Data Science with R (Beginner level)

Instructors: Scott Kostyshak and Charlie Redmon

Course Overview
NYC Data Science Academy is offering R Intensive Beginner: a five week course that will introduce you to the wonderful wold of R and provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon.

Project Demo Day and Certificates
From the rudimentary building blocks of programming basics, to data manipulation and use of advanced drawing packages, the course ends with a demonstration of a project of your choice on Project Demo Day. On Demo Day you will access and analyze real data, utilizing the tools and skillsets taught to you throughout the course. After the successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing, Honorable Graduation, and Active Participation.

Certificates are awarded according to your understanding, skill, and participation.

Syllabus

Basics: Explain the basic operation of knowledge through this unit of study. Students will learn the characteristics of R, resource acquisition mode, and mastery of basic programming.
Case Study and Exercises: Use the R language to complete certain Euler Project problems.

• How to learn R

• How to get help

• R language resources and books

• RStudio

• Expansion Pack

• Workspace

• Custom Startup Items

• Batch Mode

• Data Objects

• Custom Functions

• Control Statements

• Vectorized Operations

Getting Data: Explain the various ways the R language reads data, bring the participants through basic knowledge of web crawling, and connect to the database via sql statement calling data from a variety of locally read excel file data.
Case and Exercises: Crawl watercress data on the site and write a custom function.

• Web data capture

• API data source

• Connect to the database

• Local Documentation

• Other data sources

• Data Export

Data Manipulation: How to manipulate data and use R for the all kinds of data conversion, especially for string operation processing.
Case Study and Exercise: Find the QQ (the most used instant messenger tool) group, then discuss research options with text features.

• Data sorting

• Merge Data

• Summary data

• Remodeling Data

• Take a subset of data

• String manipulation

• Date Actions

Data Visualization: Cover two advanced drawing packages (Lattice and ggplot2) and understand the various methods of visualization.
Case and Exercises: Using graphics, text and other data.

• Histogram

• Point

• Column

• Line

• Pie

• Box Plot

• Scatter

• Matrix related

• Map

Note: If class finishes early, we will cover selected topics below based on your need.

1. Elementary Statistical Methods: The primary explanation to use R for statistical analysis and regression
   analysis. Students will master the basic statistical significance and role model.
   Case and Exercise: Using regression to predict commodity prices―simulated casino game winner.

• Descriptive Statistics

• Statistical Distributions

• Frequency and contingency tables

• Linear Regression

• Correlation

• T Test

• Non-parametric statistics

2. Preliminary Data Mining: Explain the R language for data mining expansion pack and functions use. Students will master two mining methods, supervised learning and unsupervised learning.
   Case and Exercise: Use R to participate in Kaggle Data Mining Competition

• General Mining Process

• Rattle bag

• Hierarchical clustering

• K -means clustering

• Decision Trees

• BP neural network