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Beginner, 5 Wks, in Classroom. With John Downs (Python Instructor, Software Engineer in Test @ Yodle)
Saturday, June 21, 2014 at 01:00 PM    Cost: $990
AlleyNYC, 500 7th Ave, 17th Fl
 
     
 
 
              

              
 
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DESCRIPTION
Please RSVP this class at http://nycdatascience.com/course/data-science-by-python/
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Sign up for NYC Open Data Meetup for free workshops twice/week at http://www.meetup.com/NYC-Open-Data/

June 21st, 28th & July 5th, 12th, 19th (five Saturdays, 20 hours)

Beginner level(no programming background required). A course that introduces you to data analysis & machine learning in the Python programming language. We teach Numpy, Panda & more libraries. We cover topics such as nyc open data cleaning, sentiment analysis, web scrapping, face detection a lot of cool stuff.
(April 19th & May 24th are Easter & Memorial weekend, July 5th is independence weekend)

Time: 1:15pm - 5:15pm

Instructor: John Downs, Software Engineer in Test at Yodle

Course Overview: This five week course is an introduction to data analysis with the Python programming language aimed at beginners.

Project Demo Day & Certificates: From the rudimentary building blocks of programming basics, to data manipulation & use of advanced drawing packages, the course ends with a demonstration of a project of your choice on Project Demo Day. On Demo Day you will access & analyze real data, utilizing the tools & skillsets taught to you throughout the course. After the successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing pass, Honorable Graduation pass, & Active Participation pass.

Certificates are awarded according to your understanding, skill, & participation.



Week 1: Intro to Data Analysis

Using Project Euler problems & NYC Housing Data

Overview of the Python language

IPython - Command shell

Libraries & packages for data analysis - Pandas, Numpy, SciPy, Scikit-Learn

Performing basic data analysis



Week 2: Visualization & Algorithms

Using NYC Housing Data

Graphics with Matplotlib

Web Scraping - Collecting data from the internet

Regression Analysis - Linear & Logistic



Week 3: Machine Learning

Using New York Times articles & AdClick

Scikit-Learn - Library for data analysis & data mining

Supervised & Unsupervised Learning

Decision Trees

Cluster Analysis - K Nearest Neighbors & K Means

Spam Filtering

Bayesian Analysis - Naive Bayes



Week 4: Time Series & Financial Modeling

Using Yahoo Finance

Selecting Features

Time series with the Pandas library

Causality

Feedback loops



Week 5: Building a Data Product

Web Frameworks

Intrusion Detection

Recommendation Engines
 
 
 
 
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