CS 303E: Spring, 2025
Elements of Computers and Programming

Instructor: Dr. Bill Young

Unique numbers: 50560, 50565, 50570
Class time: Hybrid (class meetings F 9am, 10am, 11am); Location: JGB 2.324
This website: www.cs.utexas.edu/users/byoung/cs303e/syllabus303e.html
Instructor Office: GDC 7.810; Phone: 512-471-9782; Email: byoung at cs.utexas.edu
Instructor Office Hours: TBA (GDC 7.810) and by appointment
TAs: Find TA office hours on the class Canvas page.
Greyson Baker: akagbaker at utexas.edu
Dewayne Benson: dewayne.benson at utexas.edu
Ayan Gupta: ayangupta at utexas.edu
Parul Gupta: parulg428 at utexas.edu
Ishita Jain: ishitajain at utexas.edu
Devvrit Khatri: devvrit at cs.utexas.edu
Sairaja Kurelli: saikurelli at utexas.edu
Abhiram Naredla: abhiramnaredla at utexas.edu
Jeremy Nguyen: jn28548 at utexas.edu
Anya Ranavat: anyaranavat at gmail.com
Shyamli Channabasappa: shyamli.channa at utexas.edu
Vidhi Sapru: vidhisapru at utexas.edu



Class slides, videos, assignments, and other information may be linked on Canvas or Ed. They will always be linked on this webpage. So use this page as your first place to look for class information.

Down the page are:

Important Class Announcements:

Breaking news important to the class will be posted here. Consult this spot often.

I've been asked to share the following: STEM Muse flyer.

HW -1: carefully read this syllabus and this additional document: How to Succeed in CS303E. Yes, I know these are a bit long, but they'll answer in advance most of the questions that typically come up about this class. They are fair game for questions on a quiz!

Week-1 (week of 1/13): Make sure you've done HW -1. Read Lecture 0: Why Computing Matters (there is no associated video). View the videos for Lecture 1: What is Python. Also, attempt weekly homework 0: HW0. You won't turn in the homework, but it will get you started in using Python, so do it. If you encounter problems, ask questions on Ed. Note that you also have HW1 due on Wednesday of next week. So you probably want to get started on the Week-2 material.

Here's a short video I made that may help you with HW1: how to approach HW1. BTW: Occasionally over the course of the semester, I will make these Zoom videos showing the development of a program. I usually do this from the Zoom linked on Canvas and for some reason this shows up as a meeting on Canvas. Please don't join!

Week-2 (week of 1/20): view the videos for Lecture 2: Simple Python. Do weekly homework 1: HW1 (due 1/22). Note that most homeworks will eventually be due on Fridays but not the first few.

Dr. Young's office is in the south wing of GDC. You have to take the south elevator, because the two towers don't connect on the 7th floor.

Feel free to email me at byoung at cs.utexas.edu or click this link: (Send me an email message). Please don't send me emails via Canvas.


About this Course:

CS303E is the first course in the Programming and Computation series (previously the Elements of Computing series) for non-CS majors. Computing is an integral part of many disciplines. This is especially true of STEM fields, but being able to think computationally and write programs is useful across the board. This course will introduce basics of programming within the context of a popular and powerful programming langugage, Python. We will study the syntax and special features of Python, develop our own algorithms, and translate them to computer code. We will learn problem solving techniques for a wide variety of problems amenable to computer solution. No prior programming experience is required or assumed. Despite the name, this is almost entirely a programming class; we won't be covering the structure of computers.

Students in this class come from a wide variety of majors and backgrounds. If you have previous significant programming experience in high school classes, other college classes, or on your own, you may get bored in this class. Consider taking the available exam to test-out of this course and begin with CS313E instead. You can find information on testing out of the class here: Testing Out. On the other hand, beginning students without much programming experience are often dismayed to find that this class is rather challenging. If you're expecting a class where you don't have to work, this isn't the class for you.

Here's some advice on how to succeed in this class: How to Succeed in CS303E. I strongly suggest that you read this. It's a bit long, but contains a lot of useful information, and will likely answer most of the questions you'll have about this class, and some you might not think to ask.

