Data Science with R Programming

This training will introduce you to the basics of Data Science and R Programming. You will be introduced to the essential R skills needed to answer essential questions about differences in crime across the different states. 

The wide range of skill set covered here include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document

Learning Outcomes:

• Understand critical programming language concepts

• Make use of R loop functions and debugging tools

• Configure statistical programming software

• Collect detailed information using R profiler

Outline:

• R Studio walkthrough

• Download Resources

• Datatypes

• Data Structures

• Create group of elements in a vector

• Use repetitions and sequence to create a vector fast

• Random numbers, rounding and sampling

• Formatting numbers

• Approaches to filtering data

• Revenue impact of Ad-campaign

• Set Operations

• Checking existence

• Nested if-else

• Writing smarter For loops

• Memory pre-allocation tactics

• Working with lubridate and anytime

• Introduction to lists

• Named list, unlist and more

• Creating Dataframe

• Visual editing

• Various dataframe operations

• R datasets, packages and public datasets

• Conditional filtering and missing values

• Grouping and case problem solution

• Debugging techniques

• Error handling

• Steps involved in significance tests

• ANOVA - R demo