Web page contents:

General Information

Learning Outcomes

• Describe important classes of data structures (dictionaries, priority queues, disjoint set union) and explain how they work.
  Before trying to design your own data structures, it is important to be aware of a good collection of classical ones. They are useful in their own right, but the ideas that underlie them are also useful for new data structures.
• Augment data structures to expand their functionality.
  If one of the standard data structures doesn't work for the task at hand, it is useful to be able to tweak an existing one to fit your purpose.
• Analyze the efficiency of data structures, including the use of amortized analysis and online competitive analysis.
  In many cases, there are multiple data structures that could be used to solve a problem. In some cases, it is important to be able to analyze the different options and choose the one that will solve the problem most efficiently. Amortized and competitive analysis are two special types of analysis that are useful in some settings.
• Demonstrate knowledge of important themes in data structure design such as persistence, self-adjustment, concurrency.
  This course is not just about going through a catalogue of data structures. Certain ideas can be applied in the construction of many different data structures, and we want to understand those concepts too. Persistence deals with maintaining multiple versions of stored data so that old versions can be accessed when needed. Self-adjustment is used instead of carefully rebalancing data structures to improve the shape of the data structure in response to the sequence of operations performed on it. Concurrent data structures allow multiple threads to access the data simultaneously.
• Select or design appropriate data structures to help solve problems for algorithmic applications.
  The ultimate goal is to ensure that you can find (or build) the right data structure for whatever task you want to solve. You should also be able to justify your design choices and explain why the algorithms that operate on the data structures are correct.

How to Learn This Material

Much of the material in this course is in the same vein as the material of EECS3101. To develop your understanding of the material, it is important to do more than just read the textbook and attend lectures: you must work through exercises.

You can often learn by struggling with problems. However, if you get too stuck or don't know how to begin, help is available. Talk to your classmates (however; see the notes below about academic honesty regarding discussing assignment problems with others). Use the office hours; the instructor is there to help you! You also learn by making mistakes and getting feedback about those mistakes. Just make sure that you use the feedback to improve your understanding.

Groups of students can learn a lot by explaining their solutions to the suggested exercises from the textbook to one another and critiquing the solutions of others. After all, learning how to explain solutions clearly is one of the goals of this course. Seeing where other students' solutions are unclear to you helps you make your own explanations clearer. Be aware that a problem may have many different correct solutions; just because someone's solution is different from yours doesn't necessarily mean that one of them is wrong.

It takes time to build new skills, so it helps if you work on exercises regularly: don't leave all the work to the days right before a test. Similarly, some of the homework assignments will be difficult to finish if you leave them to the last minute.

Academic Honesty

Although you may discuss the general approach to solving a homework problem with other people, you should not discuss the solution in detail. You must not take any written notes away from such a discussion. Also, you must list on the cover page of your solutions any people with whom you have discussed the problems. The solutions you hand in should be your own work. While writing them, you may look at the course textbook and your own lecture notes but no other outside sources.

It is not acceptable to try to find the answer to a homework question on the web, put it in your own words and submit it. You may learn a little by doing this, but you will learn much, much more by working on the problem yourself, and the purpose of this course is to help you learn how to design and analyze data structures on your own. Furthermore, the web will not be available during your exam (or during your job interview at Google), so you should learn to solve problems yourself, instead of relying on others to do your thinking for you.

As time runs out, students are sometimes tempted to get help from other students on assignments in a way that would violate the preceding policy on academic honesty. DO NOT DO THIS! If you do, I will refer the case to the Dean's Office, which is unpleasant for everyone. The assignments are worth very little, so it is not worth risking a sizable punishment. (Furthermore, I have noticed that the students who cheat on the homework assignments almost always fail the tests and exams, so even if I do not catch you cheating, you will likely fail the course if you do not do your own work on the homework assignments.)

It is important that you look at the departmental guidelines on academic honesty.

Marking Scheme

It's a very good idea to type your solutions to homework assignments, since it allows you to edit and polish the answers, but handwritten solutions are also acceptable, as long as they are legible. If you want to type your solutions, LaTeX produces elegantly typeset documents, is available for free, and was built to handle even the most complicated mathematical notation. It can take a while to learn how to use it, but once you do, you will probably not want to type documents any other way.

You should make every effort to make your answers as brief as possible, while still being thorough. Brevity requires careful thought and editing. (Pascal once excused himself for writing a long letter, saying that he did not have enough time to write a shorter one.) Students who write copious amounts usually do not know what they want to say, or are saying it in a very disorganized way. Usually, an answer to a homework question should fit on one sheet of paper. If you are writing much more than that, you probably have not found the best way to solve it. On tests, your answer should usually fit into the space provided for it.

