Fall 2026 Syllabus

CIS 527 - Enterprise Systems Administration

CC 510 - Computer Systems Administration

Previous Versions

This syllabus covers both courses. They are taught using the same content.

Instructor Contact Information

• Instructor: Mr. Russell Feldhausen (russfeld AT ksu DOT edu)
• Office: DUE 2213, but I mostly work remotely from Kansas City, MO
• Phone: (785) 292-3121 (Call/Text)
• Website: https://russfeld.me
• Virtual Office Hours: By appointment via Zoom. Book time to meet with me

Teaching Assistant

• Sicheng Chen (sicheng AT ksu DOT edu) - Office Hours by appointment. Schedule a meeting at TBA

Preferred Methods of Communication:

• Email: Email is the official method of communication for this course. Any emails sent to the instructor regarding this course should be answered within one class day. Please use the official course email address: cis527-help@ksuemailprod.onmicrosoft.com
• Ed Discussion: For short questions and discussions of course content and assignments, Ed Discussion is preferred since questions can be asked once and answered for all students. Students are encouraged to post questions there and use that space for discussion, and the instructor will strive to answer questions there as well.
• Phone/Text: Emergencies only! I will do my best to respond as quickly as I can.

Prerequisites

• CIS 527: CIS 300.
• CC 510: CC 310 or CIS 300.

Students may enroll in CIS or CC courses only if they have earned a grade of C or better for each prerequisite to those courses.

Course Description

Computer information systems form the backbone of many large organizations, and many students will be called upon in their careers to help create, manage and maintain these large systems. This course will give students knowledge and experience working with enterprise level computer systems including workstation management, file servers, web servers, networking devices, configuration management, monitoring, and more. We will mainly focus on the GNU/Linux and Microsoft Windows server software, and much of the learning will take place in hands-on lab activities working directly with these systems. In addition, students will be responsible for developing some technical documentation and communicating information about their systems in a variety of ways. Finally, throughout the course students will be exposed to a variety of information directly from system administrators across campus.

Student Learning Outcomes

After completing this course, a successful student will be able to:

• Understand the major components of an enterprise level computer network and server system
• Design and implement a simple enterprise level server system and network, as well as provision workstations on that network quickly and easily
• Communicate information about enterprise systems clearly and effectively to users of all skill levels and interests
• Develop ways to increase efficiency by automating tasks whenever possible using scripting and configuration management tools
• Understand and describe security risks in any enterprise system and any ways that they can be mitigated
• Show how to monitor enterprise systems for problems and use that information to locate and fix any issues within the system
• Work with cloud technologies and describe how they can be integrated into an enterprise information technology setup

Major Course Topics

• Configuration Management using Puppet
• Creating Secure Workstations (CIS 225 overview/review)
• Setting up an Enterprise Directory Service & Single Sign On
• Enterprise File Sharing
• Web & Application Servers
• Core Networking Services (DHCP, DNS, ICMP, etc.)
• System Monitoring & Maintenance
• Backup Strategies
• The Cloud & DevOps

Course Structure

This course is being taught 100% online and mostly asynchronous. There may be some bumps in the road. Students will work at their own pace through several modules, with due dates for completion of each module given. Material will be provided in the form of recorded videos, links to online resources, and discussion prompts. Each module will include a hands-on lab assignment, which will be graded interactively by the instructor or TAs. Assignments may also include written portions or presentations, which will be submitted online.

The course will also include a final project and presentation. More information about this can be found in the final project module on Canvas.

The Work

There is no shortcut to becoming a great programmer or system administrator. Only by doing the work will you develop the skills and knowledge to make you a successful system administrator. This course is built around that principle, and gives you ample opportunity to do the work, with as much support as we can offer.

Lectures & Quizzes: Each module will include many lectures and quizzes. The goal is to introduce you to a new topic and provide ample background information, then check for your understanding of the core concepts through the quiz. Many lectures include links to additional resources that you are welcome to review if you want to dig deeper into a particular topic. Those additional resources may also be useful when completing the lab assignments.

Lab Assignments: Throughout the semester you will be building a non-trivial system architecture iteratively; every week a new lab assignment will be due. Each lab builds upon the prior lab’s infrastructure, so it is critical that you complete each lab in a timely manner! This process also reflects the way system administration is done in the real world - breaking large projects into more readily achievable milestones helps manage the development process.

