CS Curriculum Revisions 2019-2020
This page lists proposed changes for the CS BS and MS programs that are being put forward during the 2019-2020 academic year, with a goal to be in effect for the fall 2020 term. The point of contact for these changes is Jeff Kinne (firstname.lastname@example.org). Changes have been worked on by the CS regular faculty since the 2017-2018 academic year and result from program review, assessment results, and changes in the field (CS majors tend to go through moderate updates at least once every ten years). All changes were approved by unanimous votes at the department level (vote of all CS regular faculty).
- 1 Attachments and Curriculog
- 2 Undergraduate - Updates Summary
- 3 Graduate - Updates Summary
- 4 Undergraduate - Revised Programs
- 5 Graduate - Revised Programs
- 6 Course Descriptions
- 6.1 BS Major Courses - All Concentrations
- 6.2 BS Major Courses - Computing Science
- 6.3 BS Major Courses - Information Science
- 6.4 BS Major Courses - Data Science
- 6.5 BS Service Courses - Required in Other Majors
- 6.6 BS Elective Courses - Not Required in Any Major
- 6.7 MS Major Courses - Required of All Concentrations
- 6.8 MS Major Courses - Professional Concentration
- 6.9 MS Major Courses - Academic Concentration
- 6.10 MS Major Courses - Bioinformatics Concentration
- 6.11 MS Major Courses - Data Science Concentration
- 6.12 MS Elective Courses - Not Required in Any Concentration
- 7 Curriculog Text
Attachments and Curriculog
Links to attachments that are included in curriculog for the updates to the BS and MS.
- Degree maps / plans - BS and MS
- Curriculum Maps - BS, MS
- Outcomes Library - BS, MS
- Assessment Plans - BS, MS
- Meeting Minutes and results of votes - not included here (some parts of the minutes should not be public) but are attached in curriculog.
- Projected Teaching schedule - google doc
- Links to curriculog for the proposals - Will be added here soon as the proposals are put into curriculog.
Undergraduate - Updates Summary
- Information Science Concentration - revised to be a bit "more applied".
- Computing Science Concentration - will be able to focus a bit more on systems/theory as a result of change to Information Science Concentration (senior-level systems/theory courses can go a bit deeper by only serving the Computing Science Concentration now).
- Data Science Concentration - new concentration added.
- Core - sequencing of the BS core shared by all concentrations has been tweaked.
Minors and Certificates
- Computer Science Teaching minor - new minor that could be an add-on minor to an education minor to prepare to become licensed in computer education. The minor consists of the new course CS 305 (see below) and existing courses CS 101 Fundamentals of Computing, CS 151 Intro to Computer Science, CS 260 Object Oriented Programming, and ECT 173 Fundamentals of Information Technology. (Mapping from courses to IN licensing exam)
- CS Minor and Certificate in Applied Computer Science - updated based on updates to CS core courses.
- Name change CS 201 Programming Structures (was Computer Science I), CS 202 Data Structures and Algorithms (was Computer Science II), CS 303 Discrete Structures and Computing Theory (was Discrete Structures), CS 351 Computer Organization (was CS 451 Computer Architecture), CS 499 Senior Design (was Senior Seminar)
- # of credit hours CS 303 4 hours (was 3), CS 351 4 hours (was 3), CS 499 4 hours (was 1)
- Catalog description updated for many of the courses, as noted in a section below
- Prerequisites CS 351 prereq is 201 (previously 202 and 303), CS 452 (no longer require senior standing - this course is no longer the culminating experience for the major)
- Prerequisites Add CS 351 as a prereq to - CS 456/556. Add CS 303 as a prereq to - CS 456/556.
- Prerequisites Replace CS 202 as a prereq with CS 201 for the following courses - CS 452/552, CS 457/557.
- Required courses no longer required for the BS (to make room for increase # hours in key courses above) - CS 420 Theory of Computation, CS 421 Formal Methods, CS 452 Software Engineering. These will be taught out over the next few years to fill the needs of existing students and will be offered as electives thereafter (on a rotating basis).
The following new courses are being proposed.