This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with the skills necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You can find a description of flags here: Flag Criteria. You can expect that a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems. We don't assume that you're a math major, but some problems may assume basic arithmetic and logic skills; if you don't know something, just ask, and we'll provide additional explanation.

During the height of COVID-19, this class was moved entirely online. I found that it worked very well in that format, and I kept it that way, until this semester. Most students love the flexibility, but others don't do as well in this format. Also, since there's less one-on-one interaction, it's not always easy for us to see that you're having trouble. That's why I added a mandatory in person meeting on Fridays this semester. That allows me to get a better feel of how that class is doing and also gives you the opportunity to ask questions and to see the development of programs in real time. But it's a big class; I still may not realize that you're struggling. If you are, please reach out for help. Don't wait until you are hopelessly behind. We can't help you if we don't know you're struggling.

The only specific class meeting time is on Friday. Most course content is delivered online via the recorded lectures, which you can view at your convenience, as long as you've viewed them by the week for which they're assigned. Videos and the accompanying slides will typically be made available a week or two before they are due. Make sure to keep up. The recorded lectures and associated slides will be made available to you below on this website. Some of them may also be available via Canvas or elsewhere; but this website should be your go-to location. Despite the largely asynchronous nature of this class, this is not a self-paced course. You are responsible for having viewed the videos the week they are assigned.

If you have some special circumstance that makes internet access difficult or impossible, let me know as soon as possible and I can work with you.

Class Schedule:

The following link is to a schedule that is my best estimate of everything you'll be responsible for this semester: schedule. Homeworks and projects will be posted on this webpage; and may also be posted on Ed and/or Canvas. Turn in homeworks and projects on Canvas. We'll be providing information on how to take quizzes. Each quiz will count the same as a weekly homework. All readings are from Liang; you are encouraged to do them, but will not be tested on anything solely from the textbook. View Liang as a supplementary source of information if you can't understand something from the videos.

Questions about Grading:

Weekly homeworks, projects, and quizzes are graded by the TAs. Exams are graded collectively by all of the TAs and the instructor. Each TA grades for a specific alphabetic range of students' last names. You can find your TA in the chart soon to be posted below. You will establish a connection to this specific TA; but don't hesitate to attend other TAs' office hours as well. If you have questions about the grading on homeworks, projects and quizzes, please contact "your" TA. In general, Dr. Young won't have graded your work and won't know why you lost specific points. The TAs have been asked to be understanding and flexible regarding grading issues; but in general, Dr. Young won't override their grading decisions unless the decision was clearly unfair. (If you send him a question about your grade, he'll almost certainly will refer you to your TA. Please don't interpret that as rudeness or being uninterested in your situation. He just literally won't know why you got the grade you did, because it was your TA who assigned it.)

If you have a personal emergency and need additional time on an assignment, contact your TA as soon as possible. Again, the TAs have been asked to be lenient and understanding, but don't abuse this.

Some of the TAs hold their office hours on Zoom (or equivalent) and others in person. You can access Zoom via the Zoom link on the class Canvas page. We will also communicate via email or, preferably, Ed. The schedule of the TAs office hours will be available via Canvas. Please don't send email via Canvas.

Using Ed Discussion:

We will be using Ed Discussion for much of our class communication. You should be enrolled automatically in the class Ed feed; if you're not, let Dr. Young know as soon as possible. The Ed system is great at getting you help quickly and efficiently from classmates, TAs, proctors, and the instructor. Rather than emailing questions to the teaching staff, you are strongly encouraged to post your questions on Ed. However, don't post code and other items on Ed that give away solutions to homework or projects, unless you post them privately (visible only to yourself and the instructors.)

Please use the same email on Ed, Canvas, GradeScope, and elsewhere in the class. Otherwise, we may not be able to figure out who you are and record your grades correctly.

If you turn off Ed notifications and miss an important posting, you are responsible. Yes, there's a lot of traffic; you can customize your Ed feed to only send updates periodically, but don't turn it off entirely. You can make your posts anonymous to your classmates, but not to the instructors. Posts must be pertinent and respectful. Don't use Ed as a place to vent or trash anyone. Please don't waste everyone's time posting jokes and other fluff. There will be around 600 people posting, so even a small percentage of junk is too much.

