hankroark/rp-pds15

Practical Data Science course notes offered at the University of Illinois, Research Park in Spring 2015

Practical Data Science

University of Illinois, Research Park
Instructor: Robert J. Brunner
Spring 2015

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Orientation Week :

Install the Docker Engine and the Docker container built for this Practical Data Science course. You also should visit the course Piazza.

Week 1: Data Science at the Command Line:

Use the Docker technology by working at the Unix command prompt within the course Docker container in interactive mode. This will focus on using Unix command line tools and techniques to work with data in the BASH shell

Week 2: Practical IPython:

Learn how to use the IPython notebook by using the course Docker container in server mode. Also learn basic Python programming, python data types, and file I/O, before finishing with a quick overview of the numpy and scipy libraries.

Week 3: Exploring Data Through Visualizations:

Learn how to make data visualization by using Python, primarily from within the IPython notebook by using matplotlib and seaborn. This will include a discussion of scatter plots, linear regression and plotting, histograms, box plots, and other advanced visualization concepts.

Week 4: Using Python DataFrames (Pandas):

Learn about the Data Frame concept and how to use it within Python by using the Pandas library. This will include ways to load and work with large tabular data, and to performa basic data operations like cleaning, transforming, merging, and reshaping.

Week 5: Using Databases:

Learn about database technology, before specifically focusing on relational database management systems. This will include learning how to create database, and SQL DDL and DML to create, insert, update and delete data. This will conclude with a discussion of accessing a database from Python.

Week 6: Data Acquisition:

Learn about acquiring data from diverse sources including webpages, online repositories, and social media. This will require a discussion of web scraping, DOM tress, and JSON.

Week 7: Statistical & Machine Learning:

Review basic statistics and probability and learn how to compute different random distributions by using numpy and scipy routines. Next, learn about machine learning and basic approaches to perform machine learning by using the scikit_learn library in Python.

Week 8: Data Intensive Computing:

Learn about basic concepts in high performance computing and how to perform them in Python. Next, learn about cloud computing, including how Docker technology integrates into commercial clouds. Finally, a discussion of the standard Hadoop platform and its capabilities.

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The Practical Data Science course License

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