Week 3!

Dad Joke

• What did the French man say when he went down the slide?
• Yeeesss!

Housekeeping

• Ideas for projects due today
  • Feedback within the next few days
• Pep talk :)
  • It’s OK to be lost
  • The class is front-loaded; we will slow down
  • Move catch up week?

Concepts I want to discuss

• Importing
• Comments in code

Comments and functions

def exclaim(s):
  # Changes a normal string into an exclamation!
  s = s.upper()
  return s + '!!'
  
exclaim("Hello, everyone")

'HELLO, EVERYONE!!'

Concepts I want to discuss

• Debugging
  • print()
  • intermediate outputs
  • KISS
    • Simple functions
    • Understandable variable names

Other concepts that are confusing?

Code Challenge Review

Paper discussion

Analyzing large datasets is different

• Often trace data is complete*
  • Doesn’t suffer from sampling bias
  • But lots of other biases:
    • Sampled population != population of interest
    • Reliant on systems / APIs produced by others
    • Bugs in code
    • Construct validity

Network analysis

• Turn relationships into networks
• Visual representations can be confusing
• Useful when relationships are important part of understanding a process

Text analysis

• Lots of different approaches
  • Dictionary
  • Sentiment analysis

Topic modeling

• Goal is to find a set of k topics where each topic is made up of a combination of terms and each document is made up of a combination of topics.
• Requires a lot of interpretation
• Computational grounded theory (Nelson, 2017) is one attempt to get “best of both worlds”

Prediction vs inference

• Differences in focus
  • Good at predicting outcomes or understanding the process that leads to outcomes
• Prediction approaches
  • Training data and test data
  • Cross-validation and overfitting
  • Cares less about interpretability of the model or connection to theory
• Some of these can (and should!) be used by social scientists.

Reproducibility

• Code and data can be shared really easily
• Replicate this paper?