COMP 152: Object-Oriented Data Structures and Algorithms, Fall 2020

This syllabus is subject to change based on specific class needs, especially the schedule. Significant deviations will be discussed in class. Individual exceptions to the policies and schedule are granted only in cases of true emergency. Please make arrangements with me if an emergency arises.




Data structures continues the study of abstraction and programming through a focused study of data structures, algorithms, and abstract data types. The primary focus of this course is the design and development of algorithms and programs using data abstraction and information hiding via abstract data types ADTs. Data abstraction is absolutely fundamental to good programming practice and the management of large scale problems and data sets. This course is designed to round out a student’s understanding of basic computer science concepts while strengthening the program design and development skills through continued use of an object-oriented design methodology.

It is imperative that programmers and program designers be able to determine which of the many solutions available is the best for their specific task. Throughout the course, students will focus on to making solid quantitative and qualitative judgments about program efficiency and overall program design choices. Gone are the days when “it works” is a good enough assessment for the quality of a program.

Students will explore the ideas and concepts brought up in class and homework assignments during the weekly lab session. In addition to hands on exercises, lab sessions will be used to explore current, relevant research in computer science.


The course textbook will be:

Other sources will be posted on this webpage as needed.


The course aims to cover most of chapters 1-8 and parts of chapters 9-12. Other elements may be covered as time allows. The topics include, but are not limited to:

- Review of Python fundamentals - Object-Oriented Design Basics and Patterns
- Experimental and Asymptotic Algorithm Analysis - Recursion
- Arrays and Sequence Data Types - Stacks, Queues, Deques, and Priority Queues
- Abstract Data Types - Linked Data Structures
- Binary Search Trees and Tree Traversal Algorithms - Maps and Dictionaries
- Sorting, Searching, and Selection Algorithms - Graph Algorithms (as time allows)

Programming Environment

This course uses Python 3 and, an online IDE service. Students will receive, complete, and submit assignments all via the website. Students can also complete assignments on their local machines (I recommend the Anaconda software package) or on the departmental server, but all assignment submissions must be done through unless otherwise specified.


Assignments and Workload

The weekly workload for this course will vary by student and by week but should be about 14 hours per week on average. The following table provides a rough estimate of the distribution of time over different course components for a 16 week semester, as well as detailing the type, amount, and relative value of all assignments.

Category Amount Final Grade Weight Total Time Time/Week (Hours)
Lectures 41 10% (Participation) - 2.25
Labs 8–10 15% - 2.75
Homework 8–10 15% 48 3
Exam Study - - 16 1
Exams 3–4 30% - -
Projects 2 30% 48 3
Reading+Unstructured Study - -   2

Homework assignments will always either precede a lab to prepare for it or follow a lab to complete it. Each exam will focus primarily, but not necessarily exclusively, on the material covered since the previous exam.


Lab and homework assignments are graded on a simple 3 point scale, marked with (in decreasing order) a check-plus, check, or check minus. Your final grade for these two assignment categories is then based on the respective averages.

Your participation grade is based on a variety of activities. During class I will often make use of the Socrative app, so you’ll need to install this on your phones. Participating in Socrative questions and with in-class group activities is required for a decent participation grade; an A includes asking questions either in class or in office hours.

Your final grade is based on a weighted average of particular assignment categories. You can estimate your current grade based on your scores and these weights. You may always visit the instructor outside of class to discuss your current standing. Assignments and final grades use a standard grading scale shown below and will not be curved except in rare cases when deemed necessary by the instructor.

This courses uses a standard grading scale. Assignments and final grades will not be curved except in rare cases when its deemed necessary by the instructor. Percentage grades translate to letter grades as follows:

Score Grade
94–100 A
90–93 A-
88–89 B+
82–87 B
80–81 B-
78–79 C+
72–77 C
70–71 C-
68–69 D+
62–67 D
60–61 D-
0–59 F

You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge*.

You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge.



The following tentative calendar should give you a feel for how work is distributed throughout the semester. Assignments and events are listed in the week they are due or when they occur. This calendar is subject to change based on the circumstances of the course.

Note: All readings should be done before the class period in which they are listed below.

