Course syllabus for CIS 705 (co-listed as ICS 691E)
a. Course alpha and number, and course title.
CIS 705 Data Science Foundations
ICS 691E Data Science Foundations
Lipyeow Lim
lipyeow@hawaii.edu
c. Course description.
This is a graduate course introducing all aspects of data science foundations
covering 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.
d. Course objectives.
The objective of this course is to equip a graduate student with the processes,
the knowledge and the skills required to tackle a data science problem in any
application domain at the graduate level.
e. Student learning outcomes.
The successful student would
- Understand the ethical and regulatory issues surrounding data, the analysis of
data, and the use of the analysis results.
- Be able to adapt and apply the data science process/methodology to any
application domain
- Be able to acquire and clean data for analysis using the appropriate software
and hardware tools
- Be able to model and analyze the data to derive insights
- Be able to visualize, interpret and communicate the data and the analysis
results to various audiences
- Be able to publish, curate and/or preserve/destroy the data and analysis results
f. Number of credit hours
3 credit hours
g. Prerequisites
None.
h. Textbooks, required readings
Readings will be drawn from a variety of sources including but not limited to
the following:
- DSFS - Data Science from Scratch: First Principles with Python. 1st Edition. Joel Grus.
O’Reilly Media.
ISBN-13: 978-1491901427
- DMAA - Data Mining and Analysis: Fundamental Concepts and
Algorithms.
Mohammed J. Zaki, Wagner Meira, Jr.
Cambridge University Press
ISBN-13: 978-0521766333
- Think Stats
2e, Allen B.
Downey.
- The White House, Big Data: Seizing Opportunities, Preserving Values (2014)
- Neil Richards and Jonathan King, Three Paradoxes of Big Data, 66 Stanford Law
Review Online 41 (2013).
- Foundational Methodology for Data
Science.
- The Data Science Handbook
- Booz Allen Hamilton. The Field Guide to Data Science (2nd
Ed.)
- Solving Problems with Visual
Analytics
i. Grading and Student Evaluation.
Students will be evaluated on 2 mini-project-like homework
assignments.
- Homework 1 (30%): Applying (a subset of) the Data Science Methodology to a
given data set.
- Homework 2 (70%): Applying the Data Science Methodology to a Second Application Domain
j. Classroom policies
Standard classroom policies of the University of Hawaii at Manoa.
k. Weekly or daily schedule of topics and readings, including exam dates
(Tentative).
Week 1: Data science tools (python): basic scripting.
Week 2: Data science tools (python): manipulating data
Week 3: Data science tools (python): data visualization
Week 4: Data Science Process and Methodology
Week 5: Ethics, Policy and Regulatory issues in data science
Week 6: Data acquisition & cleaning
Week 7: Data analytics: basic statistical analysis. Homework 2 due.
Week 8: Data analytics: regression analysis
Week 9: Data analytics: classification
Week 10: Data analytics: cluster analysis
Week 11: Communication, publication, curation and preservation. Homework 3 due.
Week 12: Big Data Tools, Platforms, & Technology
Week 13-16: Applications of data science in various domains, industries and sectors (4 weeks). Homework 4 due on week 16.