- CS 305 Computer Science Teaching Methods (part of CS Teaching minor) model syllabus
- CS 401 Programming for Data Science - undergraduate analog of existing CS 501
- CS 417 Machine Learning - used in data science concentration. Note that a graduate 500 level version is not being proposed (an existing course CS 617 already fills the need at the graduate level).
- CS 456L, 457L, 458L, 401L - 1 credit lab sections to go along with key senior-level courses in each concentration. Note that graduate 500 level versions are not being proposed (the graduate students do not need the extra lab time).
Graduate - Updates Summary
- Computing Science Concentration - streamlined to focus on two key courses (CS 658 Algorithms II and CS 671 Operating Systems II) rather than allowing students to choose.
- Data Science Concentration - new concentration added.
- Professional and Bioinformatics Concentrations - modified to allow either CS 500 Programming Fundamentals or CS 501 Programming for Data Science as the foundational programming course.
- Name change CS 501 Programming for Data Science (was Programming for Data Science & Analytics I), CS 601 Programming for Data Science II (was Programming for Data Science and Analytics II)
- Catalog description updated for many of the courses, as noted in a section below
Updates to course descriptions for many of the courses involved in the above are being proposed.
A new 4+1 accelerated BS+MS is being proposed, which would basically follow the rules imposed by university-wide guidelines and pair undergraduate concentrations with graduate concentrations (e.g., undergraduate Information Science naturally leads into the graduate Professional Concentration).
Undergraduate - Revised Programs
Undergraduate BS Major
Below is a summary of the revised CS BS program - with courses required of all concentrations on the left, and the three concentrations listed in the remaining columns. Credit hours and prereqs in parenthesis.
|All (25)||CS (26)||IS (25)||DS (26)|
Stats - Math 241 or 341 (3)
Math 131, 132 Calc I, II (8)
101 Fundamentals of Computing (3)
Math 131, 132 Calc I, II (8)
Culminating Exam - must be passed before graduating, students who have not passed it will be given an incomplete for their culminating experience until passed. Sample exam and grading rubric is provided at the beginning of each term, exam is given at least twice per term (once during the first half of the term and once during the second half of the term).
Changes The following are the major changes from the the current major
- Newly required - statistics - MATH 241 or MATH 341
- CS 499 - now 4 credits (was 1), name changed (was "Senior Seminar"), will include major programming project
- Now only required for Computing Science Concentration - CS 202, 456, 471.
- Now required for Computing Science Concentration instead of Information Science Concentration - CS 473.
- Newly required for Information Science Concentration - CS 101, CSS 210, CSS 211, CSS 331.
- CS 451 changed number to CS 351 and retitled (had been "Computer Architecture"), CS 201 and 202 names changed (had been "Computer Science I" and "Computer Science II").
- 1 credit lab added to CS 456, 457, and 458. CS 303 made 4 credit hours, was 3.
- New concentration - Data Science.
Minor and Certificate
Note - number of credit hours for courses are in parenthesis.
- Computer Science Minor - modified to consist of - CS 151 (3), 201 (4), 303 (4), 351 (4). This update preserves the requirement that a CS minor takes the "core" of the CS major that is required for most upper level CS courses. Previously, the minor was - CS 151 (3), CS 201 (4), CS 202 (4), 9 credits of CS electives with at least 6 at the upper division level.
- Certificate in Applied Computer Science - modified to consist of - CS 151 (3), 170 (3), 201 (4), 457 (3), 457L (1), 479 (3). Previously, the certificate was - CS 151 (3), CS 170 (3), CS 201 (4), 6 hours of CS electives.
Graduate - Revised Programs
The changes being proposed from the current MS are as follows.
- MS Program catalog description - must pass a comprehensive exam (given at least twice per term, over the foundations of computer science).
- Courses required for all concentrations - unchanged
- Bioinformatics concentration - allow either CS 500 or CS 501 (can count CS 501 in place of CS 500)
- Professional concentration - also require CS 501 Programming for Data Science
- Academic Concentration - At least one theory II course, at least one systems II course, 500 level theory if not taken at ISU, 500 level systems if not taken at ISU.
- Data Science Concentration - new concentration, see below.
