2-Day Intensive Workshop: Intro to R Programming

February 16 & 17
Thursday and Friday | 9:00 am - 5:00 pm

IMPORTANT: This is a paid workshop course at NYC Data Science Academy.

ENROLL HERE (http://nycdatascience.com/courses/intro-to-data-science-with-r-two-days-intensive-2/)

Overview

The course is a 2-day intensive workshop on basic R programming. You’ll learn how to load, save, and analyze data as well as write functions, generate graphs, and run basic statistical models. You’ll acquire the theoretical frameworks underpinning data analysis as well as practical skills.

Instructor

Vivian Zhang is the founder of NYC Data Science Academy and co-founder of SupStat. She is an adjunct professor at Stony Brook University and founded the NYC Open Data Meetup, which is 4000 strong. She has many years of practical experience in data technologies and the analytics, and has expertise in multiple programming languages including R, Python, Hadoop, and Spark. Vivian was ranked in "9 Women Leading The Pack In Data Analytics" by Forbes in August 2016. She enjoys meeting people and enjoys sharing her experiences with young professionals and students.

Syllabus

Day 1 – Getting started and working with data

• An introduction to R and data analysis:

• How to download and update R

• How to find resources and help for R

• Stages of data analysis

• Best practices of data analysis

• Visualizing data:

• Visualize the distribution of variables

• Exploring and plotting relationships between variables

• Displaying very large data sets through graphs without over-plotting

• Use best practices for Exploratory Data Analysis in R

• Working with data:

• Loading different data formats into R

• Working with factors in R

• How to clean poorly formatted data

• Saving your data

• Manipulating data:

• Subset, transform, summarize, and reorder data sets

• Perform targeted, groupwise operations on data

• Join multiple data sets together

Day 2 – Programming and modeling in R

• Programming in R:

• Create an if else statement

• Write and optimize for and while loops in R

• Use best practices for programming in R

• R Functions:

• Organize a problem into a series of functions

• Write a function in R

• Apply best practices for writing functions in R

• Simulation in R:

• Generate random numbers in R

• Visualize uncertainty with bootstrapping in R

• Construct a confidence interval with bootstrapping in R

• Test a hypothesis with a permutation test in R

• Modeling in R:

• Write a formula in R

• Fit a model to data in R

• Compare models

• Explore data sets with models