

Sridhar Seshadri

Professor of Business Administration

Business Administration

15,366 Learners

2 Courses

Offered by

University of Illinois at Urbana-Champaign

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.

Syllabus - What you will learn from this course

Week

Week 1

9 hours to complete

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.

9 hours to complete

13 videos (Total 104 min), 11 readings, 4 quizzes

13 videos

Welcome to Predictive Analytics and Data Mining2m

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

Week 2

5 hours to complete

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.

5 hours to complete

7 videos (Total 65 min), 3 readings, 3 quizzes

7 videos

Lecture 2-1: Introduction to Discriminative Classifiers7m

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

Week 3

5 hours to complete

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.

5 hours to complete

8 videos (Total 65 min), 3 readings, 3 quizzes

8 videos

Lecture 3-1: Introduction to Rules4m

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

Week 4

5 hours to complete

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.

5 hours to complete

8 videos (Total 68 min), 3 readings, 3 quizzes

8 videos

Lecture 4-1: Introduction to Model Performance6m

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

4.4

30 reviews

5 stars
61.90%
4 stars
25.39%
3 stars
7.14%
2 stars
0.79%
1 star
4.76%

TOP REVIEWS FROM PREDICTIVE ANALYTICS AND DATA MINING

by BHNov 29, 2019

I have learned a lot by this course and Instructor, Thanks Coursera

by SKAug 4, 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.

by BBSep 14, 2020

Good foundation course to understand Data Aanlytisc

by DSAug 18, 2021

syllabus designed is well and explained well by the trainers.

thank you.

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Predictive Analytics and Data Mining

Predictive Analytics and Data Mining

About this Course

Skills you will gain

Instructor

Sridhar Seshadri

Offered by

University of Illinois at Urbana-Champaign

Syllabus - What you will learn from this course

Week 1

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

Week 2

Module 2: Decision Trees

Week 3

Module 3: Rules, Rules, and More Rules

Week 4

Module 4: Model Performance and Recommendation Systems

Reviews

TOP REVIEWS FROM PREDICTIVE ANALYTICS AND DATA MINING

Frequently Asked Questions

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Predictive Analytics and Data Mining

Predictive Analytics and Data Mining

About this Course

Skills you will gain

Instructor

Sridhar Seshadri

Offered by

University of Illinois at Urbana-Champaign

Syllabus - What you will learn from this course

Week 1

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

Week 2

Module 2: Decision Trees

Week 3

Module 3: Rules, Rules, and More Rules

Week 4

Module 4: Model Performance and Recommendation Systems

Reviews

TOP REVIEWS FROM PREDICTIVE ANALYTICS AND DATA MINING

Frequently Asked Questions

Coursera Footer

Start or advance your career

Browse popular topics

Popular courses and articles

Earn a degree or certificate online

Coursera

Community

More