- Accelerated MS - 4+1 BS+MS. Common components of all 4+1 programs - student must have at least 3.0 GPA and have completed at least 80% of credits by start of fourth year, can take up to 9 500 level credits as senior and have count for both BS and MS, can take up to 6 600 level credits as senior which do not count for BS or towards 120 credits or for full time status for financial aid. Specific to CS 4+1 - approved to take any 500/600 level CS for which students have met prereqs, a 4+1 degree map for each concentration is attached. The university-wide policies are - policies. An example program is - accelerated Math MS
Data Science - New Concentration
Admissions To declare the data science concentration, students must demonstrate competency (through coursework/transcript or otherwise) in the following - basic programming and data structures, calculus, matrix algebra. To be prepared for the DS MS, an undergraduate at ISU should complete the following - CS 151 Intro to Computer Science, CS 201 Programming Structures, MATH 131/132 Calculus I/II, MATH 313 Elementary Linear Algebra.
- CS 501 Programming for Data Science (3) (if 401 not taken as undergrad)
- CS 557 Database Systems (3) (if 457 not taken as undergrad)
- CS 575 Artificial Intelligence (3) (if 475 not taken as undergrad)
- CS 601 Programming for Data Science & Analytics II (3)
- CS 617 Databases, Data Mining, and Big Data (3)
- MATH 503 Linear Algebra and Modeling for Data Science and Analytics (3)
- MATH 540 Statistics for Data Science & Analytics (3)
- Electives and culminating experience approved by the advisor to complete student's plan of study and at least 17 credits of 600 level coursework
Note - all courses required in the BS and MS programs are listed here. For courses where the catalog description is being updated, the course is marked with ++ (so use Find in your browser and search for ++ to see those courses). Courses where a name/#hours/course# change is being made are listed as the proposed name/#hours/course# but are marked with **; the original name/#hours/course# for these courses are listed above under "Courses".
BS Major Courses - All Concentrations
- CS 151 Intro to CS - ++ Core concepts that are foundational in computer science including - programming, use of computers for dealing with files and programs, how data is stored, number systems. Focus on building skills needed for programming and further study in computer science, and intermediate mastery of a particular programming language. Previous description - History of computers and computer science, principles of process description, and problem analysis. The basic structures of sequence, iteration, and selection. Programming style, artificial intelligence, current applications.
- CS 201 Programming Structures ** (4) - ++ This course is a continuation of CS-151. Advanced programming structures, patterns, and concepts such as classes, classical inheritance, functional programming, singletons, generators, data structures, sorting algorithms, and other design patterns. Previous Description - This course begins with a history of programming languages, then focuses on programming in a particular language. The following topics are covered in some detail: variables, expressions and operators, control structures, simple data types, arrays, classes, and objects. Algorithm design and security issues are also discussed.
- CS 303 Discrete Structures and Computing Theory ** (4) - ++ Mathematics content that is foundational to and useful for computer science. Topics include axioms and proofs, induction, graph theory, probability, finite automata, regular expressions, Turing machines, and the Church-Turing thesis. Previous description - This course is an introduction to discrete mathematics for computer science. The course covers the basic topics from set theory (including functions and relations), logic, number theory, counting, graph theory, and discrete probability. It involves a detailed study of proof techniques.
- CS 351 Computer Organization ** (4) - ++ This course examines in some detail how a computer works. To prepare for this study, students will learn the basics of binary arithmetic, data representation, along with propositional and predicate logic. The major hardware components of a computer, including processors (CPUs), memory (RAM), storage and other peripheral devices will be examined in some detail. Computer software will also be studied. The process of program translation and execution will be outlined. Students will learn machine language and learn to write and run simple assembly language programs. Operating system functions and the organization of file systems will be studied.
- CS 470/570 Programming Languages - ++ An introduction to programming in a wide variety of programming languages and paradigms. Paradigms include: imperative/procedural, object-oriented, functional, logic, concurrent . A focus is placed on obtaining basic proficiency in many languages. Also considered are the syntax and semantics of programming languages. Previous description - The purpose of the course is to develop an understanding of the organization of programming languages and introduce the formal study of programming language specification and analysis. Topics covered usually include: language definition structure, data types and structures, control structures and data flow, run-time consideration, interpretative languages, lexical analysis, and parsing.
