CSCI 151: Data Structures

Semester: Fall 2017
Room: King 123
Class Time: Mon/Wed/Fri 10:00pm-10:50am
Office Hours: W/Th: 14:30-16:00pm
or by appointment
Professor: Roberto Hoyle
email: roberto.hoyle AT oberlin edu
Please include "cs151" in the subject.
Office: King 223c
Phone: x58424

Announcements

Schedule

(Will be updated throughout the semester)
WEEK DAY ANNOUNCEMENTS READING HW
1 Aug 28 Consider bookmarking Java for Python Programmers as a handy reference (PDF version) Course Overview Lab 0 - Intro to lab/Eclipse
Aug 30   Introduction to Java (Read: Ch 1)
Sep 01 Objects and reference variables (Read: Ch 2)
2 Sep 04 Labor Day [No Class]
Sep 06 Last Day to Add/Drop (Sep 07) Introduction to Object Oriented Programming (Read: Ch 3) Prelab 1

Lab 1 - Playing with Java
Sep 08   Inheritance and Generics
(Read: Ch 4)
3 Sep 11  
Sep 13 Java Collections
(Read: Ch 6)
Data Structure: Array Lists, Iterators
(Read Ch 15)
Sep 15
4 Sep 18   Data Structure: Stacks
Data Structure: Queues
(Read Ch 16)
Prelab 2

Lab 2 - MyArrayList
Sep 20 Algorithm Analysis
  • Big-Oh Notation
  • Algorithm Growth
  • Classes of algorithms

(Read Ch 5)
Sep 22
5 Sep 25   Data Structure: Linked Lists
(Read Ch 17)
Lab 3
Sep 27 Recursion
  • Divide and Conquer
  • Recursive backtracking
  • Dynamic programming
(Read Ch 7.1-7.3, 7.5-7.7)
Sep 29
6 Oct 02   Data Structure: Trees
  • General
  • BST
(Read Ch 18)
Lab 4
Oct 04
Oct 06 RJH Travelling
Guest lecturer TBA
RJH Travelling
Guest lecturer TBA (Oct 08)
Data Structure: Balanced Trees
  • AVL
  • Red-Black
  • 2-3-4 Trees
(Read Ch 19)
7 Oct 09   No lab this week
Oct 11 Review for exam
Oct 13 Midterm Exam [topics]
  Oct 16 Fall Break (Oct 14-22)
Oct 18
Oct 20
8 Oct 23   Data Structure: Maps and Sets
Data Structure: Priority Queues
  • Heaps
  • Array based heaps
  • Heapsort

(Read Ch 21)
Lab 5
Oct 25
Oct 27
9 Oct 30 Last Day for P/NP, CR/NE,
  or Withdraw (Oct 31)
Data Structure: Hashtables
(Read Ch 20)
Lab 6
Nov 01  
Nov 03
10 Nov 06   Data Structure: Tries Lab 7
Nov 08 Data Structure: Graphs
  • Terminology
  • Representation
    • Edge List
    • Adjacency Matrix
    • Adjacency List

(Read Ch 14.1,14.2)
Nov 10
11 Nov 13   Data Structure: Graphs
  • Shortest Path - Unweighted BFS

(Read Ch 14.3)
Lab 8
Nov 15 Data Structure: Graphs
  • Shortest Path - Positive Weighted Paths (Dijkstra's Alg.)
  • Shortest Path - Negative Weighted Paths (Bellman-Ford Alg.)
  • Topological Sorting

(Read Ch 14.3-14.5)
Nov 17
12 Nov 20   Algorithm: Insertion and Selection Sort
Algorithm: Linear & Binary Search
(Read Ch 8.1-8.3, 5.5-5.6)
Lab 9
Nov 22
Nov 24 Thanksgiving Break (Nov 23-26)
13 Nov 27   Algorithm: Merge sort
Algorithm: Lower bound of search
(Read Ch 8.5, 8.8)
Nov 29 More Sorting Algorithms
  • Bubble
  • Shell
  • Quick
Lower bounds of sorting
Quickselect
(Read Ch 8.4,8.6-8.8)
Lab 10
Dec 01
14 Dec 04   Review and class wrapup
Dec 06  
Dec 08  
  Dec 13 Final Exam (2-4pm) [topics]

Course Description

From the Oberlin catalog course description:

This course builds upon the principles introduced in CSCI 150 and provides a general background for further study in Computer Science. The course will cover object-oriented programming concepts; the design and implementation of data structures (linked lists, stacks, queues, trees, heaps, hash tables, and graphs) and related algorithmic techniques (searching, sorting, recursion); and algorithm analysis. Students will be expected to complete a number of programming projects illustrating the concepts presented.

