CS2013-final-report

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• Grama, A., Gupta, A., Kumar V. (2003, 2nd edn.). Introduction to Parallel Computing. Harlow, England: Addison-Wesley. • To develop parallel programs C/C++ is used, MS Visual Studio, Intel Parallel Studio, cluster under MS HPC Server 2008. Why do you teach the course this way The goal of the course is to study the mathematical models, methods and technologies of parallel programming for multiprocessor systems. Learning the course is sufficient for a successful start to practice in the area of parallel programming. The course has a good reputation among students. The students that are studied this course were the winner in the track “Max Linpack performance” of the Cluster Student Competition at Supercomputing 2011. The course was a part of the proposal that was the winner of the contest “Curriculum Best Practices: Parallelism and Concurrence” of European Association “Informatics Europe” (2011) – see. http://www.informaticseurope.org/services/curriculum-award/105-curriculum-award-2011.html Body of Knowledge coverage KA Knowledge Unit Topics Covered Hours PD Parallelism Fundamentals Overview: Information dependencies analysis, decomposition, synchronization, message passing 2 PD Parallel Decomposition Data and task decomposition, Domain specific (geometric) decomposition 0.5 PD Communication and Coordination Basic communication operations, Evaluating communication overhead 2 PD Parallel Algorithms, Analysis, and Programming Characteristics of parallel efficiency, Spectrum of parallel algorithms (Matrix calculation, System of linear equations, Sorting, Graph algorithms, Solving PDE, Optimization) OpenMP, MPI, MS VS, Intel Parallel Studio 10 PD Parallel Architecture Structure of the MIMD class of Flynn’s taxonomy, SMP, Clusters, NUMA 1.5 PD Parallel Performance Characteristics of parallel efficiency: speed-up, cost, scalability, isoefficiency. Theoretical prediction of parallel performance and comparing with computational results 2 PD Formal Models and Semantics Information dependencies analysis, Evaluating characteristics of parallel efficiency (superlinear and linear speed-up, max possible speed up for a given problem, speed-up of a given parallel algorithm), Equivalent transformation of parallel programs 2 Additional topics Isoefficiency, Redundancy of parallel computations, Classic illustrative parallel problems (readers-writers, dining philosophers, sleeping barbarian, etc.), Tools for development of parallel programs, Formal models based on Information dependencies analysis - 343 -
CS in Parallel (course modules on parallel computing) Richard Brown, St. Olaf College rab@stolaf.edu Libby Shoop, Macalester College shoop@macalester.edu Joel Adams, Calvin College adams@calvin.edu http://csinparallel.org What is CSinParallel CSinParallel is not a single course, but is a project that has produced several modules on parallel computing that are suitable for use in a variety of courses. As such, the description of CSinParallel modules provided here does not match the standard form for course exemplars in <strong>CS2013</strong>. Rather, we list below the modules available from CSinParallel.org at the time of this writing, and indicate for each module the Knowledge Units from <strong>CS2013</strong> that are addressed by that module. The interested reader is encouraged to explore details of each module at the CSinParallel.org web site. Description of the Project from the CSinParallel.org Website CSinParallel modules provide conceptual principles of parallelism and hands-on practice with parallel computing, in self-contained 1- to 3-day units that can be inserted in various CS courses in multiple curricular contexts. These modules offer an incremental approach to getting CS undergraduates the exposure to parallelism they will need as they begin their careers. CSinParallel Modules # Module Name Author(s) 1 Map-reduce Computing for Introductory Students using WebMapReduce Dick Brown and Libby Shoop 2 Multicore Programming (3 versions) Dick Brown and Libby Shoop 3 Concurrent Access to Data Structures (2 versions) Libby Shoop, Dick Brown and Patrick Garrity 4 Parallel Computing Concepts Dick Brown 5 Parallel Sorting Libby Shoop 6 Concurrency and Map-Reduce Strategies in Various Programming Languages Dick Brown 7 Patternlets in Parallel Programming Joel Adams 8 GPU Programming Libby Shoop and Yu Zhao 9 Heterogeneous Computing Libby Shoop 10 Distributed Computing Fundamentals Libby Shoop 11 Pi Calculus: A Theory of Message Passing Dick Brown 12 Exemplar: Drug Design Dick Brown 13 Exemplar: Computing Pi using Numerical Integration Dick Brown and EAPF 14 An Advanced Introduction to Map-reduce using WebMapReduce Dick Brown - 344 -
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Computer Science Curricula 2013 Cur
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Computer Science Curricula 2013 Cop
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CS2013 Steering Committee ACM Deleg
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Chapter 5: Introductory Courses ...
