Data Science by Python(Beginner level,Five Saturdays) P001

Date: Classes will be offered on Mar 15th, 22th, 29th, April 5th,12th(Five Saturdays)

Venue:

http://photos1.meetupstatic.com/photos/event/a/c/6/e/600_341384142.jpeg

http://photos3.meetupstatic.com/photos/event/a/f/2/a/600_341384842.jpeg

Time: 1:15-5:15pm

Instructor: John Downs

Refund Policy:

We offer full refund if you are not happy with the first class and decide to drop it.

Course Outline:

(Content may be adjusted based on the experience of the class)

Week I: An introduction to Python

Reading: Think Python CH 2, 3, 5, 6, 7, 8, 10-15

http://www.greenteapress.com/thinkpython/html/index.html

• basic syntax

• conditionals

• iteration

• functions

• data structures

• classes

Week II: Python Standard Library and Computational Statistics

Reading: Section 9 of the Python standard library http://docs.python.org/2/library/

Think Stats CH 2, 4-9 http://www.greenteapress.com/thinkstats/html/index.html

• Python standard library

• regular expressions

• datetime

• random

• itertools

• functools

• math

• Computational statistics

• descriptive statistics

• probability distributions

• hypothesis testing

• correlation

Week III: Visualization and Exploratory Data Analysis

Reading: Python for Data Analysis CH 5, 7, 9, 10

• Visualization with Matplotlib

• histograms

• line charts

• scatterplots

• pie charts

• boxplots

• animation

• subplots

• Exploratory data analysis with Pandas

• Pandas data structures

• Handling missing data

• Merging, aggregating and transforming data

• Sampling

• Time series

Week IV: A gentle introduction to scientific computing and machine learning

Reading: Python for Data Analysis CH 4, 11

Doing Data Science: CH 3-5

Optional: Learning Scikit-Learn

• Numpy

• Linear algebra

• Random numbers

• Testing with bumpy

• A very gentle introduction to Scikit-learn

• Linear regression

• K-Nearest Neighbors

• K Means

• Naive-Bayes

• Logistic Regression

Week V: Building a data product

Reading: Doing Data Science CH 8-9

• Using web APIs

• requests library

• web scraping

• Databases - pymongo

• Building a web application with Flask