Python Workshop IIII: warm up for "Data Science by Python" class

This is a past event

32 people went

Price: $5.00
Location image of event venue


This meetup is a warm-up session for for our upcoming Python class. You may RSVP for the class at

John Downs is a Software Engineer in Test at Yodle. He is going to cover Python basics by building a feature for a data product. We're going to build a web interface using Flask to answer some questions about local restaurants.


you should have some programming experience in another language, but not necessarily Python.


Data Science Academy Syllabus:

Date: Classes will be offered on Mar 8th, 15th, 22th, 29th, April 5th(Five Saturdays)

Time: 12:00-4:00pm

Instructor: John Downs

Course Outline:

(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
* IPython
* Language Overview
* Pandas
* Numpy
* Scipy
* Scikit-learn
* 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
* Scikit-Learn
* 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
* Causality
* 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