Using Canvas:

Canvas is a learning management system used in most classes on campus. You will submit most assignments on Canvas and that's also where your assignment, quiz and test grades will be posted. You should be enrolled automatically in Canvas for the class; if you're not, let us know ASAP. It is your responsibility to check grades on Canvas and verify their correctness. If you think there is an issue or omission, call it to our attention immediately. A week after they are posted, we'll assume that the grades are OK.

The running averages on Canvas definitely will not be correct, and may confuse you. Don't rely on them. It is rather difficult for us to get Canvas to compute your course grade correctly, since we might be dropping some things, normalizing scores, giving extra credit, etc. The raw scores on individual assignments, quizzes and tests should be correct. If they are not let us know immediately. But ignore the running averages. Information on how to compute your class average for yourself is given below.

Information regarding tests and quizzes likely will come to you via Canvas mail and via Ed. That's why it's a very bad idea to turn off notifications for Canvas mail (or Ed notifications) because you may miss important announcements. Also, make sure that the email associated with you on Canvas, Ed, and GradeScope is actually an email that you check regularly. And remember: important information will always be linked from this webpage. This page should be your go-to location for information.

Some of you go by names that are quite different from your official name, i.e., the name on the class roll that I see on the UT registrar's page. This can cause problems at the end of the semester because I may not be able to match the grades on Canvas with the individual for whom I have to submit a semester grade. If your name is William and you go by Bill (as I do), we'll be able to figure that out. But if your name is Susie Jones and you go by Sammy Smith, or if you change names over the course of the semester, that may cause an issue. (This is very common for foreign students who use an English name.) Please let me know if your name you're using in this class is substantially different than the name by which you are registered with the university.

Please don't send me emails via Canvas. Instead, email me directly at byoung at cs.utexas.edu. The reason for this is that Canvas doesn't show the "thread" of the email; so if your message is part of a conversation, I may not be able to reconstruct the context. When I get a Canvas message that says "I agree" or "What did you mean by that?" I don't want to have to spend an hour trying to figure out what the heck you're responding to. Remember that you're one of around 600 students in the class.

Text:

The text book for this class is Introduction to Programming Using Python by Y. Daniel Liang, Pearson, 2012 or 2013. I view the book as a useful additional resource to help you master the materials. But you will not be responsible for anything that is only in the book. Just between us: the book is listed as required rather than optional only because they wouldn't include an optional book in the Longhorn Textbook Access program (see below).

There are a few errors in the book. Here's an errata sheet: Liang Errata.

Notice that this book is ancient in computing years, but also pretty good. You should be able to get it from the Coop or buy it on Amazon or online (see below). Note that you do not need the MyProgrammingLab materials that may or may not come with your textbook. We won't be using that.

A digital version of the textbook is available through the Longhorn Textbook Access (LTA) program, an initiative between UT Austin and the University Co-op to reduce the cost of course materials for students. You can access it through the "My Textbooks" tab in Canvas. You are automatically opted into the program but can easily opt-out (and back in) via Canvas through the 12th class day. If you remain opted-in at the end of the 12th class day you will receive a bill through your "What I Owe" page and have until the end of the 18th class day to pay and retain access. If you do not pay by the 18th class day, you will lose access to the materials after the 20th class day and your charge will be removed. More information about the LTA program is available at LTA Program

Class Lecture Videos and Accompanying Slides:

All of the class lecture videos and accompanying slides will be made available via links below as we cover new material. Note that I tinker with the slides as I find typos or find ways to explain things better. So some slides may differ in small ways from what you see on the videos. That's nothing to worry about. You'll see a date on each slideset that shows the last time updated; but the changes are typically pretty minor.

Class Recordings: Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to a Student Misconduct proceedings.

Lecture 0: Consider Computing 4up-PDF PDF
Note that I didn't record a video for Lecture 0. Please just read through the slides.

Lecture 1: What is Python 4up-PDF PDF
Video1.1 (16 minutes).
Video1.2 (26 minutes).
Video1.3 (13 minutes).

Lecture 2: Simple Python 4up-PDF PDF
Video2.1 (29 minutes).
Video2.2 (31 minutes).