Date Topic Assignments and Readings
Wed 08/19 (Week 1) Logistics, Overview 1.1-1.3
Wed 08/19 (lab) Lab 1: Python Basics with Turtle  
Fri 08/21 Python Review 1.4-1.5
Mon 08/24 (Week 2) Advanced Python Features 1.6-1.9
Wed 08/26 Review Exercises 1.10-1.11
Wed 08/26 (lab) Lab 2: Using Sets and Dictionaries  
Fri 08/28 Classes 2.1-2.3
Mon 08/31 (Week 3) Inheritance 2.4
Wed 09/02 Containment vs. Extention; Misc. OOP 2.5-2.6
Wed 09/02 (lab) Lab 3: Object-Oriented Turtle Assignment PDF
Fri 09/04 Exam 1  
Mon 09/07 (Week 4) Experimental Performance Analysis 3.1
Wed 09/09 Big-O 3.2
Wed 09/09 (lab) Lab 4: Prefix Sums Assignment PDF
Fri 09/11 Analysis Exercises 3.3
Mon 09/14 (Week 5) Justification and Proof 3.4
Wed 09/16 Big-O and proof Examples  
Wed 09/16 (lab) Hwk 5: Big-O Practice  
Fri 09/18 Common Python Bugs Handout 1, Handout 2
Mon 09/21 (Week 6) Recursive Functions 4.1
Wed 09/23 Recurrences 4.2-4.3
Wed 09/23 (lab) Lab 5/Hwk 6: Recursion  
Fri 09/25 Exam 2  
Mon 09/28 (Week 7) More Recursion 4.4
Wed 09/30 Recursive Design 4.5
Wed 09/30 (lab) Lab 6/Hwk 7: Recurrences  
Fri 10/02 Arrays 5.1-5.2
Mon 10/05 (Week 8) Dynamic Array Analysis 5.3
Wed 10/07 Sequence Type Operations 5.4-5.5
Wed 10/07 (lab) Lab 7/ Hwk 8: Dynamic Arrays  
Fri 10/09 (No class – Pseudo-fall break)  
Mon 10/12 (Week 9) Stacks 6.1
Wed 10/14 Queues 6.2
Wed 10/14 (lab) Lab 8 and Project 1: RPN Calculator  
Fri 10/16 Double-Ended Queues 6.3
Mon 10/19 (Week 10) Singly-linked Lists 7.1
Wed 10/21 Circularly-linked lists 7.2
Wed 10/21 (lab) Free Lab for Project 1  
Fri 10/23 Doubly-linked lists 7.3
Mon 10/26 (Week 11) Binary Trees 8.1-8.2
Wed 10/28 Binary Tree Implementation 8.3
Wed 10/28 (lab) Lab 10: Trees  
Fri 10/30 Exam 3  
Mon 11/02 (Week 12) Tree Traversals 8.4
Wed 11/04 Priority Queues 9.1-9.3
Wed 11/04 (lab) Lab 11: Heaps and Heapsort  
Fri 11/06 Heapify, Sorting 9.4
Mon 11/09 (Week 13) Maps: Naive Implementations 10.1,10.3
Wed 11/11 Hash Tables, plus related structures 10.2, 10.5
Wed 11/11 (lab) Project 2  
Fri 11/13 Binary Search Trees 11.1-11.2
Mon 11/16 (Week 14) AVL Trees 11.3
Wed 11/18 Merge Sort 12.1-12.2
Wed 11/18 (lab) (Free lab for Project 2)  
Fri 11/20 Quicksort and more 12.3-12.4
Mon 11/23 (Week 15) Exam Review  
Wed 12/02   Project 2 Due at midnight
TBD Exam 4 (Final)  

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Student Learning Outcomes

This course covers a variety of knowledge areas as categorized by the ACM/IEEE-CS Task Force on Computing Curricula. Note that not all of these areas are introduced in this course; some are touched upon previously and others will be developed further in later courses. At the end of the course, students should achieve the following learning outcomes with the specific level of mastery:

