Advanced R for Data Science
Register for Upcoming Classes:
An Intensive 2 Day Workshop on Advanced R for Data Science
The Advanced R for Data Science is a 2-day intensive R training program designed to provide attendees with a comprehensive toolkit for applying R to business intelligence data solutions. The workshop is meant to offer a hands-on introduction to R and a set of data analysis methods for business analysts and other professionals that already possess an adequate knowledge of computer programming experience and in particular R programming experience. Attendees will benefit from exposure to a number of statistical learning and machine learning methods for data analysis. In particular, the workshop will cover statistical learning methodology, regression, and classification. The FedEx Institute is organizing this workshop to meet a critical need among corporations and nonprofits for a deep quantitative analysis toolkit designed to meet the rising big data challenges faced by all organizations.
Course Requirements: Attendees are required to have previous R programming experience. Familiarity with another programming language or platform (Python, C, MATLAB, etc.) language is an asset. The workshop is meant for professionals with previous programming experience.
Important: Attendees must bring their own device with the required R environment (correct version) and associated packages pre-installed.Click here for installation guidelines.
Course Rubric
DAY 1
08:30 – Introduction to Statistical Learning and Machine Learning, Quick Example,
Estimating functions from data, supervised vs. unsupervised learning, Model Assessment
10:00 – Break
10:15 – Introduction to Simple Linear Regression - multiple linear regression, interaction
factors, linear model selection and regularization
11:45 – Lunch
1:15 – Classification: Logistic Regression
2:45 – Break
3:00 – Classification: Linear Discriminant Analysis
4:30 – Break
DAY 2
08:30 – Classification: Quadratic Discriminant Analysis
10:00 – Break
10:15 – Classification: Comparing Classification Methods
11:45 – Lunch
1:15 – Classification: Decision Trees
2:45 – Break
3:00 – Classification: Naïve Bayes
DAY 3
08:30 – Classification: Support Vector Machines
10:00 – Break 10:15 – Unsupervised Learning: K-Means
11:45 – Lunch
1:15 – Multiple linear regression, interaction factors, linear model selection and
regularization
2:45 – Break
3:00 – Workshop Assessment
4:30 – Break
Travel Information