# Data Science with R: Data Analysis and Visualization

### offered by NYC Data Science Academy

Overview

This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Prerequisites

Basic knowledge about computer components

Basic knowledge about programming

Syllabus

Unit 1: Basic Programming with R

Introduction to R

What is R?

Why R?

How to learn R

RStudio, packages, and the workspace

Basic R language elements

Data object types

Local data import/export

Introducing functions and control statements

In-depth study of data objects

Functions

Functional Programming

Unit 2: Basic Data Elements

Data transformation

Reshape

Split

Combine

Character manipulation

String manipulation

Dates and timestamps

Web data capture

API data sources

Connecting to an external database

Unit 3: Manipulating Data with “dplyr”

Subset, transform, and reorder datasets

Join datasets

Groupwise operations on datasets

Unit 4: Data Graphics and Data Visualization

Core ideas of data graphics and data visualization

R graphics engines

Base

Grid

Lattice

ggplot2

Big data graphics with ggplot2

Unit 5: Advanced Visualization

Customized graphics with ggplot2

Titles

Coordinate systems

Scales

Themes

Axis labels

Legends

Other plotting cases

Violin Plots

Pie charts

Mosaic plots

Hierarchical tree diagrams

scatter plots with multidimensional data

Time-series visualizations

Maps

R and interactive visualizations

Final Project

After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.