Name: Data Science by Python(Beginner level,Five Saturdays) P001
Start: 2014-03-15T13:15:00-04:00
End: 2014-03-28T05:15:00-04:00
Location: SumAll Foundation

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

Vivian Zhang

NYC Data Science Academy

Technology

Education & Technology

Big Data

Technology Professionals

Data Analytics

Learning

Machine Learning

Data Visualization

Predictive Analytics

Data Mining

Data Science

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

SumAll Foundation

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NYC Data Science Academy

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

NYC Data Science Academy

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