Class 6: The Bigger Picture#

Guest speaker#

Joshua Kravitz#

Joshua is the Head of Technology and Data on the U.S. Senate Committee on Appropriations, overseeing efforts to streamline internal processes for improved efficiency and accuracy. Previously, he was a TechCongress Policy Fellow on the House Oversight Committee focused on IT modernization, and Deputy Data Director on Jon Ossoff’s campaign for U.S. Senate. Joshua holds a B.S. in computer science and M.S. in statistics, both from Stanford University.


Final Project peer grading#

Ask Me Anything (AMA)#

Have slides on “Python beyond data analysis” as backup, but would rather talk about what you want to hear about.

Python beyond data analysis#

We’ve been focusing on using Python and pandas for data analysis. What else is Python used for?

Data engineering#

  • Automation / recurring processes

  • Copying/moving/processing/publishing data, especially Big Data

  • Monitoring/alerting

Web development#

  • Building web sites that are interactive (more than just content)

  • Forms

  • Presenting data

  • Workflows, such as:

    • Signing up for things

    • Paying for things

Machine learning#

  • Statistics, but fancy

  • Building models

  • Finding patterns

  • Recommendations

  • Detection

When people say “artificial intelligence,” they usually mean “machine learning.”

Diagram showing what type of machine learning may be useful, if at all

Source, with more thorough explanation

The process#


  1. Create a model

    1. Gather a bunch of data for training

    2. If supervised machine learning, label it (give it the right answers)

    3. Segment into training and test data

    4. Train the model against the training dataset (have it identify patterns)

    5. Test the model against the test dataset

  2. Run against new data

  3. If reinforcement learning, model refines itself

You have a head start: The fundamentals are applicable anywhere you’re using code.

Thanks to the Reader!

Thank you!#

Keep in touch.