CSI 3334: Data structures, Fall 2017
Data structures and the algorithms that operate on them are the keys to making efficient software. They are also very interesting. This course will cover data structures in a way that exercises your problem-solving skills. These problem-solving skills are what you will need to be a successful programmer, scientist, engineer, or mathematician.
This course covers:
- fundamental data structures: arrays, lists, queues, stacks, heaps, trees, and graphs
- appropriateness of different data structures for different tasks
- standard algorithms to operate on data structures, including searching and sorting
- analysis of algorithms for time and space complexity
- data abstraction (separation of interface and implementation)
- C++ implementation
This is a difficult course. My recommendation is to attend lectures, study hard, start projects early, and seek help from the professor when you need it.
Lectures are from 14:00 to 15:15 in Cashion C314 on Tuesday, Thursday. You may use the general computer science labs, though there is no lab component of the course.
My office is in the Hankamer building, and office hours are listed on my home page. I am glad to talk to students during and outside of office hours. If you can't come to my office hour, please make an appointment for another time, or just stop by.
The TA for this course is Alex Kuritcyn. The TA will assist in grading assignments but not in lecturing, assignments, or projects. Please talk with Dr. Hamerly for any assistance.
Here is a schedule of the material we will cover:
|1||Aug 21-25||Overview, C++ review||1.1-1.6; syllabus, submission guidelines, using the shell, style guidelines||Project 0 assigned|
|2||Aug 28-Sep 1||Algorithm analysis||2||Homework 0 assigned; Project 1 assigned|
|3||Sep 4-8||Algorithm analysis||Homework 1 assigned|
|4||Sep 11-15||ADTs, lists, stacks, queues||3.1-3.3, 3.6, 3.7||Project 2 assigned|
|5||Sep 18-22||Trees||4||Homework 2 assigned|
|6||Sep 25-29||Trees||Project 3 assigned|
|8||Oct 9-13||Heaps||Project 4 assigned, Homework 3 assigned, Midterm exam|
|10||Oct 23-27||Hashing||Project 5 assigned|
|11||Oct 30-Nov 3||Sorting||7 (skip 7.4)||Homework 4 assigned|
|12||Nov 6-10||Sorting, Graphs||9.1-9.5|
|13||Nov 13-17||Graphs||Project 6 assigned|
|15||Nov 27-Dec 1||Disjoint set, Algorithm design||8, 10||Homework 5 assigned||Last day of class|
The final exam will be December 8, 09:00-11:00. The latest university finals information is available at the registrar web page for final exam scheduling.
Textbooks & resources
Required text: we will be using Mark Weiss' textbook Data Structures and Algorithm Analysis in C++ (4th Edition). An older edition might be okay, but you are responsible in case there are differences between the editions. You can purchase this book from the Baylor bookstore or amazon, among other places.
Further online resources:
- We will use Canvas for keeping records of assignment grades.
- Project submission guidelines for this course.
- Using the command-line shell for testing in this course.
- Coding style guidelines for this course.
- Here is a sample vimrc file containing many of the settings I use in the VIM editor.
- Bruce Eckel, Thinking in C++ (2nd edition).
- The Standard Template Library (STL) reference.
- The C++ language reference.
Grades will be assigned based on this breakdown:
- midterm exam: 20%
- final exam: 25%
- projects: 30% (including 5% for milestones)
- homework: 25%
Important: Each project not completed by the end of the semester will result in a drop of one letter grade. For example, if you would have received a 'B', but you did not complete two of the projects, then your letter grade will be a 'D'.
Different projects and assignments will have different point values. Points are not comparable across assignments; each graded homework/project/exam/etc. will have an associated weight which determines how it factors into your grade.
In-class exams are closed-book. The final will be comprehensive.
Homework is due at the beginning of class; homework turned in after it has been collected but before the end of class will receive a 20% penalty. Homework will not be accepted after class on the due date.
Final letter grades will be assigned at the discretion of the instructor, but
here is a minimum guideline for letter grades:
F < 60 ≤ D- < 62 ≤ D < 67 ≤ D+ < 70 ≤ C- < 72 ≤ C < 78 ≤ C+ < 80 ≤ B- < 82 ≤ B < 88 ≤ B+ < 90 ≤ A- < 92 ≤ A
- This website contains the official course information. Please check it regularly for updates.
- All work in this course is strictly individual, unless the instructor explicitly states otherwise. While discussion of course material is encouraged, collaboration on assignments is not allowed. Collaboration includes (but is not limited to) discussing with anyone (other than the professor) anything that is specific to completing an assignment. You are encouraged to discuss the course material with the professor, preferably in office hours, and also by email.
- Exams may be made up with prior arrangement (made at least one class before to the exam) or due to illness, with a note from a health care professional.
- Bring any grading correction requests to your professor's attention within 2 weeks of receiving the grade or before the end of the semester, whichever comes first.
- Class should be a place you are glad to go. After all, you signed up for the class, and we get to talk about data structures!
- In order to facilitate keeping attendance, please choose a seat that you will use for the rest of the course.
I take academic honesty very seriously. Many studies, including one by Sheilah Maramark and Mindi Barth Maline have suggested that "some students cheat because of ignorance, uncertainty, or confusion regarding what behaviors constitute dishonesty" (Maramark and Maline, Issues in Education: Academic Dishonesty Among College Students, U.S. Department of Education, Office of Research, August 1993, page 5). In an effort to reduce misunderstandings, here is a minimal list of activities that will be considered cheating in this class:
- Using a source other than the course textbook, the course website, or your professor to obtain credit for any assignment, project, or exam.
- Copying another student's work. Simply looking over someone else's source code is copying.
- Providing your work for another student to copy.
- Collaboration on any assignment, unless the work is explicitly given as collaborative work. Any discussion of an assignment or project is considered collaboration.
- Using notes or books during any exam.
- Giving another student answers during an exam.
- Reviewing a stolen copy of an exam.
- Studying tests or using assignments from previous semesters.
- Providing someone with tests or assignments from previous semesters.
- Taking an exam for someone else.
- Turning in someone else's work as your own work.
- Studying a copy of an exam prior to taking a make-up exam.
- Providing a copy of an exam to someone who is going to take a make-up exam.
- Giving test questions to students in another class.
- Reviewing previous copies of the instructor's tests without permission from the instructor.
Title IX Office
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This page was last updated August 18, 2017 at 12:49 (America/Chicago)