Knowledge Unit Learning Outcome with Level of Mastery
Basic Analysis - In the context of specific algorithms, identify the characteristics of data and/or other conditions or assumptions that lead to different behaviors. [Familiarity]
  - Explain what is meant by “best”, “average”, and “worst” case behavior of an algorithm. [Familiarity]
  - In the context of specific algorithms, identify the characteristics of data and/or other conditions or assumptions that lead to different behaviors.
  - Perform empirical studies to validate hypotheses about runtime stemming from mathematical analysis. Run algorithms on various input sizes and compare performance. [Assessment]
  - Determine informally the time and space complexity of simple algorithms. [Usage]
  - Understand the formal definition of big O. [Familiarity]
  - List and contrast standard complexity classes. [Familiarity]
  - Give examples that illustrate time-space trade-offs of algorithms. [Familiarity]
  - Use big O notation formally to give asymptotic upper bounds on time and space complexity of algorithms. [Usage]
Algorithmic Strategies - Have facility mapping pseudocode to implementation, implementing examples of algorithmic strategies from scratch, and applying them to specific problems. [Familiarity]
  - Use a divide-and-conquer algorithm to solve an appropriate problem. [Usage]
Fundamental Data Structures and Algorithms - Implement simple search algorithms and explain the differences in their time complexities. [Usage, Assessment]
  - Discuss factors other than computational efficiency that influence the choice of algorithms, such as programming time, maintainability, and the use of application-specific patterns in the input data. [Familiarity]
  - Be able to implement common quadratic and $O(n \log n)$ sorting algorithms. [Usage]
  - Demonstrate the ability to evaluate algorithms, to select from a range of possible options, to provide justification for that selection, and to implement the algorithm in a particular context. [Usage, Assessment]
Fundamentals - Explain the concept of modeling and the use of abstraction that allows the use of a machine to solve a problem. [Familiarity]
Basics of Counting - Perform computations involving modular arithmetic. [Usage]
Processing - Analyze simple problem statements to identify relevant information and select appropriate processing to solve the problem. [Assessment]
  - Identify the issues impacting correctness and efficiency of a computation [Familiarity]
Defensive Programming - Demonstrate the identification and graceful handling of error conditions. [Usage]
Graphs and Trees - Demonstrate different traversal methods for trees [and graphs], including pre, post, and in-order traversal of trees. [Usage]
  - Model a variety of real-world problems in computer science using appropriate forms of [graphs and] trees, such as representing … a hierarchical file system. [Usage]
Data Modeling - Describe the main concepts of the OO model such as object identity, type constructors, encapsulation, inheritance, polymorphism, and versioning. [Familiarity]
Object-Oriented Programming - Compare and contrast the procedural and object-oriented approach. Understand both as defining a matrix of operations and variants. [Assessment]
  - Use subclassing to design simple class hierarchies that allow code to be reused for distinct subclasses. [Usage]
Algorithms and Design - Determine whether a recursive or iterative solution is most appropriate for a problem. [Assessment]
  - Implement a divide-and-conquer algorithm for solving a problem. [Usage]
  - Apply the technique of decomposition to break a problem into smaller pieces. [Usage]
  - Implement a coherent abstract data type, with loose coupling between components and behavior. [Usage]
  - Discuss the importance of algorithms in the problem-solving process. [Familiarity]
  - Discuss how a problem may be solved by multiple algorithms, each with different properties. [Familiarity]
  - Implement, test, and debug simple recursive functions. [Usage]
Fundamental Programming Concepts - Analyze and explain the behavior of simple problems involving the fundamental programming constructs. [Assessment]
  - Identify the base case and the general case of a recursively-defined problem. [Assessment]
Fundamental Data Structures - Discuss the appropriate use of built-in data structures. [Familiarity]
  - Describe common applications for each data structure in the topic list. [Familiarity]
  - Write programs that use each of the following data structures: arrays, strings, linked lists, stacks, queues, sets, and maps. [Usage]
  - Compare alternative implementations of data structures with respect to performance. [Assessment]
  - Choose the appropriate data structures for modeling a given problem. [Assessment]
Development Methods - Trace the execution of a variety of code segments and write summaries of their computations. [Assessment]
  - Apply a variety of strategies to the testing and debugging of simple programs. [Usage]
  - Construct and debug programs using the standard libraries available with the chosen programming language. [Usage]
  - Apply consistent documentation and program style standards that contribute to the readability and maintainability of software.
Software Construction - Construct models of the design of a simple software system that are appropriate for the paradigm used to design it. [Usage]
Professional Communication - Evaluate written technical documentation to detect problems of various kinds. [Assessment]