Goals and course objectives

My goals and objectives for students taking this course are as follows:

  1. Understand Big-O measurements and why it is of great importance to data structures and algorithms
  2. Study common algorithms used in computer science
    • Sorting: Bubble, insertion, shell, merge, quick, heap, radix
    • Searching/Selection: Linear, binary, quick
  3. Become skilled in common data structures (lists, stacks, queues, trees, heaps, hashtables)
    • Be able to create any of these from scratch -- structure and operations
    • Know the running times for the most common operations
    • Be able to reason about which structure is most appropriate for a given task
  4. Become fluent in Java programming including
    • Design and creation of interworking classes
    • Proper use of visibility modifiers
    • Creation and handling of exceptions
    • Regular use of Javadoc
  5. Gain experience using various tools in the Java/Linux environment
  6. Develop the habits of proper coding and thorough testing

Textbook

The text for the course is Data Structures and Problem Solving Using Java, Fourth Edition by Mark Weiss. All of the code from the text is available on the author's website: http://users.cis.fiu.edu/~weiss/dsj4/code/code.html

Data Structures and Problem Solving

Data Structures & Problem Solving Using Java by Weiss

You can use the 3rd edition if you want, but you might want to cross-check the readings.

A copy should be on reserve in the library.

Grading

Course grades will be calculated based on the following distribution:

The distribution might be adjusted based on the progression of the course.


Programming projects will generally consist of two major components:

  1. An underlying data structure implementation
  2. An application of this data structure
The actual breakdown of points will depend on the individual project, but the plurality of points will be for the data structure implementation. Projects will also be graded on style, documentation, and test cases.

If a portion of your program is not working correctly, please clearly indicate it in the comments at the beginning of the file and in the methods that are not working. Problems that I discover are graded more severely than those you discover.


Programming assignments will be graded on both correctness as well as programming style. Good programming style includes the following:

More information on Java style can be found on Sun's Code Conventions web page. There is also an open source tool checkstyle that can be used to check the style used.

Course Policy

Attendance

Regular class and lab attendance and participation is expected. Please talk to me if regular class attendance is going to be a problem.

Homework Assignments

There will be a number of assignments made in this class. I expect every student to attempt each assignment and turn in the results. You are encouraged to complete every assignment as this is one of the most effective ways to learn the material.

If you know that for some reason you will not be able to submit the assignment before the deadline, you should contact me in advance of the deadline. Extensions are only granted in exceptional circumstances, but need to be done in advance.

Late submissions of lab assignments will be penalized up to 50% per day after the deadline. No late prelabs will be accepted. You will be allowed 1 late day in each half of the semester and 1 late day that can be used whenever (total of 3). You must include the fact that you are using one of your late days in the README document for an assignment.

Accommodations for students with disabilities

If you have a disability that might impact your performance in this course, or requires special accommodation, please contact me as soon as possible so that appropriate arrangements can be made. Support is available through Student Academic Success Programs. You will need to contact them to get your disability documented before accommodations can be made.

Plagiarism and Academic Dishonesty

I have very low tolerance for academic dishonesty, and will vigorously pursue available remedies for any incidents. All work in this class is to be performed according to the Oberlin Honor Code. Specifically I expect that:

  1. Quizzes and exams will be closed book, closed notes, and no communication between students. This includes discussing the same to students who are taking a quiz/exam at another time.
  2. Discussion of assignments is expected and encouraged, however all work and code on assignments should be your own without outside assistance.
  3. Sources should be cited including the textbook and other web sites when you use them in your work.
  4. You are not permitted to share your source code with other students, including future ones.
  5. You are not permitted to use other students solutions as your own (even those from a prior semester), nor answer keys, nor instructor versions.

Illustrative examples:

  1. Confirming that we had and exam is OK, telling another student in the class who has not taken it that it was easy/hard, what topics, etc. is NOT OK.
  2. On a project or homework, discussing what needs to be done and how it can be done is OK, having a student (other than a TA) go over your code is NOT OK, discussing what might be wrong and how to tell is OK (and encouraged).
  3. Other than your partner on a given assignment, it is NOT OK to give a student a copy of your code or for them to give you a copy of theirs. As a rule of thumb when discussing the assignments, you should not be writing finished solutions out, and you should be able to independently recreate the material on your own.
  4. On an assignment, you base your design off of the textbook's example. This is OK if you cite the source in the code. You don't need to have it be part of the Javadoc. E.g.,
    /* based on insertion sort from Weiss 3rd Ed, p. 306 */

All assignments must include the following signed statement:

"I have adhered to the Honor Code in this assignment."

Electronic submissions should include the honor statement in either the README file or header comments and must include your name.

Grader and Tutors

Contact me if you are interested in a Student Academic Services approved tutor.

The CSMC might hold walk-in tutoring sessions as well.

The CS department will be hiring a couple of students to work as lab helpers. They will be in the upstairs lab during the hours posted below.

The most up-to-date list can be found here.


VI PoweredLast Modified: January 28, 2016 - Roberto Hoyle, based on material from Benjamin Kuperman

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