Welcome to Data Science Discovery!
Karle Flanagan
Wade Fagen-Ulmschneider
Introductions
No good party starts without introductions...
Introductions
Karle Flanagan (kflan)
Instructor of Statistics
College of Liberal Arts and Sciences
Wade Fagen-Ulmschneider (waf)
Teaching Asst. Prof. of Computer Science
College of Engineering
Introductions
Syllabus
Course Website: http://courses.las.illinois.edu/spring2019/stat107/�...or: Google “STAT 107 uiuc”�...or: http://go.illinois.edu/stat107
Introductions, Part II
Open up this Google Sheet on your phone/computer: https://docs.google.com/spreadsheets/d/15gPquAhVuQBb2csckZUo5FrZHQN69X0p8IgQ4DlYsCg/edit?usp=sharing
...link is also on the STAT 107 website.
What to expect this semester?
CRN,Course Subject,Course Number,Course Title,Course Section,Sched Type
41758,AAS,100,Intro Asian American Studies,AD1,DIS
47100,AAS,100,Intro Asian American Studies,AD2,DIS
47102,AAS,100,Intro Asian American Studies,AD3,DIS
51248,AAS,100,Intro Asian American Studies,AD4,DIS
51249,AAS,100,Intro Asian American Studies,AD5,DIS
What to expect this semester?
Data Science Tool: Jupyter
Interactive programming tool, optimized for programming data science-type questions.
Main benefit “jupyter notebooks” that allow us to program and document our process in the same interactive file:
Data Science Tool: Pandas
Python Data Analysis Library:
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At its core, pandas provide Excel-like access to datasets. Can quickly become much more power than Excel, particularly for large datasets.
Data Science Knowledge: Statistics
How can we know if A is better than B?
How can we compare A with B?
How do we do this under uncertainty?
Experimental Design