Data Science by R programming(Beginner level, Five Sundays) 001

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