Following along that real-world theme, labs will mostly be graded on whether they achieve the goals as described in the lab assignment. You can think of each lab assignment as a directive given to you by your supervisor - if you meet those requirements, you are successful; however, if your system fails to meet those requirements, then it is not useful at all, even if it is partially complete. In practice, you may earn some partial credit for attempting a portion of a lab, but the majority of points will require full functionality.

Final Project: At the end of this course, you will design and evaluate a final project of your choosing to demonstrate your ability. This project can link back to your interest or other fields, and will serve as a capstone project for this course.

Grading

In theory, each student begins the course with an A. As you submit work, you can either maintain your A (for good work) or chip away at it (for less adequate or incomplete work). In practice, each student starts with 0 points in the gradebook and works upward toward a final point total earned out of the possible number of points. In this course, each assignment constitutes a portion of the final grade, as detailed below:

• 70% - Lab Assignments* (7 labs, 10% each lab)
• 10% - Quizzes (15 quizzes, 0.66% each)
• 10% - Discussions (5 discussions, 2% each)
• 10% - Final Project

All group work will include a REQUIRED peer evaluation component which can adjust that portion of the individual’s grade up to 50%. If a student should fail to contribute to a group assignment at all, their grade for that assignment will be reduced to a zero. Failure to complete the peer evaluation will result in a 10% grade deduction for that assignment.

Letter grades will be assigned following the standard scale:

• 90% - 100% → A
• 80% - 89.99% → B
• 70% - 79.99% → C
• 60% - 69.99% → D
• 00% - 59.99% → F

Submission, Regrading, and Early Grading Policy

As a rule, submissions in this course will not be graded until after they are due, even if submitted early. Students may resubmit assignments many times before the due date, and only the latest submission will be graded. For assignments submitted via GitHub release tag, only the tagged release that was submitted to Canvas will be graded, even if additional commits have been made. Students must create a new tagged release and resubmit that tag to have it graded for that assignment.

Once an assignment is graded, students are not allowed to resubmit the assignment for regrading or additional credit without special permission from the instructor to do so. In essence, students are expected to ensure their work is complete and meets the requirements before submission, not after feedback is given by the instructor during grading. However, students should use that feedback to improve future assignments and milestones.

For the project milestones, it is solely at the discretion of the instructor whether issues noted in the feedback for a milestone will result in grade deductions in a later milestones if they remain unresolved, though the instructor will strive to give students ample time to resolve issues before any additional grade deductions are made.

Likewise, students may ask questions of the instructor while working on the assignment and receive help, but the instructor will not perform a full code review nor give grading-level feedback until after the assignment is submitted and the due date has passed. Again, students are expected to be able to make their own judgments on the quality and completion of an assignment before submission.

That said, a student may email the instructor to request early grading on an assignment before the due date, in order to move ahead more quickly. The instructor’s receipt of that email will effectively mean that the assignment for that student is due immediately, and all limitations above will apply as if the assignment’s due date has now passed.

Collaboration Policy

In this course, all work submitted by a student should be created solely by the student without any outside assistance beyond the instructor and TA/GTAs. Students may seek outside help or tutoring regarding concepts presented in the course, but should not share or receive any answers, source code, program structure, or any other materials related to the course. Learning to debug problems is a vital skill, and students should strive to ask good questions and perform their own research instead of just sharing broken source code when asking for assistance.

That said, the field of system administration requires the use of lots of online documentation and reference materials, and the point of the class is to learn how to effectively use those resources instead of “reinventing the wheel from scratch” in each assignment. Whenever content in an assignment is taken from an outside source, this should be noted somewhere in the assignment.

Artificial Intelligence Usage Policy

This course uses a stoplight approach regarding the use of generative artificial intelligence (GenAI) tools, such as ChatGPT, Claude, Copilot, and others. Each assignment or group of assignments will be clearly labelled with one of three labels, indicating what level of GenAI usage is allowed.

Details

RED: GenAI Prohibited You may not use any GenAI tools to complete this assignment. This assignment's main goal is to develop your own skills related to a particular task or topic, or to assess your own understanding of the concepts and skills required for this course. GenAI tools are therefore prohibited for these assignments, as they do not reflect or enhance your own learning journey.

