As data science has increased in popularity over the last few years, the R statistical programming language has been right there with it as the go to data analysis tool for statisticians and data scientists. It has similar functions and capabilities as enterprise statistical analysis tools such as SAS, SPSS, and Stata with the additional advantages of being open source (free), having a large ecosystem of user-contributed tools, and having the flexibility of a programming language.
This workshop is intended for those who have a firm grasp of the basics of R and are looking to take their skills to the next level. You will learn how to use R to conduct statistical analysis, report your findings, visualize data with more advanced charting packages, and either perform statistical/predictive modeling or recognize patterns via clustering.
The price per attendee for this workshop is $150.
What to Bring:
- Your laptop with R installed (you can download it here)
- RStudio to make R a bit easier to use
Review of the Basics:
- Starting R and RStudio
- Loading data into R
- Overview of data types
- Data munging (subsetting, aggregation, transformation)
Statistics in R:
- Hypothesis tests
- Linear regression (fitting the model, goodness of fit, etc.)
- Reporting results (knitr, pander)
Visualization in R:
- ggplot2 and it's many functions
- rCharts and it's functionalities
- googleVis and it's functionalities
- Advanced modeling - logistic regression, predictive models, etc.
- Finding patterns - clustering, multidimensional scaling, etc.
Abhijit Dasgupta is a biostatistician, data scientist, and consultant for the NIH, local startups, and other clients. He has a PhD in biostatistics from the University of Washington, and has published work in JASA, Genetic Epidemiology, and a number of other journals. He has over 15 years experience working with R and its predecessor, S+. He occasionally blogs about statistics and data science at http://statbandit.wordpress.com.
For those that are driving to the workshop, make sure to give yourselves some extra time to find parking in the area. We recommend using http://www.parkme.com to see what parking options are available and at what rates. They have mobile apps as well.
For Apple: https://itunes.apple.com/us/app/parkme-parking-find-cheapest/id417605484
For Android: https://play.google.com/store/apps/details?id=com.parkme.consumer