- CS 499 Senior Design ** (2+2) - ++ The senior design is a two semester sequence. Students enroll during their last two terms. Students complete an independent project involving the entire development cycle - requirements/needs analysis, prototyping, testing, deployment, maintenance. Students also prepare for the process of applying for and interviewing for jobs or a graduate program. Previous description - The senior seminar is taken by students in their last year. The course prepares students for the process of applying for and interviewing for jobs or a graduate program. Students also present on current topics in computer science.
BS Major Courses - Computing Science
- CS 202 Data Structures and Algorithms** (4) - ++ This course is a continuation of CS 201. It involves a deeper study of algorithm design and analysis and data structures. Students are able to choose the best data structures and algorithms to solve a problem and implement the solution in code. Students also practice programming in a variety of settings, which may include - parallel programming, network programming, graphics, security, and data science. Previous description - This course is a continuation of CS 201. It involves a deeper study of programming languages, but emphasizes programming in a particular language. Topics include algorithm design and analysis, data structures, recursion, threads, network programming, graphics, security, and ethics.
- CS 456 (3) 456L (1) / 556 (3) Systems Programming - An introduction to both program translation and operating systems. There will be a survey of topics such as: top-down and bottom-up parsing, scanning, code generation, symbol table management, linkers and loaders, batch processing systems, interacting processes, multiprogramming systems, and memory management.
- CS 458 (3) 458L (1) / 558 Algorithms (3) - Among the topics covered are: review of basic data structures and their implementations; graphs, both directed and undirected; analysis of algorithms; sorting, searching, and merging, both internal and external methods; memory management algorithms; mathematical algorithms; and, as time allows, advanced topics such as NP-complete problems.
- CS 471/571 OS - ++ This class introduces the basic functionality provided by computer operating systems, and covers three main topics. The first topic is memory management; specifically dynamic memory allocation, dynamic address translation, virtual memory, and demand paging. The second topic concerns processor management and concurrency; how do we manage multiple tasks that execute at the same time and share resources. Subtopics in this section include processes and threads, context switching, synchronization, scheduling, and deadlock. The third topic concerns file systems and storage management; the organization and operation of an example file system will be discussed in detail. Previous description - Major topics include system structure, memory management, and process management. Hands-on experience using the department’s minicomputer facilities are an important part of the course.
- CS 473/573 Networking - The course is an introduction to networking and includes detailed study of Internet protocols and socket programming. Topics include a study of IP, UDP, and TCP protocols, as well as application layer protocols such as HTTP and SMTP. Students learn to program both a client and server.
BS Major Courses - Information Science
- CS 101 Fundamentals of Computing - The main focus of the course is to give students a practical understanding of computing to become well-informed citizens and professionals in the computing age. Topics may include a basic study of - computational thinking, computer security, big data, artificial intelligence, and current trends in computing.
- CSS 210 Intro Networking - ++ Topics include network types and communication models, hardware components, applications, protocols, standards, internetworking and routing concepts, OSI Model, TCP/IP, LAN and WAN networking technologies. Add also - Computer Networks and the Internet, Application Layer, Transport Layer, The Network Layer: Data Plane, The Network Layer: Control Plane, The Link Layer and LANs, Wireless and Mobile Networks, Security in Computer Networks.
- CSS 211 Intro Security - ++ This course focuses on the foundation for the study of computer system security. The course centers around the domains comprising the information security common body of knowledge. Students will learn the security management practices as well as security architecture and models security laws, and investigations. Add also - Introduction, Toolbox: Authentication, Access Control, and Cryptography, Programs and Programming, The Web – User Side, Operating Systems, Networks, Databases, Cloud Computing, Privacy, Management and Incidents, Legal Issues and Ethics, Details of Cryptography, Emerging Topics.
- CSS 331 Files and Databases - ++ This course introduces the concepts and tools surrounding persistent data storage. It covers the physical construction of storage devices, the implementation of file-systems and files, and database design and terminology with an emphasis on implementation in the real world. Previous Description - This course introduces the basic database concepts. The course stresses the implementation of databases in the real world. Students learn about basic database design and terminology, and learn how to create a variety of databases using MS Access.