Policy Violations: Any usage of GenAI for this assignment will be treated as a violation of the K-State Honor Pledge and may result in a grade of 0 for the assignment and other sanctions approved through the K-State Honor Council.

Details

YELLOW: Limited GenAI Usage Allowed You may use GenAI tools in a limited way to complete this assignment. The assignment description may provide additional information about what tools are allowed and how they can be used. The goal of this assignment is to allow you to work with GenAI to complete a task or achieve a goal, but the completed work should still be a majority your own effort.

Citations Required: Any usage of GenAI must be noted and cited directly in the work, either in source code comments or text citations in written work. Citations should include the tool used, the prompt(s) given, context provided to the tool (e.g. existing code), and a discussion of how the results were used to complete the assignment.

No Direct AI Results: For this assignment, you may not include the GenAI results directly in your submission - it must be used to inform and adapted to fit your own work. For example, you may not prompt GenAI tools to just write your code and submit that directly; instead, you should ask for help performing specific tasks and then use the results within your own work.

Understand Your Work: To ensure compliance with this policy, the instructor reserves the right to request additional discussion or explanation of any work submitted by a student. The student should understand and be able to clearly explain all submitted work and code, even materials directly or indirectly produced by GenAI. A student who is unable to explain a submission to the satisfaction of the instructor may be considered to be in violation of this policy.

Policy Violations: Any usage of GenAI that involves direct submission of the GenAI outputs without additional work done by the student, or use of GenAI without proper citation, may be treated as a violation of the K-State Honor Pledge and may result in a grade of 0 for the assignment and other sanctions approved through the K-State Honor Council.

Details

GREEN: GenAI Encouraged You may make unlimited use of GenAI tools to complete this assignment. The goal of this assignment is to ensure you are comfortable with using GenAI tools to specifically meet a need or achieve a goal.

Citations Required: Any usage of GenAI must be noted and cited directly in the work, either in source code comments or text citations in written work. Citations should include the tool used, the prompt(s) given, context provided to the tool (e.g. existing code), and a discussion of how the results were used to complete the assignment.

Direct AI Results Allowed: For this assignment, you may include the GenAI results directly in your submission. It is still your responsibility to ensure the submission meets the assignment’s goals and is correct and factual - remember that GenAI is not infallible and may produce incorrect results. You are still solely responsible for ensuring the submission meets the stated assignment goals, and assignments in this category may receive additional scrutiny for correctness and accuracy.

Understand Your Work: To ensure compliance with this policy, the instructor reserves the right to request additional discussion or explanation of any work submitted by a student. The student should understand and be able to clearly explain all submitted work and code, even materials directly or indirectly produced by GenAI. A student who is unable to explain a submission to the satisfaction of the instructor may be considered to be in violation of this policy.

Policy Violations: Any usage of GenAI without proper citation may be treated as a violation of the K-State Honor Pledge and may result in a grade of 0 for the assignment and other sanctions approved through the K-State Honor Council.

Please contact the instructor if you have any questions about this GenAI policy. It is your responsibility to understand it and proactively ask questions if you are unsure; ignorance of this policy is not an excuse for violating it.

Artificial Intelligence Disclosure

In keeping with the expectation for transparency and citation regarding the use of generative artificial intelligence (GenAI), the instructors of this course will clearly denote any usage of GenAI tools in the process of teaching this class. Specific policies for the usage of GenAI by the instructors and TAs of this course are given below:

• RED: GenAI Prohibited Student Communication - GenAI will never be used to when communicating with students. We believe it is important for students to receive real, authentic communication from instructors and TAs.
• RED: GenAI Prohibited Grading - GenAI will never be used to suggest or assign grades for any student-submitted work. All grading decisions will be done soley by instructors and TAs.
• YELLOW: Limited GenAI Usage Allowed Code Review & Feedback - Instructors may use GenAI tools to assist with code reviews due to the scope and scale of these projects. However, all feedback given will be “human in the loop” with the final feedback coming directly from the instructors and TAs (see the first point above).
• YELLOW: Limited GenAI Usage Allowed Lesson & Learning Content - GenAI may be used in a limited way to construct lessons and learning content, such as homework scenarios or simple graphics. All usage of GenAI will be clearly marked and cited. (As of August 2026, no GenAI content exists in the course).