Assignments:

The only way to learn a programming language is to write programs. Shorter programming homeworks will be assigned nearly every week. You will also have three much more substantive programming projects assigned over the course of the semester. All assignments/projects are due at the end of the due day (11:59pm). Answers must be submitted on Canvas in the form of a Python code file. You can turn in weekly homeworks and projects up to two days late with a penalty of 10% per day. They generally won't be accepted after that; but check with your TA if you have some personal emergency.

All assignments must be your own work; do not do team coding, share code or allow others to see your code, or use an automated assistant such as ChatGPT. You can always get help from the instructor or TAs; but make sure you always do your own work. We take cheating very seriously and have very sophisticated tools to detect cheating or collusion.

By the way, most of the work in writing a program is in the design. So if you and a friend were to write pseudocode together and each then individually code from that, our tools might still flag that as cheating. You're much better off doing your work completely on your own. It's much safer to get help from the TAs or instructor than from a friend.

There will be weeks during the semester where you have an weekly homework due and also a weekly quiz, exam or project due. That's just the way it is. Plan ahead! If you wait until the last day to study or work on a project, you have no one to blame but yourself.

If you submit an assignment multiple times, Canvas renames your file from Filename.py to Filename-1.py, then Filename-2.py, etc. Don't worry about that; we always grade the latest version.

The assignments are designed to build your skills methodically in the use of particular aspects of Python programming. Later in the semester you will learn Python features that would have made some of the earlier assignments quite a bit easier. Some of you have previous programming experience and may know about these features. But don't use constructs on assignments, quizzes, or exams that we haven't covered in class yet. You will lose points! If you have questions about what you can use, just ask (preferably on Ed so everyone will see the answer).

Weekly Homeworks:

Links to weekly homeworks and projects will appear here and recent ones will also appear in the Important Class Announcements at the top of this page. Homeworks are always due by 11:59pm on the due date.

All videos and the associated slidesets are linked above on this page. Readings from Liang are optional; the suggested readings are indicated on the schedule above. BTW: I put copyright marks on everything so that if I find it on Chegg or similar sites, I can insist that it be removed.

HW -1: carefully read this syllabus and this additional document: How to Succeed in CS303E. Yes, I know these are a bit long, but they'll answer in advance most of the questions that typically come up about this class. They are fair game for questions on a quiz!

Week-1 (week of 1/13): Make sure you've done HW -1. Read Lecture 0: Why Computing Matters (there is no associated video). View the videos for Lecture 1: What is Python. Also, attempt weekly homework 0: HW0. You won't turn in the homework, but it will get you started in using Python, so do it. If you encounter problems, ask questions on Ed. Note that you also have HW1 due on Wednesday of next week. So you probably want to get started on the Week-2 material.

Here's a short video I made that may help you with HW1: how to approach HW1. BTW: Occasionally over the course of the semester, I will make these Zoom videos showing the development of a program. I usually do this from the Zoom linked on Canvas and for some reason this shows up as a meeting on Canvas. Please don't join!

Week-2 (week of 9/2): view the videos for Lecture 2: Simple Python. Do weekly homework 1: HW1 (due 9/4). Note that most homeworks will eventually be due on Fridays but not the first few.

Weekly Worksheets and Practice Problems:

TA Dewayne Benson has put together some worksheets that will provide additional practice. The good thing about these is that they ask some questions similar to what you'll encounter on the exams. This is unlike the quizzes, which strictly cover programming questions. It is suggested that you try these worksheets as we post them. They won't be collected, but feel free to ask questions on Ed about any items on which you're having problems.

There is no worksheet for Week1.

Week2 Worksheet.
Week2 answers

We'll also post practice problems on HackerRank or CodingBat for each week. These also will not be collected, but they provide excellent practice related to the material for the week. It is suggested to do as many of these as you have time for. We won't post solutions, but you are welcome to ask questions on Ed.

Week2 Practice Problems

Exams and Quizzes:

There will be three exams this semester. Two are in-class exams of about 1 hour each. There will also be a final exam of 2 hours during finals week. All exams are cumulative. See the schedule above for dates. For the final, there will be a makeup scheduled. You must have a verifiable excuse, such as a conflict with another exam, to take the makeup. The makeup will be scheduled a day or two after the regular exam. Generally, it will not be possible to take an exam early. So don't plan to leave town before the final exam.

