Events  Classes  Deals  Spaces  Jobs 
    Sign in  
With Scott Kostyshak (Data Scientist @ Supstat) and Vivian Zhang (CTO @ SupStat)
Sunday, July 27, 2014 at 10:00 AM    Cost: $1490
AlleyNYC, 500 7th Ave, 17th Fl

Sign up for our awesome New York
Tech Events weekly email newsletter.
You can contact to get corporate training or small group training.

Date: July 6th, 13rd, 20th, 27th, & August 3rd

Time: 10:00am - 5:00pm

Scott Kostyshak (Data Scientist at Supstat Inc, 5th year Econ PhD at Princeton Univ)
Vivian Zhang (CTO at SupStat Inc, double Masters Degree of Computer Science & Statistics)

Venue: 500 7th Ave, 17th Fl., New York, NY (close to Times Square)

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 & provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon.

Why R is important
R is a free, full, & dynamic programming language that, since its release in 1996, is on course to eclipse traditional statistical packages as the dominant interface in computational statistics, visualization, & data science. As an open-source platform, R has grown to become an incredibly flexible tool that can be applied to nearly every graphical & statistical problem, at virtually no cost to the user. The community of R users is continuing to build new functionality.

Project Demo Day & Certificates
From the rudimentary building blocks of programming basics, to data manipulation & 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 & analyze real data, utilizing the tools & 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, & Active Participation.

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


1. Basics: 12 hours
Abstract: Explain the basic operation of knowledge through this unit of study. Students will learn the characteristics of R, resource acquisition mode, & mastery of basic programming.
Case Study & Exercises: Use the R language to complete certain Euler Project problems.

How to learn R
How to get help
R language resources & books
Expansion Pack
Custom Startup Items
Batch Mode
Data Objects
Custom Functions
Control Statements
Vectorized Operations
2. Getting Data: 6 hours
Abstract: Explain the various ways the R language reads data, bring the participants through basic knowledge of web crawling, & connect to the database via sql statement calling data from a variety of locally read excel file data.
Case & 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
3. Data Manipulation: 6 hours
Abstract: How to manipulate data & use R for the all kinds of data conversion, especially for string operation processing.
Case Study & 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
4. Data Visualization: 6 hours
Abstract: Cover two advanced drawing packages (Lattice & ggplot2) & understand the various methods of visualization.
Case & Exercises: Using graphics, text & other data.

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

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

Descriptive Statistics
Statistical Distributions
Frequency & contingency tables
Linear Regression
T Test
Non-parametric statistics
2. Preliminary Data Mining:
Abstract: Explain the R language for data mining expansion pack & functions use. Students will master two mining methods, supervised learning & unsupervised learning.
Case & 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
© 2017 GarysGuide      About   Terms   Press   Feedback