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RStudio Public Workshop - Boston Area

RStudio

Monday, January 27, 2014 at 9:00 AM - Tuesday, January 28, 2014 at 5:00 PM (EST)

Cambridge, MA

RStudio Public Workshop - Boston Area

Ticket Information

Ticket Type Sales End Price Fee Quantity
RStudio Workshop - 2 Days: Introduction to data science with R Ended $1,200.00 $0.00

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Event Details

January 27-28, 2014 - Introduction to Data Science with R

RStudio is hosting our two day Introduction to Data Science with R course in Boston this January. This is a two-day workshop, designed to provide a comprehensive introduction to R. We'll get you analyzing and modeling data with R in no time. All participants will receive a copy of all slides, exercises, data sets, and R scripts used in the course.

Course Instructor: Garrett Grolemund - RStudio Master Instructor

Discount pricing is available for academics (33% off) and students (66% off). Space is limited, please contact us to confirm your eligibility.

Who should take this course?

This class will be a good fit for you if you are just starting with R or have dabbled in R, but wish to improve your skills. No prior experience with R or data science is required. A basic familiarity with linear models will be helpful, but is not necessary.

What will you learn?

Practical skills for visualizing, transforming, and modeling data in R. During this two-day course, you will learn how to explore and understand data as well as how to build linear and non-linear models in R. A full list of topics for each day is below.

What should you bring?

Be ready to learn. You need your laptop and the latest version of R. We also recommend downloading the RStudio IDE, as it provides a great learning environment for beginners as well as tools for when you transition into an advanced user.

Overview

Our two day course teaches you how to analyze data with R. The course is designed for non-programmers as well as data analysts who are switching to R from other software, such as SAS or Excel. The course has been tested by over 200 students, and has been honed to provide a clear and painless introduction to R.

Topics cover the three skill sets of data analysis: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. You will learn R’s syntax and grammar as well as how to load, save, and transform data, generate beautiful graphs, and fit statistical models to your data. We’ll give you a theoretical framework to help you understand the process of data analysis, but our focus is on practical tools that you can use as soon as you get back from the course.

All techniques are motivated by real problems, and you’ll be exposed to a number of real datasets throughout the course. We alternate brief lectures with hands-on practice: you’ll get plenty of experience actually using R (not just hearing about it!), and there’s plenty of help available if you get stuck.


Day 1 - Getting started and working with data

Monday, Jan 27, 2014

An Introduction to R - R does more than most statistical software packages. R is a programming language in its own right, an environment for interactive data analysis, and a community of passionate users. This orientation to the R language will get you up and running with R and RStudio.

  • How to find resources and help for R
  • How to use the R interface and workflow
  • How to store and work with data objects in R

R Syntax - Learning to speak R begins with R’s syntax. R has a special notation system that allows you to easily extract, use, or manipulate information inside data objects. In this module you’ll use R’s syntax to clean data and automate tasks that would be nearly impossible to do by hand.

  • Learn R’s notation language
  • Perform targeted searches within your data
  • Use subsetting and missing values to clean data

Visualizing Data - R’s is well known for its beautiful graphics. R packages, like ggplot2, provide an expressive and logical language for building clear and effective data visualizations.

  • Visualize the distribution of a variable
  • Explore and plot relationships between variables
  • Plot very large data sets without overplotting
  • Display multivariate relationships in 2d graphs

Customizing Graphs - R gives you complete control over the appearance of your graphics. You can customize them for publication, to highlight important findings, or to enhance your corporate branding.

  • Add titles, legends, and guides to your plots
  • Control labels and coordinate systems in your plots
  • Customize the color schemes in your plots

Day 2 - Manipulating and modeling data in R

Tuesday, Jan 28, 2014

Loading and Cleaning Data - Data comes in many formats, but R prefers just one. You can save yourself hours of time, and build good habits, by shaping your data sets into the optimum layout for R.

  • Loading different data formats into R
  • Working with factors in R
  • How to clean poorly formatted data
  • Saving your data

Manipulating Data - R’s methods for data manipulation make it easy and fast to extract information from data sets and to prepare raw data for analysis. In this module, you’ll learn how to

  • Subset, transform, summarize, and reorder data sets
  • Perform targeted, groupwise operations on data
  • Join multiple data sets together

Linear Models - Knowing what variables you should include in a model, and what you can infer from the results, are two of the most tricky skills in modeling. They are also two of the most useful. This module will teach you the statistical tools and R tools that can help.

  • Interpret model coefficients with t-tests and anova tests
  • Calculate model statistics such as R2, Cp, AIC and BIC
  • Perform variable selection with stepwise regression and LASSO regression
  • Generalize linear modeling to non-linear relationships with polynomials and splines
  • Explore generalized additive models and logistic regression

Modeling Complex Relationships - There are more modeling methods that target more types of data than we could cover in a single day. This module gets your hands dirty with the most generally useful algorithms and ends with an informative survey of all the rest.

  • Generalize linear modeling to non-linear relationships with polynomials and splines
  • Explore generalized additive models and logistic regression
  • Learn the general framework that all modeling methods belong to

Disclaimer:

In certain cases, we may need to cancel this workshop due to circumstances beyond our control or otherwise. If this happens, RStudio will refund all registration fees for those who signed up. RStudio is not responsible for any related expenses incurred by registered attendees (including but not limited to travel and hotel expenses).

Refund policy:

Until Jan 12, 2014 - Full refund
Jan 13, 2014 to Jan 19, 2014 - 50% refund of registration fees
Jan 20, 2014 and after - No refund available

Money-back guarantee:

All public workshops hosted by RStudio come with a no-questions-asked money-back guarantee.


Special thanks to our host Microsoft New England (MSNE).

MSFT Logo

Microsoft New England (MSNE) is a major center for technical innovation and research. Located in the heart of Cambridge, Massachusetts, the MSNE campus includes two buildings—One Memorial Drive and the recently renovated One Cambridge Center. At the core of MSNE is the Microsoft New England Research & Development Center (NERD). NERD is a world-renowned research and software development center, and is home to teams working on critical products and services like Microsoft Office 365 and Microsoft SQL. NERD also serves as a center of gravity for the local tech community, having hosted more than 1,000 events and welcomed more than 100,000 visitors since opening in 2008.

Have questions about RStudio Public Workshop - Boston Area? Contact RStudio

When & Where



Microsoft New England Research & Development Center
1 Memorial Drive
Cambridge, MA 02142

Monday, January 27, 2014 at 9:00 AM - Tuesday, January 28, 2014 at 5:00 PM (EST)


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Organizer

RStudio

RStudio is a company dedicated to providing software, education, and services for the R statistical computing environment. We started RStudio because we were excited and inspired by R. To learn more about us, visit our website.

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