- CS 457 (3) 457L (1) / 557 ** (3) Database Systems - ++ The course will provide an introduction to the use of data management systems for applications, an understanding of how such systems function, and the advantages and disadvantages of various types of database systems. The first part of the course will deal with the relational model, and will include an introduction to Relational Algebra and SQL. NoSQL database systems will also be studied. Topical examples will include key-store, document, graph, and other categories of database management systems. The underlying data structures and algorithms that support database systems will also be reviewed. Previous description - Data independence, relational model, relational algebra and calculus, query languages and SQL, conceptual modeling, database design, data dependencies and normalization, access methods, tables, queries, forms, macros and reports, database administration, introduction to transaction processing, concurrent transactions, and recovery. Case studies of commercial database systems such as Oracle and Microsoft SQL Server.
- CS 469/569 Unix/Linux Admin & Networking - Includes installation and configuration of Unix/Linux operating system software; set-up of hardware and software for Unix/Linux networking including TCP/IP, FTP, Telnet, DNS, DHCP, and Apache; Unix/Linux administration tasks including directories, users, tuning, backup, security, and networking.
- CS 479/579 Web Prog II - Advanced programming for the World Wide Web and the Internet. This course includes three approaches: the older CGI/PERL, Microsoft’s Active Server Pages (ASP), and Sun’s Java Server Pages (JSP). The course also includes the setup and configuration of World Wide Web servers including Apache and Microsoft’s IIS.
BS Major Courses - Data Science
- CS 202 Data Structures (4) - see under Computing Science Concentration
- CS 401 (3) 401L (1) / 501 (3) Programming for Data Science ** - Intensive programming course with a focus on solving problems in data science, specifically focusing on big data and dealing with different data formats. Students are introduced to data mining and machine learning algorithms with a focus on being able to use programming packages. Data mining and machine learning focus on algorithms that automate the process of discovering patterns in, and devising models for, large datasets. Previous description - Thorough programming course with a focus on solving problems in data science and analytics, specifically focusing on big data and dealing with different data formats.
- CS 417 Machine Learning (3) ** - ++ A continuation of CS 401. A thorough study of data mining and machine learning algorithms, the overall process and the main techniques used in data mining and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and model evaluation.
- CS 457 (3) 457L (1) / 557 (3) Database Processing - see under Information Science Concentration
- CS 475/575 Artificial Intelligence - Concepts and applications, including artificial intelligence programming languages, history, present and future development and research, expert systems, natural language processing, intelligent machines/robots, and vision. Development of artificial intelligence course project.
BS Service Courses - Required in Other Majors
- CS 256 Principles of Structured Design - IT major - An introduction to structured programming and top-down design; applications to a wide variety of practical programming problems.
- CS 260 Object Oriented Programming - some tech majors - Object oriented programming concepts and methods. Includes encapsulation, data abstraction, class development, instantiation, constructors, destructors, inheritance, overloading, polymorphism, libraries, and packages.
BS Elective Courses - Not Required in Any Major
- CS 420/520 Theory of Computation - teach out - A sampling of the different areas of theoretical computer science: finite state concepts, formal grammars and automata, computability, Turing machines, and program verification.
- CS 421/521 Formal Methods - teach out - Elements of formal logic; various approaches to automation including resolution; restrictions and search methods; inductive theorem-proving; Knuth-Bendix completion; Boyer-Moore theorem-prover; applications.
- CS 440/540 Graphics Programming - Development of monochrome and color computer graphics software. Includes animation, two-dimensional translation, rotations, clipping, and magnification; introduction to three-dimensional graphics, hidden lines, paging, windowing, and fonts. Computer graphics course project required.
- CS 452/552 Software Engineering - teach out - ++ This course studies the software life cycle: specification, object-oriented programming and design, program development, validation, testing, debugging, documentation, maintenance, revision control, CASE tools. Remove the following from the description - The course serves as a culminating experience in the CS major. Students complete a significant software project during the course that ties together much of what has been learned in other CS courses. Students give a presentation describing and demonstrating their project; these presentations are open to the rest of the department.