Announcements

• (Aug 15) 79.55% of the class completed the course evaluations. This is close enough to 80% that I will drop the lowest assignment grade when computing final grades.
• (Aug 14) The review session at 10am on Friday, August 18 will be in CLH K.
• (Aug 10) You may bring one 8.5x11-inch sheet of paper with handwritten notes on both sides to the final exam.
• (Aug 10) Next week, I will have office hours on Tue and Thu from 14:30-15:30 in my office, and a review session on Fri at 10:00 in CLH K.
• (Aug 10) If you want to learn more about concurrent data structures, you can take a look at the textbook by Herlihy et al. listed under Other References. The original paper on snapshot objects is here.
• (Aug 1) Please fill out a course evaluation
• (Jul 20) Due to popular demand, Assignment 8 is extended to July 24 at 10:00 p.m.
• (Jul 20) Test 2 will focus on material from the beginning of the Jun 15 lecture to the end of the July 20 lecture. You may bring one 8.5x11-inch sheet of paper with handwritten notes on both sides into the test.
• (Jul 20) You will be able to fill out course evaluations from July 31 to August 14. I find information from these evaluations useful, so please fill out an evaluation for this course. As an incentive, if 80% of the class fills them out, I will drop your lowest assignment grade when computing your final grade.
• (Jul 13) Due to popular demand, Assignment 7 is extended to July 17 at 10:00 p.m.
• (Jun 13) See minor correction to Assignment 4 at bottom of this page.
• (Jun 7) Test 1 will cover material up to the end of the Jun 13 lecture. You may bring one 8.5x11-inch sheet of paper with handwritten notes on one side into the test.
• (May 30) We have a better classroom for our lectures: Curtis Lecture Hall K on Tuesdays and hall J on Thursdays.
• (May 25) By popular demand, the deadline for Assignment 2 has been moved to Monday, May 29. See correction to Assignment 2 at bottom of this page.
• (May 11) The office of student accessibility services is looking for a volunteer to share class notes from EECS4101. This is a good way to help out fellow students and get volunteer experience. Please see this link for more information.

Important Dates

First class	Tuesday, September 9
Reading week #1 (no classes)	May 27-June 2
Test 1	Tuesday, June 20
Reading week #2 (no classes)	June 27-July 2
Last date to drop course without receiving a grade	Friday, July 7
Test 2	Thursday, July 27
Last class	Thursday, August 10
Last date to withdraw from course (receiving W on transcript)	Tuesday, August 15
Exam period	August 17-24

Resources

Textbook

• Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, 4th edition, MIT Press & McGraw-Hill, 2022. For solutions to some of the textbook's exercises and known bugs in the text, see the textbook's web page. You can access an electronic version of the textbook through the York Library website. If you have the older third edition of the book, that's fine.

Other References

• Peter Brass, Advanced Data Structures, Cambridge University Press, 2008. Available online through York library.
• Ronald L. Graham, Donald E. Knuth, and Oren Patashnik, Concrete Mathematics: A Foundation for Computer Science (2nd edition), Addison-Wesley, 1994.
• Maurice Herlihy, Nir Shavit, Victor Luchangco and Michael Spear, The Art of Multiprocessor Programming, 2nd edition, Morgan Kaufmann, 2020. Includes material on concurrent data structures. An earlier edition is available online through the York Library.
• Pat Morin, Open data structures.
• Gonzalo Navarro, Compact Data Structures, Cambridge University Press, 2016.
• Hanan Samet, Foundations of Multidimensional and Metric Data Structures, Elsevier/Morgan Kaufmann, 2006.
• Clifford A. Shaffer, Data Structures and Algorithm Analysis, edition 3.2.0.10, 2013.
• Daniel Solow, How to Read and Do Proofs (6th edition), Wiley, 2013.

Other Tools

• B-tree visualizer (use an even max degree)

Reading

Course Handouts

• Prerequisite material
• Assignment 1
• Assignment 2 Revised deadline: May 29, 10pm. Correction: part (f)(ii), "line 11" should be "line 10". Clarification: Each array should be full (i.e., each array should contain 2^k elements of the set for some k).
• Assignment 3
• Sample tests from a previous year: Test 1, Test 2 (and hints for solutions), Exam
• Assignment 4 This version includes a correction to line 30 and 33 of the pseudocode to detach the root next from the root list (updated June 13).
• Assignment 5 -- the TA pointed out that there was some confusion in the solutions I handed out: the root should be the element with the minimum priority, not the maximum priority.
• Assignment 6
• Assignment 7 -- deadline extended to Monday, July 17 at 10:00 p.m.
• Assignment 8 -- deadline extended to Monday, July 24 at 10:00 p.m.
• Assignment 9
• Assignment 10 (last one!) -- a typo in Q2(b)(i) and 2(b)(ii) has been corrected, and the deadline extended to Monday, August 14.

Updated August 15, 2023