Lectures

Cogs 108 SS1_23

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Click the links in the topics column to download pdf of lecture notes.

Current week is highlighted in yellow. Topics may change slightly (or order of topics).

I'll do my best to post the slides before lecture, but if not possible I will post at the beginning of lecture either to canvas or github - historically in that room canvas works better for some reason. I had the opposite with another class in a different room.

Week	Lecture #	Date	Topics	Description	Section topics	Assignments
wk1	1	7/3	Lecture 1 L1_Podcast	Introductory lecture and welcome to COGS108!
wk1	2	7/3	Lecture 2 L1_Podcast (starts@ 1:41)	Version control, git and github	Introduction, D1, A0, accessing course resources D1slides	Assigned: D1, A0 (Practice),
wk1	3	7/5	Lecture 3 L2_Podcast	Introduction to website Data Structures, Data Intuition and Wrangling
wk1	4	7/5	Lecture 4 L2_Podcast	Programming, Python review, Jupyter notebooks	D2slides, D2, A1	Assigned: D2, A1, Prev Proj. Review, LQ1
wk2	5	7/10	Lecture 5 L3_Podcast	Data ethics and privacy Integrity in qScience/Engineering
wk2	6	7/10	Lecture 6 L3_Podcast	Project proposal and previous project review intro, groups Questions in data science Visualization P1 (notebook)	Github review git review command line review, EDA and Visualization D3 slides D3	Assigned: D3, Project proposal Previous project review
wk2	7	7/12	Lecture 7 L4_Podcast	Analysis
wk2	8	7/12	Lecture 8 L4_Podcast	EDA	D4 D4 Slides	Assigned: D4, A2 LQ2
wk3	9	7/17	Lecture 9 L5 Podcast	Text/Sentiment Analysis
wk3	10	7/17	Lecture 10 L5 Podcast	Inference	D5 D5_Slides (inference)	Assigned: D5, Checkpoint 1: Data
wk3	11	7/19	Lecture 11 L6_Podcast	Perceptually aware visualization
wk3	12	7/19	Lecture 12 L6_Podcast	Machine Learning	Work on project, checkpoint questions	Assigned: D6, A3, CP1
wk4	13	7/24	Lecture 13 L7_Podcast (temporarily takes you to media gallery)	Nonparametric statistics A3 discussion, EDA checkpoint
wk4	14	7/24	Lecture 14 L7_Podcast(temporarily takes you to media gallery)	Geospatial analysis	D6 D6_Slides (text_analysis)
wk4	15	7/26	Lecture 15 L8_Podcast	ML examples and decision trees p2 and ethics p2
wk4	16	7/26	Lecture 16 L8_Podcast	Dimensionality reduction, PCA, CCA, ICA	Second part of D6 review EDA checkpoint	Assigned: CP2, LQ3, D7
wk5	17	7/31	Lecture 17 L9_Podcast	Communication
wk5	18	7/31	Lecture 18 L9_Podcast	Jobs P1	Machine learning D7 D7_slides	Assigned: LQ4, A4
wk5	19	8/2	Lecture 19 L10_Podcast	Summary of the course, looking back
wk5	20	8/2	Lecture 20 L10_Podcast	Final lecture The future of data science More on careers Advice for the future	Closing Discussion section - thoughts from the TAs Slides	Final Project, video, final project survey, post course survey, extra credit video surveys