MS Major Courses - Required of All Concentrations
- CS 685 Software Project (3-6) - The total development of an advanced software project in areas such as artificial intelligence, expert systems, data bases, data communications, operating systems, compilers, assemblers, coding theory, graph theory, word processors, and editors.
- CS 695 Computer Science Research (3-6) - Research studies in computer science.
- CS 699 Computer Science Internship (0-3) - Coordinated computer science work experience in business/industry or equivalent project work within the department. A comprehensive written report of the experience, including documented samples of software developed by the student, is required.
MS Major Courses - Professional Concentration
- CS 500 Programming Fundamentals - Review of undergraduate topics in Computer Science including Data Structures, Computer Architecture, and Computer Organization. Review of a computer programming language that students can expect to use in advanced courses.
- CS 600 Concerete Mathematics - An introduction to discrete mathematics for computer science graduate students. Students gain the skills needed to model problems abstractly, analyze solutions, and prove program correctness and efficiency. Typical topics include logic, induction, basic combinatorics, discrete probability, graph theory, and asymptotic growth of functions.
- CS 602 Mobile and Cloud Computing - The course deals with development and design of applications and systems for mobile and cloud computing. Challenges inherent to mobile and cloud computing are studied, such as security, consistency, faulttolerance, useability, and efficiency in mobile and cloud settings. Students are exposed to development on mobile devices and writing software for cloud computing.
- CS 603 Networking and Security - Introduction to the architecture and protocol of the internet and other networks, including networking configuration and management. Security considerations in networking and in general, including the types of exploits that are possible and how to mitigate risk.
- CS 609 Web Programming and Applications - An introduction to development and administration related to the web. Topics may include software design, client-side and server-side development, web applications, data management, and security.
- CS 610 Survey of Programming Languages - ++ A study of programming in a wide variety of programming languages and paradigms. Paradigms include: imperative/procedural, object-oriented, functional, logic, and scripting. A focus is placed on obtaining proficiency in many languages. A second focus is on understanding language theory, design choices, and implementation. Previous description - An introduction to programming in a wide variety of programming languages and paradigms. Paradigms include: imperative/procedural, object-oriented, functional, logic, and scripting. A focus is placed on obtaining basic proficiency in many languages. A second focus is on understanding language theory, design choices, and implementation.
- CS 611 Software Spec and Design - Approaches to software specification and development, such as Z and VDM. The course also explores the use of software tools available for use with software specification and development methods.
- CS 617 Databases, Data Mining, and Big Data - see under Data Science Concentration.
- CS 618 Computational Biology - see under Bioinformatics Concentration.
- CS 619 Trends in CS - A seminar on the current topics within computer science that are both new and of high interest within the research community and/or industry. Exposure to the latest technologies is provided.
MS Major Courses - Academic Concentration
- CS 658 Algorithms II - ++ A continuation of CS 458/558. A complete study of algorithm design techniques, including dynamic programming, greedy algorithms, divide and conquer, network flow, linear programming. Students are able to use these techniques to design algorithms for new computational tasks, including proving the running time and correctness of the algorithms. The course also focuses on NP-complete problems and techniques and analysis for handling computationally hard problems, including approximation algorithms and fixed-parameter algorithms. Previous description - Recurrences, probabilistic analysis, randomized algorithms, red-black trees, amortized analysis, Fibonacci heap, disjoint set union, the all pairs shortest path problem, and maximum flow.
- CS 671 Operating Systems II - ++ A continuation of CS 471/571. A continued look at how operating systems manage the three key resources that must be shared in computing environments - processing, memory, and long-term storage - including the system code itself and tradeoffs between different strategies. A significant programming project is completed, for example writing system modules for a modern operating system environment. The key OS concepts are applied to various settings, including computing clusters, cloud environments, and security considerations. Previous description - Understand and use the basic concepts of operating systems. Introduction of processes such as the processing unit, process management, concurrency, communication, memory management and protection, and file systems.
MS Major Courses - Bioinformatics Concentration
- CS 500 Programming Fundamentals - see under Professional Concentration.
- CS 501 Programming for Data Science I - see under BS.
- CS 600 Concrete Mathematics - see under Professional Concentration.
- CS 557 Database Processing - see under BS.
