Fundamentals of Analytics and Discovery Informatics
The foundation understanding of machine learning was built with the support of the fundamental Analytics course, but I do wish there is more time for that critical course, so more advanced machine learning theory could be covered in that course. (May 2019)
Fundamentals of Data Analytics was one of my favorite Science elective. I loved learning the basics of machine learning, predictive analysis and dealing with big data. This has enabled me to apply for jobs as a Data Analyst in addition to Business Analyst jobs (May 2019)
I had technical skills in the supply chain industry, but needed to know the fundamentals of predictive modeling and applied computing. In particular, the Fundamentals of Analytics class (16:137:550) was a key building block in this exercise. (May 2019)
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.
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.
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
This class usually includes a panel discussion on Analytics in Industry