Data Analytics with R
Overview
R is a very popular, open-source environment for statistical computing, data analytics, and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries, and advanced data analytics and graphing with real-world data.
Please note that this is an introductory-intermediate level class. And it is NOT a machine learning class
Objective
Learn to use R for data analytics
What You Will Learn
- R language fundamentals
- R data structures (Lists, Dataframes, Matrices)
- Graphing with R
- Analyzing datasets with R
Audience
Developers/Data Analysts
Duration
3 days
Format
Lectures and Hands-on
Pre-Requisites
- Basic programming background is preferred
Setup
- A modern laptop
- Latest R studio and R environment installed
Detailed Outline
Language Basics
-
- Course Introduction
- About Data Science
- Data Science Definition
- Process of Doing Data Science.
- Introducing R Language
- Variables and Types
- Control Structures (Loops / Conditionals)
- R Scalars, Vectors, and Matrices
- Defining R Vectors
- Matrices
- String and Text Manipulation
- Character data type
- File IO
- Lists
- Functions
- Introducing Functions
- Closures
- lapply/sapply functions
- DataFrames
- Labs for all sections
Intermediate R Programming
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- DataFrames and File I/O
- Reading data from files
- Data Preparation
- Built-in Datasets
- Visualization
- Graphics Package
- plot() / barplot() / hist() / boxplot() / scatter plot
- Heat Map
- ggplot2 package ( qplot(), ggplot())
- Exploration With Dplyr
- Labs for all sections
Analytics With R
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- Statistical Modeling With R
- Statistical Functions
- Dealing With NA
- Distributions (Binomial, Poisson, Normal)
- Data Exploration
- Regressions
- Linear Regressions
- Logistic Regressions
- Text Processing (tm package / Wordclouds)
- R and Big Data
- Labs for all sections
- Statistical Modeling With R