Fundamentals of Analytics and Discovery Informatics
I think that Fundamentals of Analytics is the one that helped me grow the most...I think that the hands-on project as well as the push to go a little bit outside of my comfort zone helped me develop some invaluable skills in data analysis.
This course provides an overview of modern data analytics techniques that have grown from the fields of statistics, machine learning and information theory. Decision trees, covering algorithms, association mining, statistical modeling, linear models and instance-based learning are some of the basic methods that are covered. How to engineer the input and establish the credibility of results is also considered. The course also includes select case studies of data analytics research projects underway or conducted at Rutgers University, and includes a substantial class project relevant to the data analytics field.
Course Objectives: At the conclusion of the course, students will be familiar with the basic theory and algorithms of data analytics and their application to practical problems.
PREREQUISITES: 1 intro course in statistics and 1 intro programming course. These can be undergrad or grad level.
Week 1: Intro + Course Outline, Policies, Etc.
Input: Concepts, instances, attributes
Week 2: Output: Knowledge representations
Week 3: Algorithms: Rules & statistical models
Algorithms: Decision trees
Week 4: Algorithms: Covering
Algorithms: Association mining
Week 5: Algorithms: Linear and IBL
Algorithms: IBL and Clustering
Week 6: Credibility: Evaluation of results
Week 7: Implementations: Decision Trees
Week 8: Implementations: Classification Rules
Week 9: Implementations: Instance-based
Implementations: Numeric Prediction
Week 10: Implementations: Clustering
Week 11: Text Analytics: Intro
Text Analytics: Information extraction Papers
Week 12: Text Analytics: Statistics vs. Semantics Papers
Text Analytics: Higher Order Learning™ Papers
Week 13: Text Analytics: Case Studies
Week 14: Text Analytics: Case Studies
Student project final presentations
Week 15: Student project final presentations
Review, Consultation & Study