Data Science Foundations is a graduate course introducing all aspects of data science foundations to non-specialists - computer programming is not a pre-requisite skill for this course. The course will cover: ethics, policy, regulatory frameworks; the data analysis process; programming tools; data acquisition and cleaning; data analysis and mining methods; data visualization; publication, curation and preservation; applications of data science in various domains, industries and sectors.
For more information, please consult the syllabus.
Instructor: Lipyeow Lim. POST 303E. Wed 2-3pm or by appointment. 808-956-3495. lipyeow at hawaii dot edu.
Examinations: There is no written final exam.
Communications: We will be using Slack for communications (falling back on email where appropriate). Please post questions there so that the whole class can benefit. Remote students (limited to 3 participants) can join the class via appear.in
Late policy: work submitted past due date and time will receive zero credits.
Student Conduct: All students are expected to conduct themselves above and beyond the standard set forth in UH Systemwide Student Conduct Code.
Disability: Any student who feels s/he may need an accommodation based on the impact of a disability is invited to contact the instructor privately. The instructor would be happy to work with you, and the KOKUA Program (Office for Students with Disabilities) to ensure reasonable accommodations in the course. KOKUA can be reached at (808) 956-7511 or (808) 956-7612 (voice/text) in room 013 of the Queen Liliuokalani Center for Student Services.
|Week||Date||Topic||Before Class||In Class||After Class|
|1||Tue Aug 21||Introduction||Slides | Syllabus | Buy vs Rent||Install Python|
|1||Thu Aug 23||No class because of Hurricane Lane|
|2||Tue Aug 28||Introduction||Foundational Methodology for Data Science||Slides | Buy vs Rent||video|
|2||Thu Aug 30||Python||DSFS Ch.1-2||Getting Started with Python||video|
|3||Tue Sep 4||Python - numpy,matplotlib,sklearn||DSFS Ch.3||Getting Started with Python||video|
|3||Thu Sep 6||Databases - data modeling||Slides | Modeling Data||video|
|4||Tue Sep 11||Databases - SQL||Slides | Modeling Data||video | Install sqlite3 | Assignment 1|
|4||Thu Sep 13||Databases - SQL||DSFS Ch.23||Slides | SQL||video|
|5||Tue Sep 18||Databases - SQL||Slides | SQL||video|
|5||Thu Sep 20||Linear Algebra - no F2F class||DSFS Ch.4||Linear Algebra Review|
|6||Tue Sep 25||Linear Algebra - kNN,SVD||DSFS Ch.12||kNN ipynb | Hi.Dim.ipynb | PCA ipynb||video|
|6||Thu Sep 27||Linear Algebra - LDA,DT||DSFS Ch.17||LDA ipynb | DT ipynb||video|
|7||Tue Oct 2||Linear Algebra - SGD,SVM||DSFS Ch.8||video | Assignment 1 Due.|
|7||Thu Oct 4||Linear Algebra - NN,OneVsAll,Kernel,Ensemble||Ch.18||NN demos | Ensemble demos|
|8||Tue Oct 9||Probability & Statistics||TS Ch.1-2||Stats 1||video|
|8||Thu Oct 11||Probability & Statistics - no F2F class||TS Ch.3-4||Stats 2|
|9||Tue Oct 16||DS in Agriculture - smartyields||video||video|
|9||Thu Oct 18||Probability & Statistics - PMF,PDF,CDF||TS Ch.5-6||Stats 3||video | Assignment 2|
|10||Tue Oct 23||Probability & Statistics - two rv,estimation||TS Ch.7-8||Stats 4 | Stats 5||video|
|10||Thu Oct 25||Ethics & Policy - Guest Lecture by Dr. Winter||Readings for Ethics & Policy | WMD Ch.0 | WMD Ch.1||Slides||video|
|11||Tue Oct 30||Probability & Statistics - Hypothesis Testing, CLT||TS Ch.9+14||Stats 6||video|
|11||Thu Nov 1||Data - scraping,probabilistic interpretation,time series,text||TS Ch.12 | DSFS 9+20||Data Preproc.||video|
|12||Tue Nov 6||Election Day Holiday|
|12||Thu Nov 8||Data - embeddings for text & graph,Bayesian stats||TB Ch.1||video|
|13||Tue Nov 13||Probability & Statistics - Bayesian methods||Stats 7||video|
|13||Thu Nov 15||Class Cancelled|
|14||Tue Nov 20||Cluster Analysis - kmeans,hierarchical,Gaussian Mixture||DSFS Ch.19||k-means||video|
|14||Thu Nov 22||Thanksgiving Holiday|
|15||Tue Nov 27||Data Curation - Dr.Sutherland||Slides||video|
|15||Thu Nov 29||Cluster Analysis - freqitem,LDA,Recommender Systems||Slides | Slides | LDA||video|
|16||Tue Dec 4||Project||Project Videos|
|16||Thu Dec 6||Project||Project Videos|
About this site: Modules lists the topics covered. Learning outcomes collect all the desired student learning outcomes of all the modules. Readings list the “passive” learning opportunities like reviewing of textbook sections, web pages, screencasts, etc. Experiences list the “active” learning opportunities where you must actually demonstrate a capability.