Name: Data Science by R programming(Intensive Beginner level, Five Sun) R003
Start: 2014-03-16T10:00:00-04:00
End: 2014-03-29T17:00:00-04:00
Location: SumAll Foundataion SumAll.org

Date: Mar 16th, 23th, 30th, April 6th,13th (five Sundays)

Venue:

http://photos1.meetupstatic.com/photos/event/a/c/6/e/600_341384142.jpeg

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Time: 10:00am -5:00pm

Instructors:

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

Cost:

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

Note,we don't sell individual class. It is a big commitment for us to assist you and be able to do significant analytic work and also your commitment to do a good job in the class.

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. It is preferred if you can paypal stoneapple@gmail.com(SupStat Inc business acct) to RSVP you seat and pay $1 on meetup.com since meetup charges 15% transaction fee.

Refund Policy:

We offer full refund if you are not happy with the first class and decide to drop it.

Course Outline:

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

Basics: 12 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

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

Elementary statistical methods:
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:

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

NYC Data Science Academy

Technology

Education & Technology

Big Data

Technology Professionals

Data Analytics

Learning

Machine Learning

Data Visualization

Predictive Analytics

Data Mining

Data Science