Overview

• Introduction and Learning Outcomes
• Course Evaluation and Promotion Policy
• Format of the Course: Online Lectures and Tutorials
• Lab Submission and Quizzes
• Assignments
• Tests
• Course Policies: Academic Integrity, Missing Quizzes, etc.

Faculty Information

Prof. Eric Brauer

Web:

https://ericbrauer.github.io

Email:

eric.brauer@senecapolytechnic.ca

Introduction

This is an introductory programming course designed specifically for system and network administrators.

Students will learn generally-applicable programming concepts and techniques using the Python programming language.

Following an introduction to fundamental programming principles the students will write software to automate deployment and configuration tasks over a network.

Learning Outcomes

• Design algorithms to solve simple problems which require input/output, conditions, and loops.
• Design and implement functions in order to avoid code duplication, while avoiding the use of global variables.
• Read and write data from/to plain-text and binary files.
• Write code that handles expected and unexpectederrors during execution.
• Use a programming languagedebugger to speed up locating errors in the code.

Learning Outcomes II

• Apply knowledge of established and new development tools to write deployable code efficiently.
• Automate deployment and configuration tasks using a scripting language with a configuration management tool.
• Assess, select, and use appropriate tools and techniques to develop and maintain administrative scripts and task automation.
• Provide clear and accurate documentation and comments in the source code.

Course Evaluation

Labs and quizzes: 20%

Labs(8): 10%

Quizzes (4): 10%

Assignments (2): 30%

Midterm Assessment: 20%

Final Assessment: 30%

Promotion Requirements

You must:

• Achieve a weighted average of 50% or better on the two tests.
• Satisfactory complete all assignments. Not Labs!
• Achieve a grade of 50% or better on the overall course.

Class Format

In your schedule, you will see 2 classes of 2 hours each. Normally, we would have 1 lab period and 1 lecture period. Our lectures will be video lectures, available over Zoom. The second scheduled class will be our tutorial. This will have more examples, walkthroughs, and I will be online during that time for questions, help and support, etc. I will record lectures for those who miss the scheduled classes.

Tutorial

It’s a good time to be working on labs and assignments. I will present examples/walkthroughs, and practice questions that will aid you in understanding lab/assignment concepts. Afterwards will be lab time. If you are working on labs during this time, you can get help from me very quickly.

Online Lectures

The objective of online video lectures is to accommodate people who may be taking the course in different time-zones. Videos give students the most flexibility while still delivering the same content. The video lectures will cover concepts from the lab in a more comprehensive format. (That is, we take a look at the overall concept rather than rehashing individual bits of code). Prepare for the class by reading/reviewing assigned readings, which include text, lab instruction, and slides before class.

Assigned Readings

There are two books for this course:

• Think Python 2nd Edition
• Automate the Boring Stuff with Python

Electronic copies of both can be accessed on Blackboard and available online as well.

Check the weekly schedule to find out the chapters from each book as assigned reading. Reading those chapters before each week’s lecture will help you understand the lectures and to complete the labs.

Lab Environment Setup

• Requirement for the course: a Linux development environment with git and Python 3.11.
• I recommend creating a VM where you can install a code editor like VS Code.
• Instructions can be found on the Wiki.

GitHub Setup

• Create a GitHub account
• Generate a public/private keypair for each computer you will use for labs
• Copy your public key and paste it into your GitHub:
• Click on profile -> Settings -> GPG & SSH Keys

Lab Submission

• Click on the lab from Blackboard, and accept it.
• Clone the repository to your computer using SSH:
• Copy to your clipboard

Lab Submission

• Run git clone from your terminal and paste the link from GitHub.
• Follow Wiki instructions to create your files.
• Run the Lab Check script for each lab to make sure that you complete all the required tasks.
• Labs are submitted on GitHub. Follow instructions to push your code back into the repository.

Practical Quizzes

• The labs will also prepare you for the practical quizzes. These quizzes can be considered “open book.” You may use class notes, and the Python interpreter.
• However sharing answers is not permitted.
• Quizzes will be done online on GitHub Codespaces.
• Two types of questions may appear in each quizzes:
  • Coding Questions: You will be asked to write or debug and fix a script based on given details. You are allowed to use your VM to work out the answer to this type of questions.
  • Decoding Questions: You will be given a piece of Python code and you have to figure out what it does. You will be asked a few questions to test your understanding of the given piece of Python code. For example: if you run the python code with a command line argument “ABCD”, what will be the expected output?
• The exact date for each quizzes will be announced at least one week before the scheduled date.

