Name: Data Science by R programming(Beginner level, Five Sundays) 001
Start: 2013-11-10T12:00:00-05:00
End: 2013-11-24T12:00:00-05:00
Location: Flatiron School

Update:

our Dec 1st(Sunday, thanksgiving weekend) will be held as planned.

If you can't make thanksgiving weekend class, you can choose from

--attend make-up session on Dec 7th(Sat)

--watch the video from home and take the make-up session in Feb, 2014.

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Date: Nov 10th, Nov 17th, Nov 24th, Dec 1st, Dec 8th (Five Sundays)

Time: 12:00pm to 4pm

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

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

Slides contributor:

Kai Xiao(Data Scientist @ SupStat Inc), Scott Kostyshak, Vivian Zhang

Special Thank:

We thank Ramnath Vaidyanathan(Advisory Data Scientist @ SupStat Inc, professor at McGill University), Joe Cheng(Software Engineer @ Rstudio), Josh Paulson(product manager @ Rstudio) for suggesting us a few Stunning R showcases.

Cost:

Individual: $110/class

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

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

Data for two 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 collation 3 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 3 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 5 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 ( Selected Topics )

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

Vivian Zhang

Scott Kostyshak

NYC Data Science Academy

Technology

Education & Technology

Big Data

Technology Professionals

Data Analytics

Learning

Machine Learning

Data Visualization

Predictive Analytics

Data Mining

Data Science