It is our pleasure to once again offer the intensive R beginner level course for the third time! Beginning this Sunday, the 35 hour course will walk you through the basic operations and characteristics of R, all the way to having a firm understanding of data manipulation and visualization.

Also launching this weekend are two brand new courses, Data Visualization for D3 and Data Science for Python, both for the beginner level.

Taught by preeminent data scientists in New York City, these beginner NYC Data Science Academy courses are the best introduction to the exciting world of R, open data, and statistical science.

If interested, please read the course descriptions below and RSVP today!

1. Data Science by R programming(Beginne­r level) R003

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

Time: 10:00am-5:00pm

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

Cost: $220 per class or $1100 for all five classes.

Note: NYC Data Academy does not offer individual classes. For group(5 or more persons) and enterprise pricing, please email [email protected]

Refund Policy: We offer a full refund if you are not happy with the first class and wish to drop the course.

RSVP: Data Science by R programming(Beginner level, Five Sun) R003

Course Outline:

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

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, and mastery of basic programming

• Case Study and Exercise: Using the R language completion of certain Euler Project (euler project)

2. Getting Data: 6 hours

• Abstract: Explain the various ways the R language reads data, bring the participants through basic knowledge of web crawling, and connect to the database via sql statement calling data from a variety of locally read excel file data.

• Case and Exercise: Crawl watercress data on the site and 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 and use R for the all kinds of data conversion, especially for string operation processing .

• Case Study and 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 and ggplot2) and understand the various methods of visualization.

• Case and Exercise: Using graphics, text and other data

  • Histogram

  • Point

  • Column

  • Line

  • Pie

  • Box Plot

  • Scatter

  • Matrix related

  • Map

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 and regression analysis. Students will master the basic statistical significance and role model.

• Case and Exercise: Using regression to predict commodity prices―simulated casino game winner.

2. Preliminary Data Mining:

• Abstract: Explain the R language for data mining expansion pack and functions use. Students will master two mining methods, supervised learning and unsupervised learning.

• 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

2. Data Visualization for D3.js (Beginner Level) D001

Date: Mar 15th, 22th, 29th, April 5th,12th(Five Saturdays)

Time:  9:00am-1:00pm

Instructor: Adam Pearce is a Data Interaction Developer at Quovo, a web-based investment data analytics and visualization platform. He is one of the top Stack Overflow D3 experts and his work has been featured in The Atlantic Cities, Visualizing.org, visual.ly, and VisualLoop.

Note: NYC Data Academy does not offer individual classes. For group(5 or more persons) and enterprise pricing, please email [email protected]

Cost: $850 per person

Refund Policy: We offer a full refund if you are not happy with the first class and wish to drop the course.

RSVP:  Data Visualization by D3.js (Beginner level,Five Sat) D001

Course Outline:

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

1. Week 1

2. Week 2

3. Week 3

4. Week 4

• Advanced Javascript

  • Functional programing

  • D3 & Arrays

  • Interactive sparkline

  • d3 nest

  • Piecharts

  • Crossfilter

5. Week 5

• Mapping

  • Choropleth

  • Zoom and pan

  • Projections

  • topojosn

We also offer in-depth workshops on real work projects, such as New Yorker Subway income visualization: http://www.newyorker.com/sandbox/business/subway.html

3.Data Science by Python(Beginner Level) P001

Date: Classes will be offered on Mar 15th, 22th, 29th, April 5th,12th(Five Saturdays)

Time: 1:15-5:15pm

Instructor: John Downs is a software engineer here in NYC. John is Data Science enthusiast and an expert in Python and Clojure. John’s experience ranges from use in Python, C/C++, Clojure, Java, Javascript and Matlab.

Cost: $850 per person

Note: NYC Data Academy does not offer individual classes. For group(5 or more persons) and enterprise pricing, please email [email protected]

Refund Policy: We offer a full refund if you are not happy with the first class and wish to drop the course.

RSVP: Data Science by Python(Beginner level) P001

Course Outline:

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

1. Week I: An introduction to Python

Reading: Think Python CH 2, 3, 5, 6, 7, 8, 10-15

http://www.greenteapress.com/thinkpython/html/index.html

2. Week II: Python Standard Library and Computational Statistics

Reading: Section 9 of the Python standard library http://docs.python.org/2/library/

Think Stats CH 2, 4-9http://www.greenteapress.com/thinkstats/html/index.html

3. Week III: Visualization and Exploratory Data Analysis

Reading: Python for Data Analysis CH 5, 7, 9, 10

4. Week IV: A gentle introduction to scientific computing and machine learning

Reading: Python for Data Analysis CH 4, 11

Doing Data Science: CH 3-5

Optional: Learning Scikit-Learn

5. Week V: Building data product

Reading: Doing Data Science CH 8-9