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

  • Feb 2, 2014 · 10:00 AM

Please RSVP at

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

class reviews can be found:


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)

Location: AlleyNYC 500 7th ave 17th floor, New York, NY


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.)

Our learning from offering last R classes can be found at


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

For group(5 or more persons) and enterprise pricing, please email [masked]

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, Here you can learn a little bit more about our mission, and see upcoming classes.You can also pay directly on 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), intensive data mining class is offered through our intermediate R class at and kaggle hands-on class

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

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  • Mike S.

    I took the initial version of this class late fall 2013 and found it to be well worth the time. The slides, examples and exercises were well organized. Scott Kostyshak's presentation style is clear and concise. The second iteration will have twice the classroom hours and cover a lot of material that there wasn't time for in the initial format. It's worth the investment if you want to dive into the R ecosystem.

    January 30, 2014

1 went

Your organizer's refund policy for Data Science by R programming(Beginne­r level, Five Sundays) R002

Refunds offered if:

  • the Meetup is cancelled
  • the Meetup is rescheduled
  • you can cancel at least 3 day(s) before the Meetup

Payments you make go to the organizer, not to Meetup. You must make refund requests to the organizer.

Our Sponsors

  • NYC Data Science Academy

    use"nycopen100ff" coupon to take classes on

  • Supstat

    Supstat shares its expertise in data mining and visualization.

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