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This course is a 35-hour program designed to provide a comprehensive introduction to R.
Saturday, June 10, 2017 at 10:00 AM    Cost: $2190
NYC Data Science Academy, 500 8th Ave, Ste 905
 
     
 
 
              

              
 
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LOCATION
 
DESCRIPTION
This course is a 35-hour program designed to provide a comprehensive introduction to R. Youâ€ll learn how to load, save, & transform data as well as how to write functions, generate graphs, & 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 & visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating & analyzing data of various types, creating advanced visualizations, generating reports, & 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, & the workspace


Basic R language elements
Data object types
Local data import/export
Introducing functions & 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 & timestamps
Web data capture
API data sources
Connecting to an external database

Unit 3: Manipulating Data with “dplyr”

Subset, transform, & reorder datasets
Join datasets
Groupwise operations on datasets

Unit 4: Data Graphics & Data Visualization

Core ideas of data graphics & 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 & 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.
 
 
 
 
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