Advanced analytics and practicum

 

Course Description:

This course covers advanced analytics topics intersecting the fields of data mining, machine learning and “big-data” with an emphasis on how analytics is used in various industries. The course consists of hands on project designed to impart practical analytics experience and involves a large project around analytics methodologies. Topics relate to applications of analytics in industry and case studies in analytics will be presented. Professional guest lecturers in the analytics and data science industry (from financial/insurance/entertainment/health industries) will also present. Students will be required to complete a large project (practicum) in analytics, either research or company sponsored.

Prerequisites:

Students must have completed Regression Analysis and at least 2 of the other 3 core courses.

Course Schedule:

Week 1:

  •  Course Introduction: Syllabus and Policies
  •  Overview and Introduction to Data Analytics

Weeks 2-3:

  •  Overview of Core Statistics, Statistical Analytics, Design of Experiments
  •  Project Proposal discussion, possible topics from industry

Week 4:

  •  Advanced topics: Bioinformatics & Health Informatics

Week 5:

  •  Advanced Topics: Image Analysis and application to analytics

Week 6:

 Social Media Analytics

 Recommendation Systems

  • Project Proposal – initial write up

Week 7:

  • Guest Lecture: Applied Analytics in Industry (Insurance/Entertainment/Finance/Health)

Weeks 8-9:

  •  Textual Data mining and Higher Order Learning and applications

Week 10:

 Guest Lecture: Applied Analytics in Industry (Insurance/Entertainment/Finance/Health)

  •  Project Proposal - mid-point updates

Week 11:

  •  Guest Lecture: Applied Analytics in Industry (Insurance/Entertainment/Finance/Health)
  •  Project – short updates

Weeks 12-14:

  • Project completion - Implementation demo, Final Report due

Week 15:

  • Project Presentations