Reading

If you'd like to read ahead, you can find approximate reading below

Week 1:

• Reading: Network Science book, Chapter 1. You can get the whole book for free here. Or buy it at the Campus Bookstore.

Week 2:

• Reading: Reading. Chapter 2, and 3 (section 3.1-3.7 ... the most important part is 3.1-3.4, so focus on that) of Network Science. You can find the entire book online here.

Week 3

• Reading: We will continue with Network Science. We start with the rest of chapter 3 with an emphasis on 3.8-3.9. Then we read Chapter 4, section 4.1 - 4.7 and Chapter 5, section 5.1 - 5.5.

Week4

• Reading: There's no reading today. You'll have enough to do with running regular expressions and other fun things. So if you're bored, this is a good time to catch up on the stuff from the previous weeks that you haven't looked at.

Week 5

• Reading: This week, the reading is mostly for reference. It's for you to have a place to go, if you want more detailed information about the topics that I cover in the video lectures. Thus, I recommend you check out Chapter 9 of the network science book. In particular, we'll delve into section 9.4 in the exercises. We will also talk a little bit about degree correlations - you can read about those in Chapter 7.

Week 6

• Reading: Natural Language Processing with Python (NLPP) Chapter 1, Sections 1.1, 1.2, 1.3. (It's avaukabke online) and NLPP Chapter 2.1 - 2.4.

Week 7

• Reading I: NLPP Chapter 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.9, and 3.10. It's not important that you go in depth with everything here - the key think is that you know that Chapter 3 of this book exists, and that it's a great place to return to if you're ever in need of an explanation of regular expressions, unicode, etc. We will also work with network communities.
• Reading II: Check out the wikipedia page for TF-IDF.
• Reading III: Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter
• Reading IV (Optional): Chapter 9 of the Network Science Book.

Week 8

• Reading: Network Science, Chapter 10.1, 10.2. (This is all you have to read, but I recommend the rest of Chapter 10 if you're interested in epidemics and networks.)