CS2013-final-report

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Body of Knowledge coverage [List the Knowledge Units covered in whole or in part in the course. If in part, please indicate which topics and/or learning outcomes are covered. For those not covered, you might want to indicate whether they are covered in another course or not covered in your curriculum at all. This section will likely be the most time-consuming to complete, but is the most valuable for educators planning to adopt the CS2013 guidelines.] KA Knowledge Unit Topics Covered Hours XY Full name of KU [Include explanation as needed] Num Additional topics [List notable topics covered in the course that you do not find in the CS2013 Body of Knowledge] Other comments [optional] - 233 -
CSCI 140: Algorithms, Pomona College Claremont, CA 91711, USA Tzu-Yi Chen tzuyi@cs.pomona.edu http://www.cs.pomona.edu/~tzuyi/Classes/CC2013/Algorithms/index.html Knowledge Areas that contain topics and learning outcomes covered in the course Knowledge Area Algorithms and Complexity (AL) 29 - 32 Software Development Fundamentals (SDF) 1.5 Parallel and Distributed Computing (PD) 0 - 3 Total Hours of Coverage Where does the course fit in your curriculum This is a required course in the CS major that is typically taken by juniors and seniors. The official prerequisites are Data Structures (CSCI 062, the 3rd course in the introductory sequence) and Discrete Math (CSCI 055). However, we regularly make exceptions for students who have had only the first 2 courses in the introductory sequence as long as they have also taken a proof-based math class such as Real Analysis or Combinatorics. Algorithms is not a prerequisite for any other required classes, but is a prerequisite for electives such as Applied Algorithms. What is covered in the course This class covers basic techniques used to analyze problems and algorithms (including asymptotics, upper/lower bounds, best/average/worst case analysis, amortized analysis, complexity), basic techniques used to design algorithms (including divide & conquer / greedy / dynamic programming / heuristics, choosing appropriate data structures), and important classical algorithms (including sorting, string, matrix, and graph algorithms). The goal is for students to be able to apply all of the above to designing solutions for real-world problems. What is the format of the course This is a one semester (14 week) face-to-face class with 2.5 hours of lecture a week. How are students assessed There is a written assignment (written up individually) due almost every class as well as 1 or 2 programming assignments (done in groups of 1-3) due during the semester; solutions are evaluated on clarity, correctness, and (when appropriate) efficiency. Students are expected to spend 6-10 hours a week outside of class on course material. There are also 1 or 2 midterms and a final exam. Students are expected to attend lectures and to demonstrate engagement either by asking/answering questions in class or by going to office hours (the professor's or the TAs'). Course textbooks and materials The textbook is Introduction to Algorithms, 3rd Edition by Cormen, Leiserson, Rivest, and Stein. For the programming assignments students are strongly encouraged to use their choice of C, C++, Java, or Python, though other languages may be used with permission. Students are required to use LaTeX to format their first 2-3 weeks of assignments, after which its use is encouraged but not required. Why do you teach the course this way This course serves as a bridge between theory and practice. Lectures cover classical algorithms and techniques for reasoning about their correctness and efficiency. Assignments allow students to practice skills necessary for - 234 -
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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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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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Software and Interface Design, Univ
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Computer Systems Security (CS-475),
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Body of Knowledge coverage KA Knowl
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Ullman and Widom, 2 nd edition, Pre
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Technology, Ethics, and Global Soci
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SP Professional Communication These
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Why do you teach the course this wa
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search, probabilistic reasoning, lo
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Students are expected to spend abou
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CS188: Artificial Intelligence, Uni
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Introduction to Artificial Intellig
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Computer Networks, Williams College
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NC Introduction All 1.5 NC Networke
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CSCI 432 Operating Systems, William
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OS Security and Protection Access c
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Assignment Five: Memory management
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Programming Language: C Assignment
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Parallel Programming Principle and
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PD Parallel Algorithms, Analysis, a
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• Grama, A., Gupta, A., Kumar V.
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Body of Knowledge coverage For each
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1: Map-reduce for CS1/WMR 2: Multic
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What is the format of the course Co
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Csc 453: Translators and Systems So
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CSCI 434T: Compiler Design, William
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AR Machine-level representation of
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Languages and Compilers, Utrecht Un
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COMP 412: Topics in Compiler Constr
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separate compilation is the key fea
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programming languages. A major them
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CSCI 1730: Introduction to Programm
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CSC 2/454: Programming Language Des
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PL Runtime Systems Data layout; sto
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How are students assessed Over 10 w
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Programming Languages and Technique
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esults from programming language th
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PL Event-driven and Reactive Progra
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Overall, students learn the followi
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PL Type systems Compositional type
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What is the format of the course Th
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Introduction to Computer Science, H
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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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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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