Late Work

Due to the asynchronous nature of this course, staying on task and keeping up with deadlines is very important. Therefore, all course work must be submitted, and all interactively graded materials must be graded with the instructor or TA, on or before the posted due date to receive full credit. For labs, it is not simply enough to contact the instructor/TA asking to schedule a grading time before the due date; the grading itself must be completed before the due date in order to be considered “on time”.

Any work submitted and graded after the due date is subject to a deduction of 10% of the total points possible on the assignment for each class day that the assignment is late. For example, if an assignment is due on a Friday and is submitted the following Tuesday, it will be subject to a reduction of 20% of the total points possible, or 10% for each class day it was late (Monday and Tuesday in this example). Grading done on non-class days will be considered to have been submitted on the next available class day. Deductions for non-class days will still be automatically entered by Canvas - contact the instructor to have these deductions removed.

These deductions will only be applied to grades above 50% of the total points on the assignment. So, if you scored higher than 50%, your grade will be reduced by the late penalty down to a minimum grade of 50%. If you scored lower than 50% on the assignment, no deductions will be applied.

Also, note that several labs in this class require successful completion of previous labs. If you are behind and choose to skip a lab assignment to catch up, you may still have to make up some or all of that work in order to complete a later lab. You may contact the instructor to discuss options for obtaining model solutions to previous labs if needed.

All course work must be submitted, and all interactively graded materials must be graded with the instructor, on or before the last day of the semester in which the student is enrolled in the course in order for it to be graded on time. No late work will be accepted after that date.

If you have extenuating circumstances, please discuss them with the instructor as soon as they arise so other arrangements can be made. If you know you have upcoming events that will prevent you from completing work in this course, you should contact the instructor ASAP and plan on working ahead before your event instead of catching up afterwards. If you find that you are getting behind in the class, you are encouraged to speak to the instructor for options to catch up quickly.

Incomplete Policy

Students should strive to complete this course in its entirety before the end of the semester in which they are enrolled. However, since retaking the course would be costly and repetitive for students, we would like to give students a chance to succeed with a little help rather than immediately fail students who are struggling.

If you are unable to complete the course in a timely manner, please contact the instructor to discuss an incomplete grade. Incomplete grades are given solely at the instructor’s discretion. See the official K-State Grading Policy for more information. In general, poor time management alone is not a sufficient reason for an incomplete grade.

Unless otherwise noted in writing on a signed Incomplete Agreement Form, the following stipulations apply to any incomplete grades given in this course:

1. Students who request an incomplete will have their final grade capped at a C.
2. Students will be given a maximum of 8 calendar weeks from the end of the enrolled semester to complete the course. It is expected that students have completed at least half of the course in order to qualify for an incomplete.
3. Students understand that access to instructor and TA assistance may be limited after the end of an academic semester due to holidays and other obligations.
4. For CC courses only:
   1. Students may receive at most two incompletes in Computational Core courses throughout their time in the program.
   2. Any modules in a future CC course which depend on incomplete work will not be accessible until the previous course is finished
      1. For example, if a student is given an incomplete in CC 210, then all modules in CC 310 will be inaccessible until CC 210 is complete

Recommended Texts & Supplies

To participate in this course, students must have access to a modern web browser and broadband internet connection. All course materials will be provided via Canvas. Modules may also contain links to external resources for additional information, such as programming language documentation.

The online textbook for this course can be found at https://textbooks.cs.ksu.edu/cis527/. All relevant pages from the textbook are also embedded into the appropriate Canvas modules.

Students in this course are expected to have access to a computer with virtual machine software (VMware, Virtual Box, Parallels, or other) installed and running. The computer should be capable of running multiple VMs simultaneously, which usually means having 8GB of RAM and a moderately powerful processor. Contact the instructor if you have questions or concerns.

All K-State Computer Science students have access to free software from Microsoft and VMWare. More information can be found on the K-State CS Support Website.

Since this class covers such a wide range of material, no single textbook will suffice. Therefore, students who would like a textbook should refer to resources available through the K-State Library and other online resources. The O’Riley For Higher Education digital library contains an entire catalog of books published on that platform, and it is a great resource for this course.

We will also use several online resources as needed.