Quizzes: There will also be several quizzes over the course of the semester. In a non-hybrid class, these would be pop quizzes. But in our hybrid format, we'll announce them well in advance and you'll have several different times during the day when you can take it (e.g., 8am, noon, 4pm, 8pm). They probably will be given on the online platform GradeScope, and will consist of programming problems. Each will be a different version and they will be autograded. You'll have an opportunity to practice with the platform before the first quiz that counts.

If you miss a quiz, you won't be able to take a makeup. We can't reschedule a quiz even if you have an excellent excuse for not taking it. However, each quiz will count the same as one weekly homework; i.e., it's not a large portion of your grade, so don't freak out if you have to miss one.

Do not take any quiz more than once on penalty of a 0 on the quiz. If you have serious issues during the quiz administration, post a message on Ed ASAP. Don't send an email because we may not see that in time to help you. If you take it early, do not discuss the content with anyone else in the class who may not have taken it. During a quiz, you may consult the slidesets, your book, any notes you've taken, practice problems, and previous homeworks. You may not consult the internet or any other person.

Several students in past semesters have had difficulties because they used a different email for GradeScope than the one used for Canvas. If you do that, the scores on quizzes may not be reflected on Canvas. Please ensure that you use the same email for both. If we find that you have multiple accounts on GradeScope, we'll consider that probable evidence of cheating.

Getting help:

It is a good idea to post your questions on Ed, so that others can comment and also see the answer. But please don't post homework or lab solutions or large code fragments except in private messages to the instructors. The TAs will manage and grade the projects and homeworks and they are your best source of information on those. General questions about class material or tests should be directed to Dr. Young.

FERPA prohibits instructors from discussing grades with students over email. However, it allows doing so if you provide explicit permission. So, if you ask via email for an update on your grades or how you're doing in the class, please understand that I can't do it unless you explicitly say that you're OK with me providing an email response.

If you are having personal issues that are affecting your performance in the course, feel free to reach out to Dr. Young or to the TAs, if you feel comfortable doing so. This will allow us to provide any resources or accommodations that we can. If immediate mental health assistance is needed, call the Counseling and Mental Health Center (CMHC) at 512-471-3515. Outside CMHC business hours (8am-5pm Monday-Friday) contact the CMHC 24/7 Crisis Line at 512-471-2255.

Help from Sanger Learning Center: This course is supported by Supplemental Instruction (SI) sessions from the Sanger Learning Center Flyer. SI Sessions are led by experienced and trained students who develop engaging, structured, small-group activities for you to work through. These sessions are a consistently scheduled time for you and your classmates to tackle difficult content and learn the best approaches to the course! More information on session times and how to access them will be made available. You're welcome to attend sessions at any point in the semester but regular participation in SI Sessions has been shown to improve students' performance by an average of one-half to a full letter grade higher than the class mean. It is highly recommended for everyone. The Sanger folks will be posting messages to Canvas before long to tell you how to join.

Computation of Your Grade:

The weighting of the grades for the various aspects of the course are as follows:

Component Percent
Exam 1 15%
Exam 2 15%
Final Exam 20%
Weekly Homework and Quizzes30%
Projects 15%
Attendance5%

Individual homework and quizzes each count the same amount, 10 points. The total number of points possible depends on how many total items there are. Suppose there were 20 of them, for a possible 200 points. I add up your points and divide by some smaller number (like 170), effectively dropping three items. (But it's actually better than if I had dropped the lowest three, because every point still contributed to the total score, and you could conceivably score more than 100%.) Details may differ this semester, but I'll do something similar. Bottom line: if you miss a quiz or homework, don't freak out. It probably won't hurt you at all. Just don't miss a bunch of them.

Your semester course grade is computed from the raw scores on Canvas using a Python program I have written. Grades for the entire course tentatively will be averaged using the weighting below:

Course score Grade Course score Grade
[93...100]A [73... 77)C
[90... 93)A- [70... 73)C-
[87... 90)B+ [67... 70)D+
[83... 87)B [63... 67)D
[80... 83)B- [60... 63)D-
[77... 80)C+ [ 0... 60)F

Note that this is tentative. The grades may be curved and may be a bit more generous than this. They will not be less generous. That is, if you have a 93 you are guaranteed an A; but someone who gets an 92 might also get an A, depending on the final distribution of grades in the class.