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CC152: Computer Architecture and En
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Programming Languages and Technique
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Chapter 1: Introduction ACM and IEE
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KA subcommittee Chairs (as members
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Knowledge Areas The CS2013 Body of
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of the implications of social respo
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Manaris (College of Charleston), Sa
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Chapter 2: Principles Early in its
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novel, interesting, and effective w
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Appreciation of the interplay betwe
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to be able to communicate with, and
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● The learning outcomes and hour
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● Many strong computer-science cu
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include appropriate elective materi
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We use three levels of mastery, def
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Parallel and Distributed Computing
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select topics from Tier-2 (to inclu
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common“CS0” courses: precursor
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Tradeoffs: A programming-focused in
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Parallel Processing Traditionally,
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Chapter 6: Institutional Challenges
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Computer Science Across Campus An a
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programs do not have an explicit li
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accessible, building interdisciplin
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provide for assessing the effective
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AL. Algorithms and Complexity (19 C
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[Core-Tier2] 7. Describe various he
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Learning Outcomes: 1. Define the cl
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Architecture and Organization (AR)
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7. Evaluate the functional and timi
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AR/Interfacing and Communication [1
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Computational Science (CN) Computat
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CN/Introduction to Modeling and Sim
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CN/Processing [Elective] The proces
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CN/Data, Information, and Knowledge
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Discrete Structures (DS) Discrete s
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DS/Basic Logic [9 Core-Tier1 hours]
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DS/Graphs and Trees [3 Core-Tier1 h
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Graphics and Visualization (GV) Com
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GV/Fundamental Concepts [2 Core-Tie
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GV/Geometric Modeling [Elective] To
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GV/Visualization [Elective] Visuali
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HCI/Foundations [4 Core-Tier1 hours
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HCI/User-Centered Design and Testin
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Learning Outcomes: 1. Explain basic
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o Game engines o Mobile augmented r
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IAS. Information Assurance and Secu
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OS/Concurrency 1.5 OS/Scheduling an
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SP/Social Context 0.5 SP/Analytical
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11. Discuss the importance of usabi
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Learning outcomes: [Core-Tier2] 1.
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Learning outcomes: 1. Describe the
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Information Management (IM) Informa
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• Design of core DBMS functions (
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3. Demonstrate use of the relationa
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IM/Physical Database Design [Electi
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• Presentation, rendering, synchr
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IS/Fundamental Issues [1 Core-Tier2
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4. Describe over-fitting in the con
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IS/Agents [Elective] Cross-referenc
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Learning Outcomes: 1. Explain the d
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Networking and Communication (NC) T
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NC/Networked Applications [1.5 Core
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NC/Social Networking [Elective] Top
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OS/Overview of Operating Systems [2
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OS/Memory Management [3 Core-Tier2
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OS/File Systems [Elective] Topics:
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Platform-Based Development (PBD) Pl
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Learning Outcomes: 1. Design and im
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Because the terminology of parallel
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PD/Parallelism Fundamentals [2 Core
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• Topologies o Interconnects o Cl
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o Software as a service o Security
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PL. Programming Languages (8 Core-T
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PL/Functional Programming [3 Core-T
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o o o Enforce invariants during cod
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PL/Compiler Semantic Analysis [Elec
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• Metaprogramming: Macros, Genera
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PL/Language Pragmatics [Elective] T
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emphasize formal analysis (e.g., Bi
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Learning Outcomes: 1. Analyze and e
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Software Engineering (SE) In every
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such system are assigned to the cor
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Learning Outcomes: [Core-Tier1] 1.
