Applied Analytics with R
This is the third course in the R series and will build on the material from Intermediate R. Attendance at the introductory or intermediate course is not required for those with significant practical experience in a professional setting but it is strongly encouraged that attendees have significant experience with the topics covered in those courses. This course will cover the application of several descriptive, predictive and prescriptive analytic techniques. The emphasis will be on the general purpose of these techniques along with integration and application in R rather than on the theoretical nature. This allows this course to be more accessible to a wider audience looking to inject R for analytic purposes across organizational processes. There will be an emphasis on using hands on exercises and real world datasets.
Upon successfully completing this course, students will be able to use R to:
- Preprocess their data prior to modeling to improve model performance
- Apply a variety of descriptive, predictive, and prescriptive analytic models
- Extract and interpret model results
- Visualize their modeling results to communicate their findings
Although comprehension and progression varies by class, the following provides an illustration of the variety of techniques that are commonly covered.
- Descriptive visualization
- Numerical data descriptive statistics
- Categorical data descriptive statistics
- Assessing basic assumptions
- t tests/ANOVA
- Time series forecasting
- Linear regression
- Multilevel modeling
- Logistic regression
- Decision trees/random forests
- Support vector machines
- K-means clustering
- Neural networks
- Linear programming
- Data envelopment analysis
- Decision modeling
Course fee: $795, includes breakfasts, lunches, refreshments and free parking for both days.
U-Square. Room 359
225 Calhoun Street
Cincinnati, OH 45219 (Google Maps link)
Hello! I'm Brad Boehmke, a computational economist focused on applying advanced evidence-based analytics to provide decision makers robust understanding of economic behavior, performance, and potential policy impacts across an organization.
I work on empirical econometric research, write data analysis software, teach people about business analytics and R programming, write books, and create digital projects.
I do my work as an Assistant Professor of Logistics and Supply Chain Management at the Air Force Institute of Technology Department of Operational Sciences and as an Adjunct Assistant Professor of Business Analytics at the University of Cincinnati Lindner College of Business. Previously, I was a Senior Operations Research Analyst with the Air Force and spent many years developing life cycle forecasting, risk and decision analysis models.