Sign In to Download the Syllabus
The Computer Science 251 syllabus is available to enrolled or registered students. Sign in to your UPI Study account to download it instantly — or enroll today to get access.
Once signed in, the syllabus will download automatically. No extra steps.
Data Structure
and Algorithms
The backbone of every great programmer: learn to structure data and solve problems efficiently, the way top engineers actually think.
What You'll Learn
A full semester of data structure and algorithms — completed at your own pace, with no deadlines or fixed class times.
What You'll Be Able to Do
- Implement and use Java programming by configuring a coding environment, constructing Java statements, implementing loops, and effectively debugging Java programs
- Discuss object-oriented design principles, including inheritance, polymorphism, and encapsulation, and apply these concepts to design and implement complex Java programs
- Master data structures such as arrays, linked lists, trees, and queues, understanding their practical applications and implementing them efficiently in Java
- Analyze algorithms, including recursion and sorting algorithms, evaluate their performance, and apply to solve real-world problems
- Develop skills in text processing, graph data structures, memory management, and external memory techniques, ensuring they are well-equipped to handle diverse programming challenges in Java
- Design, implement, and analyze Java programs effectively via assignments and practical applications.
Major Course Topics
Course chapters and lessons
Data Structure and Algorithms has 17 chapters across 56 instructional hours, as listed in the course syllabus. Expand any chapter to see the lessons inside it.
- Chapters
- 17
- Lessons
- 87
- Instructional hours
- 56 hrs
- Semester hours
- 3
Chapter 1
Getting Started — Your First Steps in Java Programming
- 1.1 Getting Started — Installing, Configuring, and Running Your First Java Program
- 1.2 How Java Reads and Executes Instructions Step by Step
- 1.3 Automating Repetitive Tasks Using Conditional Looping Mechanisms
- 1.4 The Complete Cycle of Writing, Testing, and Fixing Java Code
Chapter 2
Writing Clean and Logical Code Before You Build
- 2.1 Designing Program Logic Before Writing Any Code
- 2.2 Documenting Your Code for Clarity and Team Collaboration
Chapter 3
Thinking in Objects — Core Principles of Reusable Software Design
- 3.1 The Five Pillars That Shape How We Design Reusable Software
- 3.2 Modeling Real-World Entities as Programmable Objects
- 3.3 How Child Classes Borrow and Extend Behavior from Parent Classes
- 3.4 Choosing Between Contracts and Partial Blueprints in Class Design
Chapter 4
From Theory to Code — Abstract Types, Exceptions, and Nested Structures
- 4.1 Defining Data Behavior Without Committing to Implementation Details
- 4.2 Turning Theoretical Data Definitions into Working Java Code
- 4.3 Anticipating and Recovering from Runtime Errors Gracefully
- 4.4 Grouping Helper Classes Inside Other Classes Without Object Binding
- 4.5 Creating Classes That Live Inside and Depend on Their Enclosing Object
Chapter 5
Fixed-Size Storage — How Arrays Hold and Organize Data
- 5.1 How Contiguous Memory Blocks Store Collections of Elements
- 5.2 Rearranging Elements Within a Fixed-Size Collection
- 5.3 Organizing Data Across Rows, Columns, and Higher Dimensions
- 5.4 Determining Whether Two Collections Hold Identical Content
- 5.5 Creating Independent Copies of Array-Based Collections
Chapter 6
Flexible Storage — Building Chains of Connected Nodes
- 6.1 Why Connecting Individual Nodes Solves Problems Arrays Cannot
- 6.2 Constructing a One-Directional Chain of Connected Nodes
- 6.3 Linking the Last Node Back to the First for Continuous Traversal
- 6.4 Enabling Forward and Backward Navigation Between Connected Nodes
Chapter 7
Measuring the Efficiency and Cost of Computational Solutions
- 7.1 What Separates a Good Solution from a Poor One in Programming
- 7.2 Techniques for Evaluating How Fast and How Much Memory a Solution Uses
- 7.3 Constant, Logarithmic, Linear, and N-Log-N — The Functions Behind Efficient Algorithms
- 7.4 Quadratic, Cubic, and Exponential — The Functions That Signal Expensive Algorithms
- 7.5 Expressing Worst-Case Performance Using Standardized Growth Categories
- 7.6 Logical Methods for Proving That an Algorithm Works Correctly
Chapter 8
Solving Problems Through Self-Calling Methods
- 8.1 When a Function Should Call Itself vs. When a Loop Does the Job Better
- 8.2 How a Method Breaks a Problem into Smaller Versions of Itself
- 8.3 Calculating the True Cost of Methods That Call Themselves
- 8.4 Applying Self-Calling Techniques to Build and Navigate Complex Structures
Chapter 9
Finding What You Need — Search Strategies from Simple to Advanced
- 9.1 Checking Every Element One at a Time Until the Target Appears
