Data structure choice
Students learn when to use arrays, lists, stacks, queues, maps, sets, trees, heaps, and graphs.
Advanced CS Support
Data Structures & Algorithms
Learn the core data structures, algorithmic patterns, and runtime reasoning behind advanced computer science.
This course is for students moving beyond introductory programming. Students learn how to choose data structures, reason about efficiency, trace algorithms, and solve harder problems with cleaner design.
Course Overview
A bridge from AP Computer Science A to college-level computer science
Students connect programming knowledge to abstract data types, algorithmic tradeoffs, and the problem-solving habits used in contests and college CS.
Runtime reasoning
Students practice Big-O intuition, nested-loop analysis, recursion cost, and tradeoffs.
Algorithm patterns
Lessons cover searching, sorting, traversal, recursion, graph search, and greedy reasoning.
College readiness
The course supports Advanced Topics in CS, intro college DSA, and competitive programming growth.
Student Fit
Who this course is for
The starting point is adjusted to the student's age, coding background, school workload, and long-term goals.
- Students who completed AP Computer Science A or equivalent Java/Python experience
- Advanced high school students taking data structures or Advanced Topics in CS
- Students preparing for USACO Silver, Codeforces, or LeetCode-style DSA
- College students in intro data structures courses
- Students who want stronger algorithmic thinking before research or project work
Prerequisites
What students should know before starting
A bridge track is available when a student needs foundations before the main curriculum.
- Comfort with one programming language such as Java, Python, or C++
- Understanding of loops, methods/functions, arrays/lists, and basic classes
- Willingness to trace algorithms and explain time complexity
- AP Computer Science A completion is helpful but not required for a custom track
Curriculum
Data structures and algorithms curriculum
The sequence can be taught in Java, Python, or C++ depending on school, contest, or college goals.
1
Algorithm Analysis
Students learn how to compare approaches and reason about efficiency.
- Big-O notation
- Best/worst/average cases
- Nested loops
- Recurrence intuition
- Space complexity
- Constraint analysis
2
Linear Structures
Students deepen list-based thinking and learn abstract data types.
- Arrays
- ArrayList/list
- Linked lists
- Stacks
- Queues
- Deque patterns
3
Hashing and Maps
Students learn fast lookup patterns and common frequency/counting techniques.
- Sets
- Maps/dictionaries
- Hash tables
- Collision intuition
- Frequency maps
- Duplicate detection
4
Recursion and Sorting
Students connect recursive thinking to classic algorithms.
- Recursive tracing
- Backtracking basics
- Selection sort
- Insertion sort
- Merge sort
- Quick sort intuition
5
Trees and Heaps
Students learn hierarchical structures and priority-based processing.
- Binary trees
- Tree traversals
- Binary search trees
- Heaps
- Priority queues
- Heap sort
6
Graphs and Problem Solving
Students build graph vocabulary and traversal habits.
- Graph representation
- DFS
- BFS
- Shortest path intuition
- Topological ideas
- Connected components
Practice
DSA practice themes
Practice combines implementation, tracing, runtime analysis, and problem selection based on student goals.
Runtime drills
Linked-list tracing
Stack and queue tasks
Hash map problems
Recursion/backtracking
Tree traversals
BFS/DFS
Sorting analysis
Outcomes
By the end of this course, students will be able to
- Choose appropriate data structures for common problem types
- Implement and trace lists, stacks, queues, maps, sets, trees, heaps, and graphs
- Analyze time and space complexity at an AP Computer Science A-to-college bridge level
- Explain sorting, searching, recursion, and traversal algorithms
- Solve harder coding problems with a structured plan
- Prepare for college DSA, Advanced Topics in CS, USACO, Codeforces, or LeetCode-style work
Learning Format
How sessions are structured
- Concept lessons
- Live implementation
- Tracing practice
- Complexity review
- Problem-set coaching
- Mock quiz or contest review
Why Code Scholars
Support that builds real understanding
Students get direct coaching, careful correction, and a course path that turns practice into visible progress.
Deeper Reasoning
Students learn why an algorithm is appropriate, not just how to code it.
Bridge to College CS
The course fills the gap between AP Computer Science A and college data structures.
Contest Support
Patterns also support USACO, Codeforces, and LeetCode-style practice.
Language Flexibility
Implementation can be taught in Java, Python, or C++.
Clean Explanations
Students practice explaining tradeoffs and correctness clearly.
Confidence With Harder Problems
Complex topics become manageable through small repeated patterns.
Start Data Structures & Algorithms
Schedule a consultation to discuss current level, goals, timeline, and the best starting point.