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

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

Beginner level(no programming background required). A course that introduces you to data analysis and machine learning in the Python programming language. We teach Numpy, Panda and more libraries. We cover topics such as nyc open data cleaning, sentiment analysis, web scrapping, face detection a lot of cool stuff.
(April 19th and May 24th are Easter and 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 and Certificates: From the rudimentary building blocks of programming basics, to data manipulation and 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 and analyze real data, utilizing the tools and 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, and Active Participation pass.

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

Week 1: Intro to Data Analysis

Using Project Euler problems and NYC Housing Data

Overview of the Python language

IPython - Command shell

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

Performing basic data analysis

Week 2: Visualization and Algorithms

Using NYC Housing Data

Graphics with Matplotlib

Web Scraping - Collecting data from the internet

Regression Analysis - Linear and Logistic

Week 3: Machine Learning

Using New York Times articles and AdClick

Scikit-Learn - Library for data analysis and data mining

Supervised and Unsupervised Learning

Decision Trees

Cluster Analysis - K Nearest Neighbors and K Means

Spam Filtering

Bayesian Analysis - Naive Bayes

Week 4: Time Series and Financial Modeling

Using Yahoo Finance

Selecting Features

Time series with the Pandas library


Feedback loops

Week 5: Building a Data Product

Web Frameworks

Intrusion Detection

Recommendation Engines
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