- CS 617 Databases, Data Mining, and Big Data - see under Data Science Concentration.
- CS 618 Computational Biology - An introduction to computational biology. Topics may include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. A focus is placed on the computational cost of solving problems in terms of CPU time, memory, and disk space. Study of the core algorithms used to solve problems.
MS Major Courses - Data Science Concentration
- CS 501 Programming for Data Science I - see under BS.
- CS 557 Database Processing - see under BS.
- CS 575 Artificial Intelligence - see under BS.
- CS 601 Programming for Data Science II - A second course in programming related to data science and analytics. Ability to handle data in non-standard formats, utilize cloud/cluster for computationally intensive analysis/simulation, and produce interactive interfaces to data and results. Use of machine learning tools.
- CS 617 DB, Data Mining, and Big Data - Introduction to using relational databases to accomplish common data storage and analysis needs. The techniques and tools available to deal with data that is too large to store in a database running on a single server. Topics may include data warehousing, map reduce, association rules, machine learning, and streaming algorithms. Note - keep 501 in Python and 617 in Python?
- Note - for Math 503, update to remove diff eq, put it more towards regression and progression, gradient (since no calc III)
MS Elective Courses - Not Required in Any Concentration
- CS 620 Theory of Comp II - Turing machines, the Church-Turing thesis, decidability, the halting problem, reducibility, recursive function theory, the recursion theorem, time and space complexity, classes P and NP, NP-completeness.
- CS 621 Discrete Struct II - Symmetry and counting, automorphism groups of combinatorial structures, finite fields and applications, error correcting codes, and generating functions.
- CS 652 Soft Eng II - Topics covered include design patterns, aspect-oriented application development, business rule approach to application development, XML processing and applications, refactoring, clean room techniques, and domain driven design.
- CS 670 Concurrent Programming - Fundamental concepts are covered including concurrency, synchronization, safety and liveness, fairness, axioms and inference rules. Concurrent execution with shared variables, semaphores of, critical regions, monitors; concurrent execution using message passing: asynchronous and synchronous message passing; concurrent programming languages and environments.
- CS 673 Networking II - Topics include internetworking philosophies, unicast and multicast routing, congestion control, network quality of service, mobile networking, router architectures, network-aware applications, content dissemination systems, network security, and performance issues. Material for the course will be drawn from research papers, industry white papers, and Internet RFCs.
Catalog description - The following text is used for Justification in curriculog for courses that are only having their catalog description updated - "Catalog description is being updated. This is a minor change. Note that the CS BS and MS programs are also being updated this academic year. This course is a required course in the BS/MS. To see a summary of the rest of the CS curriculum updates being proposed, see https://cs.indstate.edu/wiki/index.php/CS_Curriculum_Revisions_2019-2020".
Prerequisites - For courses that are having prerequisites updated, the following is included in the Justification - "Prerequisites for this course are being updated based on updates to the CS BS/MS. Prerequisite is being updated to - BLANK; previously the prerequisites were BLANK."
Name change - For courses that are having their name changed, the following is included in the Justification - "The name of this course is being updated (previously was BLANK) to more accurately reflect the current content of the course and its place within the BS/MS programs."
Credit hours change - For courses that are having their # credit hours updated, including the following in the Justification - "The # of credit hours is being updated (will be BLANK, previously was BLANK). The previous # of credit hours was not sufficient to adequately meet all of the learning outcomes that are desired for this course."
Lab courses - For new lab courses, or for courses matched with new lab courses, put the following in the Justification - "Note that a lab course is also being added, BLANK that is 1 credit and will give the students more time to work on programs under instructor guidance."
Impact and Notification - Where appropriate, included the following in the Impact and Notify other Departments fields. Impact - "COURSE is required in the MAJOR major and is an option in the MAJOR major. This is a minor update to the catalog description to reflect how the course is being taught." Notification - "The DEPARTMENT(S) has (have) been notified of the update."
Prerequisite - For courses that will not require CS 201 in place of CS 202 (because CS 202 is no longer required of all concentrations), include in the Justification - "Replacing prerequisite of CS 202 with CS 201 because CS 202 will no longer be required for all concentrations in the CS BS.".