Data Science by R programming(Beginner level, Five Sundays) R002

Announcement: We are changing the date from five Saturdays to five Sundays. Sorry for the inconvenience!

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Date: Feb 2nd, Feb 9th, Feb 23th, Mar 2nd of 2014(Five Sundays)

Time: 10:00pm to 5:00 pm (4.5 hours teaching, 1.5 hours hands-on, 1 hour break)

Instructors:

Vivian Zhang (CTO @Supstat Inc, Master degrees in Computer Science and Statistics)

Scott Kostyshak (Data Scientist @ Supstat Inc, 5th year Econ PhD at Princeton Univ.)

Cost:

Individual: $220/class, $1100 for all five classes

For group(5 or more persons) and enterprise pricing, please email vivian.zhang@supstat.com

The class is extended from first offering 20 hours to 35 hours, the new charge is $1100 for five classes.

If you'd like to sign up and reserve your seat, you can use our site, http://www.nycdatascience.com . Here you can learn a little bit more about our mission, and see upcoming classes.You can also pay directly on meetup.com using the paypal option.

Course Outline:

(Content may be adjusted based on the real teaching condition)

Basics: 6 hours
Abstract: explain the basic operation of knowledge through this unit of study , students can learn the characteristics of R , resource acquisition mode , and mastery of basic programming
Case and Exercise: Using the R language completion of certain Euler Project (euler project)

• 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: 6 hours

Abstract: explain the various ways the R language read data , the participants through the basic WEB knowledge of web crawling , connect to the database via sql statement calling data from a variety of local read excel file data .
Case studies and exercises: crawl watercress data on the site , write a custom function .

• Web data capture
• API data source
• Connect to the database
• Local Documentation
• Other data sources
• Data Export

Data manipulation: 6 hours

Abstract: how to manipulate the data use R for the all kinds of data conversion, especially for string operation processing .
Case studies and exercises : Find the QQ(the most used instant messager 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 6 hours

Abstract: cover two advanced drawing package , lattice and ggplot2, understand the various methods of visualization to explore.
Case and Exercise: Using graphics to right before the movie , text and other data to describe

• Histogram
• Point
• Column
• Line
• Pie
• Box Plot
• Scatter
• Matrix related
• Map

Elementary statistical methods: 6 hours
Abstract: The primary explanation to use R for statistical analysis , regression analysis, students can 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
• Correlation
• T test
• Non-parametric statistics
• Linear Regression
• Regression Diagnostics
• Robust Regression
• Nonlinear regression
• Principal Component Analysis
• Logistic Regression
• Statistical Simulation

Preliminary data mining ( If we finish the class early, we will cover selected topics based on your need)

Abstract: explain the R language for data mining expansion pack and functions use , students can master the supervised learning and unsupervised learning two mining methods .
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