Professor Donald Bowen
Office: RBC 460
TA - Daniel Appierto
(1) puts the world of data at your fingertips, (2) makes the essential task of collaboration — a must in (complicated and large) real-world projects — easier. We will work in Python, and work on applying the lessons to financial problems.
Office hours, actually let's call these...
I suspect that the initial learning curve will be steep, so I have posted a lot of office hours. Around Valentines Day, I will reduce my explicit office hours. Remember, when you get stuck, to check out the resources in the Resources and Help tab.
In RBC 460, with Prof. Bowen:
In RBC 407, with Mr. Appierto:
These are subject to change, so please check back!
(1) project management and programming golden rules>
* = $$$, and ** = :)
(2) our data science “stack”
(3) data: cleaning, exploring, visualizing and organizing
(4) web crawling and data scraping
(5) prediction models and data analysis
* “A few times a year, I get asked to be a judge of student statistical projects in politics or sports. While the students are very bright, they spend WAY too much time using fancy statistical methods and not enough time framing the right questions and contextualizing their answers. If you want to be a good data scientist, you should spend ~49% of your time developing your statistical intuition (i.e. how to ask good questions of the data), and ~49% of your time on domain knowledge (improving overall understanding of your field). Only ~2% on methods per se.” - Nate Silver
Unless explicitly noted, all assignments must be completed individually. Changes to submissions after the deadline will be ignored, and missed assignments will receive a zero.
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There is nothing here, because the class hasn’t started! Who is to say what is “frequent”? But this section will constantly evolve. Please check back often.
This course draws on great work, including: