Description: Computer science encompasses a wide range of topics and skills designing and building computer devices (e.g., your cell phone, laptop, cleaning robots), designing and building programming languages (e.g., Java, Python, C) and the supporting software that control these devices (e.g., operating systems, compilers), and the actual applications themselves (e.g., reading an x-ray, video animation, autonomous vehicles). This course focuses on how to write computer programs that solve problems using a computer and computations to determine the solution.

Programming is one of the key tools that computer scientists use to help themselves and others use to solve problems. This course uses a particular programming language, Python, to introduce you to how to use a computer to solve your problems. In this course, most of your learning will take place when you are putting in to practice the concepts from the book and lectures to solve more and more complex problems using more and more sophisticated programming practices. You will learn by doing. Performance on the programming assignments and performance on exams correlate closely.

Programming is a skill learned by experience, not just listening to lecture. To gain that experience I strongly encourage you to attend class to complete the in-class programming exercises and do as many extra problems as time allows. See the schedule for extra problems from the textbook.

Objectives: This is a first course in computer programming. The purposes of the course are to learn fundamental computer science concepts including algorithm development, problem decomposition, variables, computation, parameters, conditionals, iteration, Strings, lists, lists of lists, dictionaries, sets, and working with files. By the end of the course students are expected to be able to implement programs consisting of roughly one hundred lines of code employing non trivial algorithms and multiple functions

Estimates of the required effort to pass the class are:

• 3 - 5 hours per week of studying
• 2 - 10 hours per week of programming (less early in the course, more later)

Quantitative Reasoning Flag: This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.

Lectures: Monday, Wednesdays, and Thursdays. 11:30 am - 1:30 pm. BUR 112. Each lecture shall have an in-class programming exercise. 21 total. Completing these may help you get bumped to the next higher grade if you are close to a cutoff. (Details in the grading system described below.) Complete the specified readings before lecture. Topics and readings listed on the course schedule.

If you miss a lecture you are responsible for catching up on the material on your own. In-class programming exercises cannot be made up. Use the book, the slides, and the course discussion tool, Piazza, to ask question. If you fall behind, you will very likely not be able to catch up. Keep up!

I do not allow the use of laptops or any device with a raised screen in class. If you have a device that can lay flat on the desk such as a tablet or 2-1 computer you may use it. I strongly recommend you do not have your phone or other electronic devices out in class. I implore you to not use class time to e-mail, post to Instagram or Snapchat, etc. If you are addicted to your smartphone, laptop, or tablet, consider this class your social media free zone. Further, I guarantee you that you will not do well in the course if you are simply looking up long enough to type out what’s on the projector screen before you return to social media apps. The empirical research on this topic is clear: people are incapable of learning and retaining information when they are multi-tasking on their

Teaching Staff:

• Instructor, Mike Scott, email: scottm@cs.utexas.edu
  Help Hours: Monday, Wednesday, and Thursday 1:30 - 3:30  pm. Held in GDC 3.202, the public computer lab in GDC.  If you cannot make these hours email me to request an appointment.

• Teaching Assistants

TA	Email	TA Help Hours T = Tuesday, H = Thursday, All times are CDT.	Assigned Students based on Last Name
Mayra		M 9 - 11 ZOOM, W 9:30 - 11:30, H 9:30 - 11:30	A - Mad
Risha		T 1 - 3, W 4 - 5, H 4 - 5	Mae - Z

Required Materials:

1. Starting Out With Python, 5th Edition by Tony Gaddis, ISBN: 9780135929032, Note, the access code is NOT required. Just the textbook.
   Textbook homepage. Textbook Companion Site (VideoNotes May be Useful)
   

2. A computer capable of downloading and running Python and connecting to the web to access course materials and turn in assignments.

Class web site: http://www.cs.utexas.edu/~scottm/cs303e. Course materials and announcements are available there.

Startup: Most of the things you need to do to set up your infrastructure for the class are on the startup page.

 Class Discussion Tool: The class discussion group is on Ed, available via Canvas. The Ed discussion group is an official channel of communication for this course.

Canvas: We use Canvas and GradeScope to turn in programming assignments and post grades.

Email: All students shall be familiar with the University's official e-mail student notification policy. It is your responsibility to keep the University informed as to changes in your e-mail address. You are expected to check e-mail on a frequent and regular basis in order to stay current with University-related communications, recognizing that certain communications may be time-critical. It is recommended that e-mail be checked daily. The complete text of this policy and instructions for updating your e-mail address are available at http://tinyurl.com/pm6ej6e which includes instructions on how to update the email address you have on record with UT.

You are responsible for checking your e-mail and the class discussion group on Ed regularly for class work and announcements.

Software: Download and install Python 3.8.x on to your machine. Instructions at this page.

I also recommend downloading and installing the PyCharm Python, Educational Version, IDE (interactive development environment, an application that is a tool to help write computer programs). You may use whatever IDE you want, but many students have found PyCharm superior to IDLE, the simple IDE that comes with Python. The teaching staff may not be able to help with problems you have with IDEs besides PyCharm.

Schedule: A schedule of lecture topics, reading assignments, and assignment distribution and due dates is available online, via the class web page. The schedule page contains links to slides for the lectures, assignments, and online readings. Readings are to be completed before lecture. The exercises listed on the schedule are not turned in, but doing these will increase you chances of success in the class. The schedule is subject to change.

Grading: The class components used to determine your final average are:

• Grades are maintained on the University Canvas site.
   
• Programming assignments can count no more than 200 points towards your final average. Note, points for the programming assignments add up to 210 points. There are 10 points of "slack" in these non-exam components. This slack is to account for any non academic issues that may occur during the semester that cause you to miss one or more of these non-exam components. (Such as illness, family emergencies, hardware problems, etc.)
   
• The final letter grades will be assigned based on your total points out of 1000 points. The grade cutoffs are: <600 = F, 600 - 699 = D, 700 - 799 = C, 800 - 899 = B, >= 900 = A. Plusses and minuses (+ and -) will be assigned to scores within 25 points of the cut offs. So for example total points 875 to 899 earn a B+ and total points 900 to 924 earn an A-.
   
• Note, points are not added to your final total. The "slack points" are handled by basing your final average on 1000 points instead of 1010 points. There are 1010 points available in the course. Non-exam component points are capped at a maximum of 210. In other words, earning more than 210 points on the non-exam components will NOT result in points being added to your exam point total. This means Canvas underestimates your grade in many cases, but possibly overestimate it towards the end of the semester.
  
  
• Students may not do any form of extra work to improve their grade. The only opportunities for extra credit in this course are:
  • 10 points: completing the background survey on Canvas by 11 pm on Thursday, June 13.
  • 10 points for completing the end of term, Course Instructor Survey (CIS) (available at the end of the semester)
These 20 points are added to your non exam point total. They are essentially extra slack points.
 
• Your final points out of 1000 is calculated as follows:
  
  min(200, sum of points on programs + background survey completion + end of course survey) + midterm exam score + final exam score
  
• Each lecture shall have an in-class programming exercise. 21 total. Completing these may help you get bumped to the next higher grade if you are close to a cutoff.
  
  For every two in class exercise you make a reasonable attempt on one point will be added to your final point total out of 1000. If you complete an odd number the additional points will be rounded up. Examples: You complete 18 in class exercises. 9 points are added to your final point total out of 1000. You complete 13 in class exercises. 7 points (6.5 round up to 7) are added to your final point total out of 1000.
  
  
• Depending on the results of an exam, exam scores may be adjusted. No other class components (programming assignments) are curved.
   
• You have 8 slip days (max of 1 per assignment) to use through out the term on your assignments if you are not able to turn an assignment in on time. See the assignments page for details of the requirements for turning in assignments and how slip days work. Slip days are to account for life circumstances (My hard drive crashed!! I got a virus downloading a BitTorrent file!! My CPU caught fire while I was mining crypto currencies!) and emergencies. Do not use your slip days frivolously. If you use up your slip days, late assignments shall receive a score of 0. Other than slip days, late programming assignments are not accepted for any reason. Assignments must be turned in via Canvas and shall NOT accepted via email.
   
• The required format and procedures for turning in assignments are available on the assignments page.  Assignments that are not turned in to the correct directory in the correct format will cause you to, lose points, and/or receive a 0 on the assignment.
   
• If a student misses the midterm exam with a verifiable excuse, then the final exam shall count for the missed exam. Contact the Mike if you miss an exam. The reason for missing the exam must be verified through student emergency services.
   
• If you believe we have made an error in grading an assignment, you must submit your complaint via email, along with supporting evidence, to your grader within 5 days of the date the teaching staff graded the assignment. TAs will post to Ed when assignment grading is complete for their section. Do not bicker or quibble with the TAs regarding assignment grading. Your grade is NOT the starting point of negotiations with your TA. Consider your TA your manager at software development job. Use their feedback to improve on the NEXT program.
  
  
• Questions about your grade are to be sent to the TA assigned to your section. See the table above for TAs and assigned students.
  

Guiding Principle - No whining: Feedback and concerns about the course are always welcome; legitimate grading errors that are identified in a timely fashion will certainly be corrected, but whining is counter-productive and will only irritate those who evaluate your work to determine grades. Take the feedback you receive on programming assignments, especially the feedback on program hygiene (how the code is written and if it follows the Python style guidelines) and improve on the following assignments.  Realize if you ask for a regrade "because it can't hurt to ask" your score may actually go down if we find more errors and problems.

Please, when something bad happens, or when you’re feeling overwhelmed, get help. Don’t endure it on your own. Even talking through the situation often helps. Here are some options:

• See me. Come by help hours, or book an appointment (just email me).

• Talk to Student Emergency Services. They are here to help you with all kinds of life’s troubles (family, housing, health, money, stress, etc.) whether it’s a crisis or not.
  Tel: (512) 471-5017 E-mail: studentemergency@austin.utexas.edu

University Code of Conduct:

The core values of the University of Texas at Austin are learning, discovery, freedom, leadership, individual opportunity, and responsibility. Each member of the University is expected to uphold these values through integrity, honesty, trust, fairness, and respect toward peers and community.

Academic Honesty: Taken from the CS department Code of Conduct.

"The University and the Department are committed to preserving the reputation of your degree. It means a lot to you. In order to guarantee that every degree means what it says it means, we must enforce a strict policy that guarantees that the work that you turn in is your own and that the grades you receive measure your personal achievements in your classes:

Every piece of work that you turn in with your name on it must be yours and yours alone unless explicitly allowed by an instructor in a particular class. Specifically, unless otherwise authorized by an instructor:

• Students may not discuss their work with anyone except the instructor and other members of the instructional staff (instructor, TA, lab proctor or partner on a pair assignment).
• Students may not acquire from any source (e.g., another student, an internet site, LLM such as chatGPT) a partial or complete solution to a problem or project that has been assigned.

You are responsible for complying with this policy in two ways:

1. You must not turn in work that is not yours, except as expressly permitted by the instructor of each course.
2. You must not enable someone else to turn in work that is not theirs. Do not share your work with anyone else. Make sure that you adequately protect all your files. Even after you have finished a class, do not share your work or published answers with the students who come after you. They need to do their work on their own. This means do not post your solution code to any public web site such as public repositories on GitHub. Do not post your work to the web even after you have completed CS303e.

The penalty for academic dishonesty will be a course grade of F and a referral of the case to the Dean of Students. Further penalties, including suspension or expulsion from the university may be imposed by that office.

One final word: This policy is not intended to discourage students from learning from each other, nor is it unmindful of the fact that most significant work in computer science and in the computing industry is done by teams of people working together. But, because of our need to assign individual grades, we are forced to impose an otherwise artificial requirement for individual work. In some classes, it is possible to allow and even encourage collaboration in ways that do not interfere with the instructor's ability to assign grades. In these cases, your instructor will make clear to you exactly what kinds of collaboration are allowed for that class."

For CS303e the policy on collaboration is modified as follows:

You are encouraged to study for tests together, to discuss at a very high level methods for solving the assignments, to help each other in using the software, and to discuss methods for debugging code.

You are committing academic dishonesty if you look at someone else code (current students, past students and code from the web) in electronic or discuss the code in at such a detailed level that solutions turn out essentially the same. You are committing academic dishonesty if use use a large language model / generative AI such as chatGPT to generate any of your code. You shall not ask anyone to give you a copy of their code or, conversely, give your code to another student who asks you for it.

Similarly, you shall not discuss your algorithmic strategies at such a detailed level that you and your collaborators end up turning in essentially the same code. Discuss very high level approaches together, but do the coding on your own. Realize with complex problems, two programs that have produce the same results given the same input will vary significantly in approach and structure. You are making many, hundreds, of micro decisions as you design and implement your programs. It is extraordinarily unlikely two people working on the same complex problem will produce the same solution.

Examples of cheating are many and include accessing another student's account, using a large language model / generative AI such as chatGPT to generate any of the code you turn in, looking at someone else's solution code, copying or downloading someone else's solution code on the web or other sources, referring to solutions from previous semesters, having another student walk you through the solution and how to code it, discussing the problem at such a detailed that you are essentially coding together, having another student perform significant debugging of your code, having another student write your code for you and / or allowing others to copy of access your solution code.  This means you shall not look on the internet for code to solve your problems. This list is not all inclusive.

Examples of allowable collaboration include discussions and debate of general concepts and solution strategies and help with syntax errors.

You can reuse code from lecture, help hours, and the class textbook with proper credit given. Use a comment to note code and the source (lecture, help hours, or class textbook).

If you have any doubts about what is allowed ask the instructor.

Plagiarism detection software will be used on assignments to find students who have copied code from one another. 

The typical penalty for committing academic dishonesty on a programming assignment is a grade of zero on the assignment AND a one full letter grade reduction in the student's final course grade.

For more information on Scholastic Dishonesty see the University Policy on Scholastic Dishonesty

Religious Holidays: By UT Austin policy, you must notify "as far in advance of the absence as possible so that arrangements can be made."  Please email me at least fourteen (14) days prior to the date of observance of a religious holy day so I have time to set up accommodations. If you must miss a class, an examination, a work assignment, or a project in order to observe a religious holy day, you will be given an opportunity to complete the missed work within a reasonable time after the absence.

Disabilities and Access: Students with a documented disability may request appropriate academic accommodations from the Division of Diversity and Community Engagement, Services for Students with Disabilities, 512-471-6259 (voice) or 1-866-329-3986 (video phone). https://diversity.utexas.edu/disability/about/
• Please request a meeting as soon as possible to discuss any accommodations
• Please notify me as soon as possible if the material being presented in class is not accessible
• Please notify me if any of the physical spaces are difficult for you to use and / or navigate