1. Undergrad (UG)s committee
David Feil-Seifer (also on college committee)
Monica Nicolescu
Ming Li
Lei Yang
Dongfang Zhao
Sushil Louis (Chair)

2. Including these slides
Undergrad committee
Including these slides

3. Minor in Big Data
With the rapid advance of computer and internet technologies, a plethora of data accumulates and presents many challenges of big data. The objective of the minor in big data is to provide students with both the technical skills and the theoretical knowledge to address evolving big data challenges. Through core courses and electives, students who complete the minor will develop skills in advanced statistics, machine learning and data mining, plus programming and data management, and build expertise in solutions to a broad range of big data problems.
The minor in big data is open to all students at the university. Completion of minor requirements may require completion of additional courses as prerequisites.

4. Minor in Big Data COURSE REQUIREMENTS
The minor requires a minimum of 18 credits from the courses listed below. No more than nine of the credits can also count towards your major or other degree requirements.

5. Minor in Big Data Required courses (6 credits)
Introduction to Data Management Systems (Dongfang)
Introduction to Big Data (Feng)

6. Minor in Big Data Core courses (6-12 credits)
CS 302 Data Structures
CS 442/642 Cloud Computing
CS 415/615 - Parallel Computing
CS 457/657 - Database Management Systems
IS 475 Database Design and Implementation
CS 491/691 Data Mining (need a new proposal to create a normal course, Lei)
Data-Intensive Computing (Dongfang)
Big Data Systems (Feng)
CS 491H/691H - Data Science & Big Data
Foundation/application courses (6-12 credits)
CS 477/677 Analysis of Algorithms
CS 479/679 Pattern Recognition
CS 482/682 Artificial Intelligence
CS 491/691 Convex Optimization and Engineering Applications (need a new proposal to create a normal course, Lei)
IS Data Resource Management
IS 477 Data Communications
MATH Mathematical Modeling
STAT Statistical Theory
STAT Continuous Statistics
STAT 453/653 Statistics: Discrete Methods

7. Items
New “C or better” in all required courses needs “gradual” updates to course descriptions (DFS)
Any update?

8. Items CA schools transfer agreements Math 301 / CS 365 Recent Issues
Ming please contact them and see what is happening
Math 301 / CS 365
Undergrad committee unanimously passed this on Nov 4, 2016
Dept. Passed this on Nov 14
Now to C*C*C*C, Ivy, and catalog
Recent Issues
CS219 pre-req for CPE301, CS302
ENG300 as a co-req for CS446

9. Items CS for engineers CS for all Meeting with ME soon
Will Dean support resources when needed?

10. CS0 CS 0 for all (committee has not finished discussion)
Possible Pros:
More TAs, More Lecturers, More research time, 2+1  1 + 1
Cons
No TAs, No Lecturers
Consider morphing CS105 into CS 0. But need data
What computing should UNR under-graduates know when they graduate?
Programming (scratch?), Simple databases like Access or Google’s tables, Spreadsheets and Macros, Security essentials, material from current CS105 … ?
Can we do a survey of faculty, alumni, students, … ?
Should be part of Silver Core, required for all?

11. CS0 CS105: Half and half course CS0
Current core objectives plus core science
CS0
Must be part of the core curriculum
Design, group design, design thinking
Large multi-week project versus smaller projects
Careers: Computing in Art, in X
Survey of how embedded computing is in the modern world
Ethics, law, policy
Implications: How does computing relate to the modern world ?
Privacy, Big data, security, AI, networks, …
Future predictions
Algorithms/Programming/Software/Scratch/Mobile
Computing concepts
Architecture
Number systems
Logic