Week 1

• Jan 22 Introduction
• Analyzing text to gain insight about people and about language:
• Google trends
• Word watchers: tracking the language of public figures
• Language Log: more on the language of politicians
• Language Log: the language of real estate ads
  
• Language Log: the decline of passivity
  
• Language Log: decreasing definiteness
  
• Language Log: when did spelling become more consistent?
• Language Log: almost literally, and literally
  
  
• Jan 24 Python: Installation session
  
• We will be installing:
• Python: https://www.python.org/downloads/
• numpy: https://numpy.org/
• matplotlib: https://matplotlib.org/
• Jupyter notebooks: https://jupyter.org/
• NLTK: Installing NLTK itself: http://www.nltk.org/install.html You also need the NLTK data, see http://www.nltk.org/data.html

Week 2

• Jan 27 Python: Programming first steps
• Code for download
  
  
• Jan 29 Exploring and visualizing data
• Code for download.
• Data for download
  
  
• Jan 31 Exploring and visualizing data, continued
  

Week 3

• Feb 3 Python basics: conditions and lists
• Code for download
  
• Feb 5 Python basics: loops
  
  
• Feb 7 What can I do for my project?
  

Week 4

• Feb 10 Python basics: dictionaries, and how to count words
  
  
• Feb 12 Python basics: preprocessing text and counting words
  
• Homework 1 due
  
  
• Feb 14 Descriptive statistics: central tendency and spread
• Readings: SE chapter 2
• Do women talk more than men do? Here are some relevant Language Log posts:
• http://itre.cis.upenn.edu/~myl/languagelog/archives/003420.html
• http://itre.cis.upenn.edu/~myl/languagelog/archives/003607.html
  

Week 5

• Feb 17 Descriptive statistics continued
  
  
• Feb 19 We talk in class about your project ideas.
  
  
• Feb 21 Probability distributions and what we can do with them
• Readings: SE ch. 3

Week 6

• Feb 24 Probability distributions, samples, and populations
  
  
• Feb 26 Statistical tests: the basic principle; significance thresholds
• Readings: SE ch. 4,5, 6
  
• https://xkcd.com/539/
• Initial project description due.
  
  
  
• Feb 28  The t-test
• Readings: SE ch. 7
  

Week 7

• Mar 2 The t-test and confidence intervals
• Readings: SE ch. 8, 9
  
  
• Mar 4 Python: defining your own functions, and structuring your programs
  
• Homework 2 due.
  
  
• Mar 6 Python functions, classes, and objects
  

Week 8

• Mar 9 Comparing more than two data sets: ANOVA
• Pitfalls of statistical analysis: The problem of multiple testing https://xkcd.com/882/
• Readings: SE ch. 11, 12
  
  
• Mar 11 ANOVA, continued
  
  
• Mar 13 Different tests for different types of data
  

Week 9

Week 10

• Mar 23 Correlation and linear regression
• Readings: SE ch. 20, 261-70
• https://xkcd.com/552/
  
• https://9gag.com/gag/2344861
• Language Log on correlation and causation
  
• And another note on correlation and causation: "Nobel prize winners eat more chocolate"??
  
  
• Mar 25 More linear regression
• Readings; SE rest of ch. 20, ch. 21 
• How not to do a scientific study: "p-value hacking"
• A  linear regression tutorial
  
  
• Mar 27 Practicing regression
  

Week 11

• Mar 30 Logistic regression
  
  
• April 1 Model comparison
  
• Intermediate project report due.
  
  
• April 3 Practicing regression
  

Week 12

• April 6 Practicing regression
  
  
• April 8  Python: advanced pandas: sorting data frames
  
• Homework 3 due.
  
  
• April 10 Python pandas: grouping, merging, appending data frames
  

Week 13

• April 13 Python: advanced data visualization
  
  
• April 15 Python: advanced data visualization, continued
  
  
• April 17 Python: list comprehensions, and exceptions
  

Week 14

• April 20 Clustering for data analysis
  
  
• April 22 More clustering
  
• Homework 4 due.
  
  
• April 24 Clustering and visualization
  

Week 15

• April 27 Classification
  
  
• April 29 Classification continued
  
  
• May 1 Experimenting with classification
  

Week 16

• May 4: Project presentations
  
  
• May 6: Project presentations
  
  
• May 8: Project presentations