| |
|
|
| |
EVENT DETAILS |
Please RSVP this class at http://nycdatascience.com/course/data-science-by-python/
Sign up for the newsletter for free Data Science learning material and upcoming classes at http://nycdatascience.com/
Sign up for NYC Open Data Meetup for free workshops twice/week at http://www.meetup.com/NYC-Open-Data/
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
Causality
Feedback loops
Week 5: Building a Data Product
Web Frameworks
Intrusion Detection
Recommendation Engines
|
|
|
|
|
|