Students Needing Accommodations:

The university is committed to creating an accessible and inclusive learning environment consistent with university policy and federal and state law. Please let me know if you experience any barriers to learning so I can work with you to ensure you have equal opportunity to participate fully in this course. If you are a student with a disability, or think you may have a disability, and need accommodations please contact Disability and Access (D&A). Please refer to D&A's website for contact and more information: D&A Website. If you are already registered with D&A, please deliver your Accommodation Letter to me as early as possible in the semester so we can discuss your approved accommodations and needs in this course.

In our hybrid class, most accommodations (recording lectures. copies of the slides, etc.) are either already available to everyone in the class or not particularly applicable: Accommodations. The accommodation that is typically most relevant in this class is extra time on tests. That will be provided, but only if we know that you're entitled to the accommodation in time for us to arrange it. Extra time for quizzes is provided on GradeScope; extra time for exams is providing by administering them in a separate location. If you have questions, please ask.

Scholastic Dishonesty:

Academic dishonesty will not be tolerated. See http://www.cs.utexas.edu/academics/conduct for an excellent summary of expectations of a student in a CS class.

All work must be the student's own effort. Work by students in previous semesters, code that you find on-line, or code written by an automated system such as ChatGPT is not your own effort. Don't even think about turning in such work as your own, or even using it as a basis for your work. We have very sophisticated tools to find such cheating and we use them routinely. It's far better to get a 0 on an assignment (or exam) than to cheat.

By the way, even if you do all of the work yourself, sharing your work with someone else is still cheating. You will both be punished. You may think that you're doing your friend a favor. You're not; you're putting both of your academic futures at risk.

Many students begin every assignment by immediately going to Google, trying to find something that might keep them from having to solve the problem for themselves. That is an incredibly stupid thing to do. For one thing, you won't learn the material. But more importantly, you're starting down a moral slippery slope that's liable to send you over a cliff. Suppose you find something up to and including a complete solution that some idiot has posted on GitHub; will you have the self-discipline not to use it?

You may naively believe that changing variable names and reordering code will keep you from being caught. Computer science is amazing! We have very sophisticated automated tools that can compare thousands of programs and find copying even if the variable names are different and the code is substantially re-ordered. With very high likelihood, you will be caught if you cheat. Every semester, students learn this the hard way. Every semester, several students are caught cheating in this class and get an F and/or are reported to the Dean of Students office. Don't be one of those students. It's not worth it!

Sharing of Course Materials is Prohibited: No materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (quizzes, exams, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may not be shared online or with anyone outside of the class unless you have my explicit, written permission. Don't post your work on any publicly available site, such as GitHub, Course Hero, or Chegg.com. It's understandable that you're proud of your work, but this just invites copying for students in this and subsequent semesters. If someone copies your work, even without your knowledge, you will both be liable to punishment.

Unauthorized sharing of materials promotes cheating. It is a violation of the University's Student Honor Code and an act of academic dishonesty. I am well aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course, and even expulsion from the University.

No deviation from the standards of scholastic honesty or professional integrity will be tolerated. Scholastic dishonesty is a serious violation of UT policy; and will likely result in an automatic F in the course and in further penalties imposed by the department and/or by the university. Don't do it! If you are caught, you will deeply regret it. And even if you're not caught, you're still a cheating low-life.


Some Interesting Links:

As I find items of interest to the class, I will post them here. Newer items are near the top. Some of these links may be stale or broken.

A previous TA created two videos that shows how to create a file in an editor and run it in Windows or MacOS. If you still aren't able to do that, I suggest you watch either: Windows video or Mac OS video.

Also, here's a pretty good video one of the TAs found on YouTube explaining how to create a simple Python file (on Windows) and run it: Running a Python Program in Windows

A former TA, Katherine Liang, has created some tutorial videos you might find helpful: see them at Kathy's videos. Currently she has videos relating to: Python Basics: ord and chr; Python Loops with break and continue; Recursion, linear search and binary search.

Good turtle graphics documentation: Turtle Graphics
Turtle tutorial: Tutorial.
Some issues around floating point: FP issues
Some nice videos on Python from the Khan Academy: Khan Academy Videos.

Python Links

Python Tutorials and Books