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Instructor is Vivian Zhang (CTO of SupStat & Adjunct Prof. at NYU & StonyBrook).
Sun, Feb 22, 2015 @ 10:00 AM   $2990   WeWork, 205 E 42nd St, 16th Fl
 
     
 
 
              

  
 
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LOCATION
EVENT DETAILS
Each class is 35 hours of classroom guidance with a optional three week-long showcase project of students' own choices and optional presentation of their projects.

Dates: Jan 18th, 24th, 31th, Feb 8th, Feb 15th 2015 (Five Sun)

Dates: Feb 22, Mar 1st, 8th, 15th, 22th 2015 (Five Sun)

Time: 10:00 a.m. - 5:00 p.m.

Instructor: Vivian Zhang, CTO of SupStat Inc., Adjunct Professor at Stony Brook University, Masters degree of Computer Science, and Masters degree of Applied Math and Statistics

Venue: WeWork, 205 E 42nd St, 16th Fl

Room Name: Glass Door Room

**You can contact customerservices@nycdatascience.com for corporate training or small group training opportunities.

Course Overview

NYC Data Science Academy is now offering an intermediate R course in Machine Learning, a five week course designed around students who have taken NYC Data Science Academy's R Beginner course or for those who already have a firm understanding of R and are looking to extend those R skills to machine learning and advanced statistical methods. The goal of this course is to bring our students to near-expert level in this field.

Be sure to read the course syllabus below to ensure your level is appropriate.

Why R is important

R is a powerful, comprehensive, and 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, and data science. And another thing: it's free! As an open-source platform, R has grown to become an incredibly flexible tool that can be applied to nearly every graphical and statistical problem. The community of R users is continuing to add new functionality to the language, and R is often the first statistical tool to provide support for new algorithms and cutting-edge methods in data science.

Frequently asked questions

1. Do I have to do three weeks project? Is it required for taking this class?
Students could choose to spend extra 3 weeks with the teaching crew to do a project of their own choices. We are happy to offer assistance and arrange presentation to demo their work.
2. Can I take class online if I am not in NYC?
You can take it onsite or through recorded sessions on youtube and get timely assistance from teaching crew by google hangout or Skype.
3. If I have to miss some session, how can I make it up?
We record all of our classes and make it available for students right after each class. If you miss a class, you can also get extra help such as office hour or internet support through google hangout or Skype.

Project Demo Day and Certificates

From data mining to time series models, the course ends with a demonstration of a project of your choice on Project Demo Day.
On Demo Day you will present a unique application of the tools and skill set taught to you throughout this course. We encourage you to be creative! Students have chosen projects ranging from digital marketing simulation to finding the relation between people using natural language processing. The possibilities are endless! After successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing, Honorable Graduation, and Active Participation. Certificates are awarded according to your understanding, skill, and participation.

SYLLABUS

Week 1: Introducing Data mining - 7 hours

What is data mining and how to do it
Steps to apply data mining to your data
Supervised versus unsupervised learning
Regression versus classification problems
Review of linear models
Simple linear regression
Logistic regression
Generalized linear models

Week 2: Performance Measures and Dimension Reduction - 7 hours

Evaluating model performance
Confusion matrices
Beyond accuracy
Estimating future performance
Extension of linear models
Subset selection
Shrinkage methods
Dimension reduction methods

Week 3: kNN and Naive Bayes models - 7 hours

The k-Nearest Neighbors model
Understanding the kNN algorithm
Calculating distance
Choosing an appropriate k
Case study
Naive Bayes models
Understanding joint probability
The Naive Bayes algorithm
The Laplace estimator
Case study

Week 4: Tree models and SVMs - 7 hours

Tree models
Regression trees and classification trees
Tree models with party
Tree models with rpart
Random Forest models
GBM models
Support Vector Machines
Maximal margin classifiers
Support vector classifiers
Support vector machines

Week 5: The Association Rule and More Models - 7 hours

Market Basket Analysis
Understanding association rules
The a priori algorithm
Case study
Unsupervised learning
K-means clustering
Hierarchical clustering
Case study
Time series models
Stationary time series
The ARIMA model
The seasonal model
If we finish the class early, we will cover selected topics based on your needs/interests.
 
 
 
 
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