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 Student Handbook.
Course Description
Survey basic abstract data types, their associated algorithms, and how they are implemented. Topics discussed include the structures of 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 donations. 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 its donors, it can create a model of the average donor that contributes to the non-profit – say, for example, based on the size of the gift and the location – so that it can better determine who is most receptive to its mission. In this case, the size of the gift and the location are the "data" of the donor model. If the non-profit were to use this model, it would identify 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 (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 includes 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 will also see learning outcomes in each unit. You can use those learning outcomes to help organize your studies and gauge your progress.
Course Materials
This course's primary learning materials are articles, lectures, and videos.
All course materials are free to access and can be found in each unit of the course. Pay close attention to the notes that accompany these course materials, as they will tell you what to focus on in each resource and will help you understand how the learning materials fit into the course as a whole. You can also see a list of all the learning materials in this course at this link.
Some parts of this course may have been created or reviewed with the support of artificial intelligence (AI). To make sure you receive accurate, high-quality, and academically sound learning materials, all AI-assisted content is carefully checked and approved by Saylor Academy's faculty and subject matter experts.
Evaluation and Minimum Passing Score
Only the final exam is considered when awarding you a grade for this course. To pass this course, you will need to earn a grade of 70% or higher on the final exam.
Your score on the exam will be calculated as soon as you complete it. Be sure to study in between each attempt! If you do not pass the exam, you will not complete this course or receive a certificate of completion. You can attempt the exam as many times as you want.
There are end-of-unit assessments in this course that are designed to help you study and do not factor into your final course grade. You can take them as many times as you want until you understand the concepts they cover.
You can see all of these assessments at this link.
Continuing Education Credits
The certificate earned by passing this self-paced course displays the program hours you completed and continuing education credits (CEUs). CEUs document successful completion of courses that are designed to improve the knowledge and skills of working adults. Many industries value CEUs, and now your certificate reflects them clearly, and they may be used to support career advancement or to meet professional licensing standards. This course contains 3.8 CEUs.
Tips for Success
CS201: Elementary Data Structures is a self-paced course, meaning you can decide when to start and complete the course. We estimate the "average" student will take 38 hours to complete. We recommend studying at a comfortable pace and scheduling your study time in advance.
Learning new material can be challenging, so here are a few study strategies to help you succeed:
- Take notes on terms, practices, and theories. This helps you understand each concept in context and provides a refresher for later study.
- Test yourself on what you remember and how well you understand the concepts. Reflecting on what you've learned improves long-term memory retention.
Technical Requirements
This course is delivered entirely online. You will need access to a computer or web-capable mobile device and consistent internet access to view or download resources and complete auto-graded assessments and the final exam.
To access the full course, including assessments and the final exam, log into your Saylor Academy account and enroll in the course. If you don't have an account, you can create one for free here. Note that tracking progress and taking assessments require you to log in.
For more details and guidance, please review our complete Technical Requirements and our student Help Center.
Optional Saylor Academy Mobile App
You can access all course features directly from your mobile browser, but if you have limited internet connectivity, the Saylor Academy mobile app provides an option to download course content for offline use. The app is available for iOS and Android devices.
Fees
This course is entirely free to enroll in and access. All course materials, including textbooks, videos, webpages, and activities, are available at no charge. This course also contains a free final exam and a free course completion certificate.