Python for Data Acquisition
Python for Data Acquisition

Abstract: 

a. First Hour

i. Installation of Anaconda/Postgresql
ii. Quick review?
iii. File Handling
iv. String formatting?
v. Built in libraries to do:

1. Sys
2. Argparse
3. Os
4. Logging
5. datetime
6. JSON
7. CSV
8. Regular expressions
9. Configparser

vi. Lab: Try out processing some pre-made data with built in tools

b. Second Hour

i. Requests

1. Get data from web HTML
2. Get files from web, CSV/JSON/XML

ii. Beautiful Soup

1. Parse out data from HTML
2. Parse out data from XML
iii. Lab: download and parse data with new tools and from lab 1

c. Third Hour

i. Install Postgresql/pgadmin
ii. Install SQLAlchemy

1. ORM
2. Set up database
3. Set up tables
4. Write data to tables from JSON/CSV
5. Read data back for processing

iii. Lab: store data from lab 2 in database and retrieve/modify it.

Bio: 

Phil Tracton did his BS in Electrical Engineering at University of Maryland and his MS at California State University Northridge. He has spent the last 17 years work on firmware and electronics for implantable medical equipment with Medtronic. He is currently a Principal IC Design Engineer working on deep brain stimulus and implantable drug pumps. For the last 9 years he has been teaching at UCLA Extension. His list of classes include Python Programming 1, Embedded Software 1, and Using FPGA’s in Embedded Systems. He is finalizing his new class on using Python on a Raspberry Pi. In data science and machine learning he is interested in its applications in the biomedical device space.