- 9.2 Eliminating Half the Remaining Elements with Each Comparison
- 9.3 Splitting a Sorted Collection in Half Repeatedly Using Self-Calling Methods
- 9.4 Starting Small and Doubling the Search Range Until the Target Is Found
Chapter 10
Processing Data in Controlled Order — Stacks, Queues, and Deques
- 10.1 Processing the Most Recently Added Element First — Last-In, First-Out Behavior
- 10.2 Processing the Earliest Added Element First — First-In, First-Out Behavior
- 10.3 Adding and Removing Elements from Both Ends of a Sequence
- 10.4 Real-World Stack Applications — Expression Evaluation, Undo Systems, and Call Tracking
- 10.5 Real-World Queue Applications — Task Scheduling, Buffering, and Breadth-First Traversal
Chapter 11
Dynamic Collections — Resizable Lists, Iterators, and Java's Framework
- 11.1 Collections That Automatically Expand as More Elements Are Added
- 11.2 Accessing and Manipulating Elements Based on Their Location in a Sequence
- 11.3 Visiting Every Element in an Unordered Collection One at a Time
- 11.4 A Tour of Java's Pre-Built Tools for Managing Groups of Objects
- 11.5 Rearranging Position-Based Collections into a Desired Order
Chapter 12
Organizing Information in Parent-Child Hierarchies
- 12.1 Why Hierarchical Structures Are Ideal for Representing Nested Relationships
- 12.2 Restricting Each Parent to at Most Two Children for Efficient Operations
- 12.3 Choosing the Right Tree — Comparing Layouts and Their Real-World Applications
Chapter 13
Serving the Most Important Element First — Priority Queues and Heaps
- 13.1 Why Some Waiting Lines Need to Prioritize Certain Elements Over Others
- 13.2 Constructing a Priority-Based Waiting Line in Java Step by Step
- 13.3 Using a Complete Binary Hierarchy to Maintain Priority Order Efficiently
- 13.4 Leveraging Priority-Based Structures to Arrange Data in Sorted Order
Chapter 14
Instant Lookups — Mapping Keys to Values Through Hashing
- 14.1 Pairing Unique Identifiers with Their Corresponding Values for Fast Retrieval
- 14.2 Designing Hash Functions That Distribute Keys Evenly Across Available Slots
- 14.3 The Array-Based Storage Structure That Makes Near-Instant Lookup Possible
- 14.4 Redistributing Entries Fairly When Multiple Keys Compete for the Same Slot
- 14.5 Maintaining Key-Value Pairs in a Naturally Ordered Arrangement
- 14.6 Storing Multiple Entries at the Same Position Using Linked Buckets
- 14.7 Building Real Applications That Rely on Key-to-Position Mapping
Chapter 15
Keeping Searchable Structures Balanced — Search Trees and Self-Adjusting Nodes
- 15.1 Arranging Nodes So That Left Children Are Smaller and Right Children Are Larger
- 15.2 Automatically Rebalancing After Every Insertion and Deletion
- 15.3 Allowing Nodes to Hold Multiple Keys and Branch in More Than Two Directions
- 15.4 Comparing Balanced Search Approaches and Applying Them Through Hands-On Exercises
Chapter 16
Bringing Order to Chaos — Sorting Strategies, Collisions, and Text Processing
- 16.1 Two Elementary Sorting Approaches — Bubble Sort and Selection Sort Compared
- 16.2 Splitting a Collection in Half, Sorting Each Piece, Then Combining the Results
- 16.3 Choosing a Pivot Element and Partitioning Everything Around It
- 16.4 Taking One Element at a Time and Sliding It into Its Correct Position
- 16.5 How Different Sorting Strategies Compare in Speed, Memory, and Stability
- 16.6 Finding Alternative Slots When the Intended Position Is Already Occupied
- 16.7 Applying Sorting and Selection Techniques to Real-World Datasets
- 16.8 Why Sequences of Characters Deserve Their Own Specialized Data Handling
- 16.9 Techniques for Locating a Specific Word or Pattern Inside a Larger Text
- 16.10 Storing Large Vocabularies Efficiently Using Prefix-Based Tree Structures
- 16.11 Reducing File Sizes by Replacing Frequent Characters with Shorter Codes
Chapter 17
Networks, Connections, and Managing Data Across Memory Layers
- 17.1 Understanding Graph Theory — Vertices, Edges, Paths, and How Connected Structures Represent Relationships
- 17.2 How Different Connection Types Change the Behavior of a Network
- 17.3 Computing the Cheapest Route Between Two Points in a Weighted Network
- 17.4 Viewing Connected Structures Through the Lens of Discrete Mathematics
- 17.5 Classifying Networks by Their Shape, Density, and Loop Behavior
- 17.6 Solving Real Problems by Modeling Them as Networks of Connections
- 17.7 How Java Allocates, Tracks, and Reclaims Memory Automatically
- 17.8 Why Frequently Used Data Lives in a Faster, Smaller Memory Layer
- 17.9 Navigating Massive Datasets Stored on Disk Using Multi-Level Index Trees
- 17.10 Arranging Data That Is Too Large to Fit in Main Memory All at Once
These chapters come straight from the official course syllabus, which also sets out the graded assignment and the proctored final exam.
