MIS 432 Master Syllabus

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MIS 432: Advanced Data Mining Master Syllabus

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Course Description

This course covers business analytics using advanced data mining methods for developing predictive models. It includes feature selection to identify dimensions for constructing decision making models. More advanced techniques such as decision trees, neural networks, and other classification and prediction methods will be covered. Emphasis on applications will include hands-on experience using commercial data mining software and real business data. A third attempt will require academic advisor approval.Offered by Information Sys and Ops Mngmnt. Limited to two attempts. 

Course Objectives

Upon completion of this course, students will be able to 

  • use advanced supervised learning techniques such as neural networks 
  • use multiple models to obtain better predictive performance 
  • use regression-based forecasting and smoothing-based methods 
  • use advanced unsupervised learning techniques such as clustering and association rules. 
  • use advanced topics in regression such as the lasso to yield simpler and more interpretable models.
  • apply the analysis techniques to business problems using a commercial enterprise data mining software 

MIS (Management Information Systems) Learning Goals

  • Apply knowledge of information technology, operations, and business functions to assess, design and improve business processes. 
  • Effectively manage projects, including information technology projects. 
  • Understand the overall systems development life cycle and be able to recommend IT solutions. 

BS Business Learning Goals

  • The social, global, ethical, and legal contexts of business and will be able to reflect on the role of the individual in business. 
  • The ability to apply knowledge of professional skills necessary for success in business including effective business writing. 
  • Technical and analytic skills appropriate for success in business. 
  • The ability to apply knowledge of core business disciplines including accounting, finance, information systems, management, marketing, and operations management. 
  • How research in the business disciplines contributes to knowledge and how such research is conducted.

Student Responsibilities

Students are expected to attend all classes and are responsible for keeping themselves updated on any changes on the course website. In case of absence, it is the student’s responsibility to catch up with the material covered. Without advance notice and approval, no extensions or options to retake are provided if you miss any quizzes, exams, or assignments due to absence. 

Methods of Student Evaluation

Students will be evaluated based on homework quizzes, projects, exams, and discussion board participation. 

Grading

Homework Quizzes 25%
Discussion Board Participation 10%
Final Project  
  • Part I
 5%
  • Part II
 10%
Midterm Exam 25%
Final Exam 25%

Course Grading

Course Grade Course Average Course Grade Course Average
A+ 97 to 100 B- 80 to 82.99
A 93 to 96.99 C+ 77 to 79.99
A- 90 to 92.99 C 73 to 76.99
B+ 87 to 89.99 D 60 to 69.99
B 83 to 86.99 F 0 to 59.99

Topical Outline

Weeks Topic Assignments
Week 1 Intro to R and R programming basics 
  • Download the R and R Studio Software 
  • Take the Intro to R Quiz 
  • Participate in the Online Discussion 
Week 2  Time Series Data and Forecasting
  • Read Chapter 17 
  • Take the Time Series Data and Forecasting Quiz 
  • Participate in the Online Discussion 
Week 3 Forecasting and Autocorrelation 
  • Read Chapter 17 
  • Take the Forecasting and Autocorrelation Quiz 
  • Participate in the Online Discussion
Week 4 Polynomial Regression and Splines 
  • Read Chapter 7 from the ISL book 
  • Take the Polynomial Regression and Splines Quiz 
  • Participate in the Online Discussion 
Week 5 Collinearity and Interaction Terms 
  • Read Chapter 3 from the ISL book 
  • Take the Collinearity and Interaction Terms Quiz 
  • Participate in the Online Discussion 
Week 6 Association Rules 
  • Read Chapter 14 
  • Take the Association Rules Quiz 
  • Participate in the Online Discussion 
Week 7 Midterm Exam
  • Study for and take the Midterm on Monday March 8th  
  • Midterm will cover topics from Weeks 1 - 5 
Week 8 Collaborative Filtering
  • Read Chapter 14 
  • Take the Collaborative Filtering Quiz 
  • Participate in the Online Discussion
Week 9 Neural Networks
  • Read Chapter 11 
  • Take the Neural Networks Quiz 
  • Participate in the Online Discussion 
Week 10 Neural Networks Part 2
  • Read Chapter 11 
  • Take the Neural Networks Quiz Part 2 
  • Participate in the Online Discussion
Week 11 Project Part 1 Assignment 
  • Acquire a dataset that can be used for a Neural Network application 
  • Clean and preprocesses the dataset 
Week 12 K-means Clustering 
  • Read Chapter 15 
  • Take the K-means Clustering Quiz 
  • Participate in the Online Discussion 
Week 13 Hierarchical Clustering
  • Read Chapter 15 
  • Take the Hierarchical Clustering Quiz 
  • Participate in the Online Discussion 
Week 14 Project Part 2 Assignment 
  • Apply the neural network application to your clean and preprocessed dataset 
  • Turn in the Project Assignment with the interpretation and analysis 
Week 15 Final exam
  • Study for and take the Final Exam on Monday May 3rd 
  • Final exam will cover material from Weeks 6, 8-10, and 12-13 

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