CSC 316 — Data Structures
Spring 2019 Schedule of Lectures
Lecture slides, assignments, and solutions available from the course Moodle space
Date  Lecture #  Topic  Assignment  Due 

Jan 7  1 
Overview, goals, logistics
Introduction 

Jan 9  2 
Algorithm analysis
Chapter 4 (5th/6th) 
Project 1
HW 1 

Jan 14  3 
Recursion
Chapter 3.5 (5th) / Chapter 5 (6th) 

Jan 16  4 
Stacks, queues
Chapter 5 (5th) / Chapter 6 (6th) 

Jan 21  —  No class (MLK Holiday)  
Jan 23  5 
Linked lists
Chapter 3.23.4, 6 (5th) / Chapter 3.23.4, 7 (6th) Discussion of Project 1 
HW 2  HW 1 
Jan 28  6 
Linked lists (cont'd)
Dictionaries Chapter 9.5 (5th) / Chapter 10.1 (6th) 

Jan 30  7 
Binary search
Chapter 9.3 (5th) / Chapter 10.3 (6th) Skip lists Chapter 9.4 (5th) / Chapter 10.4 (6th) 

Feb 4  8 
Introduction to trees
Chapter 7 (5th) / Chapter 8 (6th) 
HW 2  
Feb 6  —  No class (reading day)  Project 2  Project 1 
Feb 11/12  —  First inclass exam  
Feb 13  9 
Binary search trees
Chapter 10.1 (5th) / Chapter 11.1 (6th) 

Feb 18  10 
23 trees, Btrees
Chapter 10.4, 14.3 (5th) / Chapter 11.5, 15.3 (6th) 
HW 3  
Feb 20  11 
Discussion of Project 2 Splay trees Chapter 10.3 (5th) / Chapter 11.4 (6th) 

Feb 25  12 
Priority queues
Heaps Chapter 8.18.3 (5th) / Chapter 9.19.4 (6th) 

Feb 27  13  Leftist Heaps  HW 3  
Mar 4  —  No class (reading day) 
Project 3
HW 4 
Project 2 
Mar 6/7  —  Second inclass exam  
Mar 11  —  No class (Spring break)  
Mar 13  —  No class (Spring break)  
Mar 18  14 
Uptrees for unionfind applications
Chapter 11.4 (5th) / Chapter 14.7.3 (6th) 

Mar 20  15  Introduction to graphs
Chapter 13.1, 13.2 (5th) / Chapter 14.1, 14.2 (6th) 

Mar 25  16 
Minimum spanning trees
Chapter 13.6 (5th) / Chapter 14.7 (6th) Discussion of Project 3 

Mar 27  17 
Discussion of Project 3
Shortest paths Chapter 13.5 (5th) / Chapter 14.5 (6th) 
HW 5  HW 4 
Apr 1  18 
Shortest paths (cont'd), Topological ordering
Chapter 13.4.1, 13.4.3 (5th) / Chapter 14.6 (6th) 

Apr 3  19 
Graph traversals
Chapter 13.3 (5th) / Chapter 14.3 (6th) 

Apr 8  —  No class  Project 4  Project 3 
Apr 10  20 
PageRank
Hashing techniques Chapter 9.2 (5th) / Chapter 10.2 (6th) 

Apr 15  21 
Discussion of Project 4
Hashing techniques (cont'd) 

Apr 17  22 
Sorting: Mergesort, Quicksort, Sorting lower bound, Radix sort
Chapter 11.111.4 (5th) / Chapter 12.112.4 (6th) 
HW 5  
Apr 22  23  Greedy algorithms  
Apr 24  —  No class (reading day)  Project 4  
Apr 29/30  —  Final exam 
Syllabus
Prerequisites
Students who wish to take this course must be CSC majors who have received a grade of C or better in both CSC 216 (Programming Concepts with JAVA) and CSC 226 (Discrete Math).Objectives
The purpose of this course is to introduce the principles and underlying concepts of algorithm design, and enhance your problem solving and software development skills. To this end, a wide range of practical techniques for manipulating data in digital computers will be presented, along with a mathematical analysis of their performance.
At the conclusion of the course you should be able to:
 complete moderately large programming projects independently, and code modules of larger projects;
 define abstract data types to subdivide a large problem into smaller, manageable subproblems;
 select for a repertory of commonly used data structures the ones most appropriate for the application at hand;
 effectively implement the abstract data types learned in class (or the ones you design yourself);
 evaluate the relative performance of alternative data structure and algorithm designs for a given problem, in terms of their asymptotic running time; and
 explain and feel confident about your approach to the solution of a given problem, and its implementation.
I encourage and expect you to participate actively in the learning process. In particular, I welcome your comments and questions as we cover material in class. Oneway lectures quickly become boring, both for you and for me. By asking lots of questions your understanding of the material will be deepened significantly, and the course will be much more fun!
Outline
The course will cover a wide range of data structures and associated algorithms, including:
 Properties of programs, running time, and asymptotics
 Array and linkedmemory implementations of lists, stacks, and queues
 Searching using lists, unbalanced tree structures (binary search trees, Splay trees) and balanced trees (23 trees, randomized binary search trees)
 Uptrees as sets with unionfind operations
 Graphs and graph algorithms (traversals, shortest paths, minimum spanning trees)
 Sorting (heap sort, merge sort, insertion sort, selection sort, quick sort)
 Hash tables and hashing techniques
Textbook
Students are required to purchase the following textbook:
 M. T. Goodrich, R. Tamassia, Data Structures and Algorithms in JAVA (6th edition), Wiley, 2014
The authors maintain a webpage with useful resources.
I will also make available an extensive set of lecture slides.
Grading
Students are required to complete all assignments and show all work in order to receive full credit. The final grade will be determined using the following weights:
 40% — Four programming projects (10% each)
 10% — Five homework assignments (2% each)
 30% — Two inclass exams (closed book, 15% each)
 20% — Final exam (comprehensive, closed book)
Policies
Attendance: Attendance is not mandatory but strongly encouraged. Students are responsible for making up any course material they miss.
Assignments: No hard copies of assignments or solutions will be handed out. New assignments and solutions will be announced in class and/or the course mailing list, and will be available on the course web page.
Submission: Students must submit their assignments as PDF or Word files using the submit facility. The deadline for submission is midnight (Eastern time) on the day due. Any deadline extensions are up to the discretion of the instructor, and will be announced to the whole class. Extensions may be provided to individual students only in advance of the submission deadline and only under extenuating circumstances.
Late Submission: No late assignments will be accepted and no partial credit will be given for late assignments without a valid excuse.
Cheating: Homework and projects are individual assignments and students are required to submit their own solutions. All students are bound by the University's academic integrity policies (refer to the relevant section below).
Teaching Assistant
TBD (tbd@ncsu.edu) is the TA for this course.
His office hours are: TBD in 1229B EB2, or you may contact him to arrange for an online chat or video call at a mutually convenient time.
Feel free to contact the TA for any questions about the course.
Office Hours
My office is in Room 2306 of the EB II building.
My office hours are 4:155:15pm on Mondays and Wednesdays. Distance students may either call me during those times, or may arrange to stop by or call at a different mutually convenient time.
Academic Integrity
Students are required to respect the NC State academic integrity policies.