This book contains useful information for anyone thinking about pursuing a career in system administration or information technology in general:

“The Practice of System and Network Administration” by Thomas Limoncelli, Christina Hogan and Strata Chalup.
ISBN 0321492668 - eBook Editions Available - Amazon Link

Subject to Change

The details in this syllabus are not set in stone. Due to the flexible nature of this class, adjustments may need to be made as the semester progresses, though they will be kept to a minimum. If any changes occur, the changes will be posted on the K-State Canvas page for this course and emailed to all students.

Standard Syllabus Statements

Netiquette

Online communication is inherently different than in-person communication. When speaking in person, many times we can take advantage of the context and body language of the person speaking to better understand what the speaker means, not just what is said. This information is not present when communicating online, so we must be much more careful about what we say and how we say it in order to get our meaning across.

Here are a few general rules to help us all communicate online in this course, especially while using tools such as Canvas or Discord:

• Use a clear and meaningful subject line to announce your topic. Subject lines such as “Question” or “Problem” are not helpful. Subjects such as “Logic Question in Project 5, Part 1 in Java” or “Unexpected Exception when Opening Text File in Python” give plenty of information about your topic.
• Use only one topic per message. If you have multiple topics, post multiple messages so each one can be discussed independently.
• Be thorough, concise, and to the point. Ideally, each message should be a page or less.
• Include exact error messages, code snippets, or screenshots, as well as any previous steps taken to fix the problem. It is much easier to solve a problem when the exact error message or screenshot is provided. If we know what you’ve tried so far, we can get to the root cause of the issue more quickly.
• Consider carefully what you write before you post it. Once a message is posted, it becomes part of the permanent record of the course and can easily be found by others.
• If you are lost, don’t know an answer, or don’t understand something, speak up! Email and Canvas both allow you to send a message privately to the instructors, so other students won’t see that you asked a question. Don’t be afraid to ask questions anytime, as you can choose to do so without any fear of being identified by your fellow students.
• Class discussions are confidential. Do not share information from the course with anyone outside of the course without explicit permission.
• Do not quote entire message chains; only include the relevant parts. When replying to a previous message, only quote the relevant lines in your response.
• Do not use all caps. It makes it look like you are shouting. Use appropriate text markup (bold, italics, etc.) to highlight a point if needed.
• No feigning surprise. If someone asks a question, saying things like “I can’t believe you don’t know that!” are not helpful, and only serve to make that person feel bad.
• No “well-actually’s.” If someone makes a statement that is not entirely correct, resist the urge to offer a “well, actually…” correction, especially if it is not relevant to the discussion. If you can help solve their problem, feel free to provide correct information, but don’t post a correction just for the sake of being correct.
• Do not correct someone’s grammar or spelling. Again, it is not helpful, and only serves to make that person feel bad. If there is a genuine mistake that may affect the meaning of the post, please contact the person privately or let the instructors know privately so it can be resolved.
• Avoid subtle -isms and microaggressions. Avoid comments that could make others feel uncomfortable based on their personal identity. See the syllabus section on Diversity and Inclusion above for more information on this topic. If a comment makes you uncomfortable, please contact the instructor.
• Avoid sarcasm, flaming, advertisements, lingo, trolling, doxxing, and other bad online habits. They have no place in an academic environment. Tasteful humor is fine, but sarcasm can be misunderstood.

As a participant in course discussions, you should also strive to honor the diversity of your classmates by adhering to the K-State Principles of Community.

SafeZone Ally

I am part of the SafeZone community network of trained K-State faculty/staff/students who are available to listen and support you. As a SafeZone Ally, I can help you connect with resources on campus to address problems you face that interfere with your academic success, particularly issues of sexual violence, hateful acts, or concerns faced by individuals due to sexual orientation/gender identity. My goal is to help you be successful and to maintain a safe and equitable campus.

Copyright Notice

© The materials in this online course fall under the protection of all intellectual property, copyright and trademark laws of the U.S. The digital materials included here come with the legal permissions and releases of the copyright holders. These course materials should be used for educational purposes only; the contents should not be distributed electronically or otherwise beyond the confines of this online course. The URLs listed here do not suggest endorsement of either the site owners or the contents found at the sites. Likewise, mentioned brands (products and services) do not suggest endorsement. Students own copyright to what they create.