Course Syllabus

Welcome to CS201: Elementary Data Structures

Specific information about this course and its requirements can be found below. For more general information about taking Saylor Academy courses, including information about Community and Academic Codes of Conduct, please read the Saylor Student Handbook.


Course Description

Introductory survey of the specification and implementation of basic abstract data types and their associated algorithms. Structures discussed include: stacks, queues, lists, sorting and selection, searching, graphs, and hashing; performance tradeoffs of different implementations and asymptotic analysis of running time and memory usage. 


Course Introduction

When we use programming for problem-solving purposes, data must be stored in certain forms, or Data Structures, so that operations on that data will yield a specific type of output. Imagine, for example, that a non-profit is having trouble staying afloat and needs an increase in donation. It decides it wants to keep track of its donors in a program in order to figure out who is contributing and why. You would first need to define the properties that would define those donors: name, address, amount donated, date of donation, and so on. Then, when the non-profit wants to determine how to best reach out to their donors, it can create a model of the average donor that contributes to the non-profit--say, for example, based on size of gift and location--so that it can better determine who is most receptive to its mission. In this case, size of gift and location are the "data” of the donor model. If the non-profit were to use this model, it would be identifying real donors by first generating an abstract donor. This is an example of using Abstract Data Types. Abstract Data Types both take into account the Data Structure (i.e. the way in which data about donors is stored) and provide the necessary operations on that structure. In this course, we will discuss the theoretical and practical aspects of algorithms and Data Structures. We will also learn to implement Data Structures and algorithms in C/C++, analyze those algorithms, and consider both their worst-case complexity and practical efficiency.

This course is comprised of the following units:

  • Unit 1: Abstract Data Types and Arrays in C++
  • Unit 2: Introduction to Stacks and Queues
  • Unit 3: Pointers and References in C++
  • Unit 4: Dynamic Memory Allocation
  • Unit 5: Linked Stacks, Queues, and Lists
  • Unit 6: Algorithm Efficiency 
  • Unit 7: Searching and Sorting Algorithms
  • Unit 8: Hash Tables, Graphs, and Trees


Course Learning Outcomes

Upon successful completion of this course, you will be able to: 

  • discuss the general-purpose nature of Abstract Data Types (ADTs):
    • describe ADTs; and
    • summarize specific data types (SDTs) within the context of ADTs.
  • explain elementary data types within the wider context of ADTs:
    • interpret the three elementary data types that are native to C/C++ (scalars, vectors, multi-dimensional arrays) within the context of ADTs;
    • demonstrate the use of scalars, vectors, and multi-dimensional arrays;
    • show how scalars are used to build vectors; and
    • show how vectors can be used to build multi-dimensional arrays.
  • identify the five basic Composite Data Types (CDTs):
    • define the five basic CDTs (lists, stacks, queues, trees, graphs, hash tables);
    • relate an application's requirements to an appropriate CDT; and
    • implement solutions to applications requiring one or more of the five basic CDTs.
  • examine algorithms to determine their computer-resource utilization:
    • define Big-O analysis;
    • explain why Big-O analysis is important to algorithm design and selection;
    • distinguish between Big-O analysis, counting program steps, and counting lines of executable code; and
    • organize search and sort algorithms according to their Big-O resource utilization growth curve.
  • contrast sequential and binary search techniques:
    • define sequential search and name its primary attributes;
    • define binary search and name its primary attributes; and
    • compare the resource utilization curves of sequential and binary search as data-size increases.

Throughout this course, you'll also see related learning outcomes identified in each unit. You can use the learning outcomes to help organize your learning and gauge your progress.


Course Materials

The primary learning materials for this course are readings, lectures, video tutorials, and other resources.

All course materials are free to access, and can be found through the links provided in each unit and subunit of the course. Pay close attention to the notes that accompany these course materials, as they will instruct you as to what specifically to read or watch at a given point in the course, and help you to understand how these individual materials fit into the course as a whole. You can also access a list all of the materials used in this course by clicking on Resources in the course's "Activities" menu.


Evaluation and Minimum Passing Score

Only the final exam is considered when awarding you a grade for this course. In order to pass this course, you will need to earn a 70% or higher on the final exam. Your score on the exam will be tabulated as soon as you complete it. If you do not pass the exam on your first attempt, you may take it again as many times as needed, following a 7-day waiting period between each attempt. Once you have successfully passed the final exam you will be awarded a free Saylor Certificate of Completion.

There are also 8 unit assessments and other types of quizzes in this course. These are intended to help you to gauge how well you are learning and do not factor into your final course grade. You may retake all of these as many times as needed to feel that you have an understanding of the concepts and material covered. You can locate a full list of these sorts of assessments by clicking on Quizzes in the course's "Activities" menu.


Earning College Credit

This course provides students the opportunity to earn actual college credit. It has been reviewed by Brandman University and can applied as credit towards a degree by students who are currently enrolled or plan to enroll at Brandman. You can read more about this special program here.

If you are seeking to earn college credit at Brandman, you must take and pass the version of the exam titled "Proctored Final Exam." That exam will be password protected.

Note: There is a 14-day waiting period between attempts of the Direct Credit final exam. There is no imposed wait period between attempting the non-credit certificate-bearing exam and the credit exam. Some credit exams have a maximum number of attempts allowed, which will be detailed on the exam's instructions page.


Tips for Success

CS201: Elementary Data Structures is a self-paced course in which you the learner determines when you will start and when you will complete the course. There is no instructor or predetermined schedule to follow. While learning styles can vary considerably and any particular student will take more or less time to learn or read, we estimate that the "average" student will take 38 hours to complete this course. We recommend that you work through the course at a pace that is comfortable for you and allows you to make regular (daily, or at least weekly) progress. It's a good idea to also schedule your study time in advance and try as best as you can to stick to that schedule.

Learning new material can be challenging, so below we've compiled a few suggested study strategies to help you succeed:

  • Take notes on the various terms, practices, and theories as you read. This can help you differentiate and contextualize concepts and later provide you with a refresher as you study.
  • As you progress through the materials, take time to test yourself on what you have retained and how well you understand the concepts. The process of reflection is important for creating a memory of the materials you learn; it will increase the probability that you ultimately retain the information.
  • Although you may work through this course completely independently, you may find it helpful to connect with other Saylor Academy students through the discussion forums. You may access the discussion forums at


Suggested Prerequisites

In order to take this course you should:

Technical Requirements

This course is delivered fully online. You will be required to have access to a computer or web-capable mobile device and have consistent access to the internet to either view or download the necessary course resources and to attempt any auto-graded course assessments and the final exam.

  • To access the full course including assessments and the final exam, you will need to be logged into your Saylor Academy account and enrolled in the course. If you do not already have an account, you may create one, free of charge, here. Although you can access some course resources without being logged into your account, it's advised that you log in to maximize your course experience. For example, some of the accessibility and progress tracking features are only available when you are logged in.
  • If you plan to attempt the optional credit recommended final exam that accompanies this course, then you will also need access to a webcam enabled computer. A webcam is needed so that our remote proctoring service can verify your identity.

For additional technical guidance check out Saylor Academy's tech-FAQ and the Moodle LMS tutorial.



There is no cost to access and enroll in this course. All required course resources linked throughout the course, including textbooks, videos, webpages, activities, etc are accessible for no charge. This course also contains a free final exam and course completion certificate.

This course does contain an optional final exam that will provide students an opportunity to earn college credit. Access to the exam itself is free, though it does require the use of a proctoring service for identity verification purposes. The cost for proctoring is $25 per session.

Last modified: Wednesday, August 8, 2018, 4:11 PM