Please RSVP at http://nycdatascience.com/course/data-science-by-python/
Old Date: April 19th, 26th, May 3th, 10th, 17th (five Saturdays)
New Date: April 26th, May 3th, 10th, 17th, 31th (five Saturdays)
(We made such change because April 19th and May 24th are Easter and Memorial weekend)
Instructor: John Downs
Project Demo Day and Certificates: The course is five days and ends with a demonstration of a project of your choice on Project Demo Day. On Demo Day you will showcase a project of your choosing, utilizing the tools and skillsets taught to you throughout this course. We encourage you to be creative! The possibilities are nearly endless! All the instructors will help you to implement your own project.
After the 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.
Cost: $850 for all five classes
Note,we don't sell individual class. It is a big commitment for us to assist you to be able to do significant analytic work and also your commitment to do a good job in the class.
For group(5 or more persons) and enterprise pricing, please email [masked].
It is preferred if you can paypal [masked](SupStat Inc business acct) to RSVP you seat and pay $1 on meetup.com since meetup charges 15% transaction fee.
We offer full refund if you are not happy with the first class and decide to drop it.
(Content may be adjusted based on the experience of the class)
Week 1: Intro to Data Analysis with Python - 4 hours
Abstract: An introduction to the Python language and libraries for data analysis. An overview of data mining methods.
Exercises: Project Euler, New York City Housing Data
* How to learn Python
* Python resources
* Language Overview
* Data Analysis Overview
Week 2: Visualization and Algorithms - 4 hours
Abstract: Data visualization, collection and regression
Exercises: NYC Housing Data, Web Scraping
* Graphics with Matplotlib
* Collecting data from the web
* Data Aggregation
* Linear Regression
* Logistic Regression
Week 3: Machine Learning - 4 hours
Abstract: Machine learning with Scikit-Learn
Exercises: New York Times article classification, Ad Click prediction
* Decision Trees
* Supervised Learning
* K Nearest Neighbors
* Unsupervised Learning
* K Means
* Spam Filtering
* Naive Bayes
Week 4: Time Series and Financial Modeling - 4 hours
Abstract: Analysing time series data, models for finance, causality and feedback loops
Exercises: Yahoo Finance
* Selecting Features
* Time Series with Pandas
* Feedback loops
* Financial Models
Week 5: Building a Data Product - 4 hours
Abstract: An overview of some data products and hands on work
Exercise: Recommendation Engine
* Web Frameworks
* Intrusion Detection
* Recommendation Engines