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SE/Tools and Environments [2 Core-T
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SE/Software Design [3 Core-Tier1 ho
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[Elective] • Potential security p
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o o o o Code segments Libraries and
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Systems Fundamentals (SF) The under
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2. Describe how hardware, VM, OS, a
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2. Describe the scheduling algorith
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Social Issues and Professional Prac
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understanding of our commitment to
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Learning Outcomes: 1. Evaluate stak
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Learning Outcomes: [Core-Tier1] 1.
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[Elective] 8. Discuss ways to influ
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• Differences in access to comput
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Appendix B: Migrating to CS2013 A g
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and are treated as a topic geared t
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GV The storage of analog signals in
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SDF This new knowledge area pulls t
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Table B-3: Core Learning Topics and
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Table B-4: New and Expanded Core Le
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IAS Core-Tier1: • Analyze the tra
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IS NC Core-Tier2: • Translate a n
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• Describe at least one design te
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• Explain why the creation of cor
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• Apply simple algorithms for exp
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Appendix C: Course Exemplars While
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IS U. San Francisco Artificial Inte
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ACM/IEEE-CS CS2013 Course-Exemplar
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CSCI 140: Algorithms, Pomona Colleg
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AL PD advanced data structures algo
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What is the format of the course Th
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CS 256 Algorithm Design and Analysi
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and other applications including Ba
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• Data races and higher-level rac
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CS/ECE 552: Introduction to Compute
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Body of Knowledge coverage KA Knowl
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AR Assembly Level Machine Organizat
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Altruism/Collaboration/Competition,
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everyday lives, and therefore at le
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COSC/MATH 201: Modeling and Simulat
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ehaviors, simplifying assumptions;
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MAT 267: Discrete Mathematics, Unio
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Identify a problem and analyze it i
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CS109 covers: • Counting • Comb
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CS 250 - Discrete Structures I, Por
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CS 251 - Discrete Structures II, Po
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DS Graphs and Trees Tree, tree trav
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The course gives an overview to a v
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CS371: Computer Graphics, Williams
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Texture mapping, including minifica
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Other materials are: R and RStudio
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3. Assignment (20% + 10%) Due in we
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CO328: Human Computer Interaction,
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Human Computer Interaction, Univers
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Human-Computer Interaction, Stanfor
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Human Information Processing (HIP),
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PD PD/Parallelism Fundamentals Para
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• Analytical Reasoning: Assertion
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CS1101: Introduction to Program Des
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Finally, the design process from
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documentation activity that occurs
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Why do you teach the course this wa
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SE Software Validation and Verifica
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Cloud computing and the shift in th
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SE-2890 Software Engineering Practi
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Software Development, Quinnipiac Un
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• Networking subsystem focusing o
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SF Input/output Programmed data tra
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◦ C preprocessors, multifile prog
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Ethics in Technology (IFSM304), Uni
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Analyze basic logical fallacies in
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Specific topics include: • Capaci
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Additional topics The sustainabilit
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How are students assessed The basic
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Ethics & the Information Age (CSI 1
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SP Intellectual Property Overview a
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privacy. Students are asked to shar
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The Digital Age, Grinnell College G
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AR Digital logic and digital system
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COS 126: General Computer Science,
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Prospective computer science majors
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CSCI 0190: Accelerated Introduction
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An Overview of the Two-Course Intro
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Course textbooks and materials Free
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programming (scripting) is covered
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One way to document this spiral app
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3. Data types and structures 1. pri
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◦ Comparison of results for small
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Appendix D: Curricular Exemplars Th
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Bluegrass Community and Technical C
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Knowledge Units in a Typical Major
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Notation: < 25% of KU covered # 25-
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Appendix: Information on Individual
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Curricular Analysis The Knowledge U
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management, the Internet, e-mail, t
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Mathematical Foundations (two desig
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Multi-paradigm, Introductory Sequen
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Additional Comments Although the ex
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CSC 312 - Implementation of Program
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• Biocomputation • Unspecialize
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Additional Comments We instituted a
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CS 145: Introduction to Databases T
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Williams College Department of Comp
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Possible Curricular Revisions Note
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CSCI 237 - Computer Organization Th
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surface scattering functions, binar
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A Cooperative Project of
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