How It Works
Three steps from enrollment to transferable college credit — no campus visits, no application required.
Pick & Enroll
Start one or more courses covering lower or upper division requirements — instantly, today.
Earn College Credit
Watch short video lessons, take quizzes and the final exam — all from home, on your schedule.
Transfer Your Credits
Send earned credits to your school and graduate sooner, at a fraction of the cost.
Does My School Accept This Course?
UPI Study courses carry NCCRS recommendations — the same standard recognized by hundreds of cooperating colleges and universities. Check if your school is on our list and move forward with confidence — or schedule a call with our team.
Students Also Enrolled In
Stack multiple courses to build 6, 9, or 12+ transferable college credits — at a fraction of the cost of most campus options.
UPI Study Courses Include
Master data structure and algorithms and build a foundation for a tech career.
The backbone of every great programmer: learn to structure data and solve problems efficiently, the way top engineers actually think.
🎯 Why It Matters
- Gain in-demand technical skills recognized by employers
- Understand systems, security, and infrastructure at a practical level
- Build knowledge that applies across IT roles and industries
- Foundation for certifications and advanced study
💼 In-Demand Careers
- IT Support Specialist
- Systems Administrator
- Network Technician
- Security Analyst
- Database Administrator
- Technical Consultant
🚀 How This Sets You Up
- Complete structured lessons and a comprehensive final exam
- Earn 3 transferable college credit hours
- Recognized by NCCRS cooperating universities globally
- Self-paced — no deadlines, no campus visits required
135,000+ Learners.
Real Results.
From college freshmen to career changers, international students to working adults — here's what our community says.
Refer a friend, earn free courses
Your friend gets 25% off their first purchase, and you earn points when they join and buy. 1 point = $1 toward courses, transcripts and Lifetime. Points are never cash.
See how it works →
See the course before you enroll
A 54-second look at Data Structure and Algorithms.
Common Questions Answered.
Everything you need to know about Data Structure and Algorithms — credits, grading, transfer, and how it compares to traditional options.
What is Data Structure and Algorithms?
What technical background do I need for Data Structure and Algorithms?
Is the Data Structure and Algorithms course self-paced for college credit?
How many college credits is Data Structure and Algorithms worth?
How is the Data Structure and Algorithms course graded? Are there exams?
Will my university accept Data Structure and Algorithms credits from UPI Study?
Why take Data Structure and Algorithms online instead of at a traditional university?
Can international students take Data Structure and Algorithms for college credit?
Is Data Structures & Algorithms a hard course?
What are the prerequisites for Data Structures & Algorithms?
Is this required for most CS degrees?
Where will my college put this credit?
Still have questions? Our team answers within hours — not days.
Book a Free 15-Min Call with UPI Team →We're Here When You Need Us
Book an Advisor Call
Talk directly with our team about how credits transfer and whether your university accepts them.
WhatsApp Support
Quick answers about credits, course selection, pricing, and transfer policies — over WhatsApp.
Transfer Credit Blog
In-depth guides on testing out of courses, transfer credit policies, ACE evaluations, and saving on tuition.
Find My College
Check how your college treats our courses before you enroll.
Why Take College Credits?
Quick answers about transferable online credits, test-out style learning, and NCCRS-recommended options through UPI Study.
Which universities accept these credits?
UPI Study courses carry NCCRS recommendations recognized by hundreds of cooperating colleges and universities across the U.S., Canada, and UK.
Who can take self-paced college courses?
Anyone can enroll — including high school students, incoming college students, adult learners, and students trying to test out of traditional courses.
Why choose this over traditional classes?
Save time and reduce tuition costs. Many students complete prerequisites faster and shorten their time to graduation — at a fraction of the cost.
How many credits can I earn?
This course is NCCRS-recommended for 3 semester credit hours. Transfer as many credits as your university allows — students have transferred anywhere from 3 to 90+ credits depending on their school's policy.
Everything You Need to Succeed
Video-Based Lessons
HD video lectures broken into short, focused modules. Watch at your own pace — pause, rewind, replay any time.
Quizzes & Final Exam
Evaluate your understanding after every section with quizzes and a comprehensive final exam that mirrors real college assessment.
Structured College-Level Course
Structured lessons covering every topic — designed as a standard Data Structure and Algorithms college-level course.
Official Transcript
Complete the course, request your transcript, and transfer 3 credit hours to NCCRS cooperating universities. No separate exam required.
Earn 3 College Credits
for $250.
Self-paced. No deadlines. NCCRS-recommended and recognized by cooperating universities worldwide. Join 135,000+ learners on the UPI Study platform.