Course Overview:

The advancement in technology and changes in consumer behavior during the last two years have produced 90% of the data in the world, at 2.5 quintillion bytes of data a day. By 2030, there will be more than 50 billion smart connected devices globally, collecting, analyzing, and sharing data. Yet less than 20% of these data are effectively utilized for decision-making. While companies understand the importance of data-driven decision-making, many lack the capabilities, knowledge, and confidence needed to utilize the plethora of data available. This course introduces the science of processing data using expert systems for faster and smarter decision-making, and provides hands-on training of using R for data visualization, association analysis, and clustering.

Learning Outcomes:

Upon successfully completing this course, students will be able to:

- Understand the fundamentals of data engineering and predictive modeling

- Handle and summarize large datasets using MS Excel and R

- Apply appropriate analytical tools and algorithms for a given problem/dataset

- Derive insights from dataset

- Demonstrate knowledge of contemporary issues 

Topics:

Students in this course will have six homework assignments, two exams, and a data analytics case study using the tools and techniques learned in this course.

Team Work: We live in a team-based world. The ability to work with colleagues toward a common goal is an important skill to learn and demonstrate for career advancement. Generally, teams are made up of people with different expertise, so the team can be energized and prepared to deliver results. Therefore, in this course, both homework and case studies have to be done in teams. The recommended practice is to solve the HWs independently and then collaborate with your team member to discuss your solutions, resolve any issues, and finally compile the work to submit it as a team.

Course Schedule:

Week 1: Overview of Data Analytics

Weeks 2 - 4: Exploratory Analysis and Visualizations

Week 5: Introduction to Machine Learning and Predictive Analytics

Week 6: Predictive Analytics - simple regression

Week 7: Predictive Analytics - multiple regression

Week 8-9: Predictive Analytics - Tree-based

Week 10: Predictive Analytics - Artificial Neural Networks

Week 11: Market Basket Analysis

Week 12: Customer Segmentation using Clustering

Week 13-16: Final Exam and Case Study

Instructor:

Sharan Srinivas, Ph.D.

Department of Industrial and Systems Engineering | Department of Marketing

Length:

16-weeks

Department: 

Department of Industrial and Systems Engineering and Department of Marketing

Credit:

4.5 CEUs | 45 Professional Development Hours

Audience:

Adult Learners

Accommodations

University of Missouri Extension complies with the Americans with Disabilities Act of 1990. If you have a disability and need accommodations in connection with participation in an educational program or you need materials in an alternate format, please notify your instructor as soon as possible so that necessary arrangements can be made.

Cancellations and Refund Requests

Access MU Extension’s Course Cancellation and Refund Policy for details.