Assignments

• Assignments are based off concepts and lessons learned from the lectures, the labs, and the weekly assigned reading.
• Don’t be afraid to ask your professor if you have any questions about the assignments.
• There are two mandatory assignments, 15% each.
• Files will be provided over GitHub, and GitHub will be used for evaluation.
• Assignments must use code that is within scope for the course material. Code that is outside the scope of what you learned will be given a grade of zero.
• I may ask you to undergo a code review. If you fail (or fail to attend) code review, you will only be able to score a maximum of 50%.

Creating Backups!

• You are responsible for backing up your work in this class! This includes your
• VM, and any lab work, python scripts, and assignments you’ve completed.
• Fortunately, this is what GitHub is for.
• Commit your work after every significant change!!

Course Policies

• Late Policy
• Missed Tests/Quizzes Policy
• Academic Integrity Policy
• Information Technology Acceptable Use Policy

Getting Help

Having problems? No problem, that’s normal!

• Contact Me after lecture (email, MS Teams)
• Leave a question on the appropriate MS Teams channel
• Bring it up during tutorial! This would be a great time to explain the concept!

Getting Help

When you ask your question, include:

• What you’re trying to do (not just “lab 3”)
• What result you’re expecting
• What result you’re getting (including error messages)
• What you’ve already tried to fix your problem.

Include your error message!

“How do I get better at programming?”

• learn by doing!
• Don’t just watch examples, copy them.
• Take an example, and modify it. See what happens.
• Later on, take a tedious/boring task and automate it.
• (Refer to Automate the Boring Stuff for ideas!)

Lab Marks

To receive full marks for labs, they must be completed and submitted by their due dates as posted on Blackboard (excepting the first two).

Your labs have a Check script.

• If all tests pass, you receive full marks.
• If 50% - 99% of tests pass, you get 50% of marks.
• If less than 50% of tests pass, the lab is not yet complete.

Late Labs

• Lab files can be submitted late for a maximum grade of 50%, but all tests must pass.
• If late labs are not 100% complete, they will get 0.

Late Assignments

Milestones

• On the deadline of a milestone, I will look at code that you’ve committed on GitHub.
• If you didn’t commit any code, you get zero.
• On the next milestone, if you got caught up, you can get 50% of marks for the late milestone.

Assignment Final Submissions

• To receive full marks for assignments, they must be completed and submitted by their due dates as posted on the web site.
• Late assignments are subject to 10% late penalty per school day. Milestones must be submitted on time.
• Both assignments must be completed satisfactory, even if it is overdue and worth zero marks, to get the credit for this course.

Quizzes

• There are five quizzes this semester.

• I will be dropping the lowest quiz mark.

• The rest of the quizzes are worth 2.5%.

• There are no makeups or second chances on quizzes, so don’t bother emailing me.

• If you miss one quiz, not to worry, it won’t affect your grade. But you’ll have to be sure to write the next 4.

Missed Tests

If you are going to miss a scheduled test, Email me before the assessment is scheduled to begin.

It doesn’t have to be a long email, just let me know what’s going on.

Provide valid supporting documentation before alternative arrangement will be considered. Tests will require a make-up.

Academic Integrity Policy

This college (and course) have a zero tolerance policy towards plaigiarism, infringement of any kind. The code you submit needs to be your own.

Please refer to the following web page if you’re at all uncertain.

• Do not use or share completed code for labs, assignments, or quizzes.
• If you want to share example to help a classmate, make sure that it is not directly from a lab or assignment.
• Change what it does/how it works.
• Sharing a line of code if okay. Do not share your files.
• If you are using Github, your repository must be private.

Regarding LLMs Such As ChatGPT

Regarding LLMs

• LLMs are a fine resource for experienced devs, because we can evaluate and troubleshoot the solutions that ChatGPT provides.
• Before you can evaluate and troubleshoot these solutions, you need to practice solving easier problems.
• This course provides you with this practice, but the problems we train you on are easily solved with LLMs. In my experience, practice problems are the only types of problems that LLMs excel at. (Real life is messy).

And So

I will ask that you not use ChatGPT or similar LLMs during the course. Try to solve the problems with your debugger and brain. The process is the point!

• LLM solutions are ineligible for points in the assignments and tests. You will get zero.
• Code that is out of scope will be given a grade of zero.
• LLMs are not permitted in technical job interviews. If you can’t do it honestly, you will lose opportunities.
• Sharing proprietary code with ChatGPT will get you fired.
• Relying on ChatGPT means you will always be reliant on it, even if it goes away!

Ready? Set, Go…

• Complete the quiz on Course Expectations
• Get comfortable with Blackboard and bookmark the Wiki
• Create a GitHub account and get started on Lab 1
• Set up your development environment

Assigned Reading for Next Class

Think Python: Chapter 1 Automate the Boring Stuff with Python: Chapter 1 and 2