Data Structures & Algorithms
The computer-science core every strong engineer shares: the essential data structures, the algorithms that operate on them, complexity analysis, and the problem-solving patterns that interviews and real systems demand.
The core that separates strong engineers
Two developers can both make a feature work, but the one who understands data structures and algorithms will write code that scales, choose the right approach the first time, and reason about performance rather than guessing. This program builds that computer-science core deliberately, because it is what separates an engineer who can only follow patterns from one who can solve genuinely new problems.
It is also what technical interviews test, for better or worse, so this program serves a double purpose: it makes you a stronger engineer and it prepares you for the interviews that gate the best roles. The two goals reinforce each other, because the same understanding that impresses an interviewer is what lets you build efficient systems.
The double payoff is real: the same understanding that lets you ace a technical interview is what lets you build systems that scale, so the time invested here serves both your job search and your engineering ability at once.
Structures and algorithms
The program starts with complexity analysis, Big-O, so that every later choice can be reasoned about rather than guessed. From there it builds the essential structures: arrays and dynamic arrays, linked lists, stacks and queues, hash tables, then trees, heaps, and graphs, each taught with how it works internally and when to reach for it.
On top of the structures come the algorithms: sorting and searching, recursion and divide-and-conquer, greedy algorithms, and dynamic programming, the technique that intimidates many developers until it is taught clearly. The emphasis throughout is on understanding why each works and what it costs, not memorizing implementations.
Understanding a structure from the inside, how a hash table resolves collisions, why a balanced tree stays fast, is what lets you choose correctly under pressure rather than reaching for whatever you used last time. That judgment is what the program builds.
Problem-solving patterns
Real skill in this area is less about knowing every algorithm and more about recognizing which pattern a problem fits. The program teaches those patterns explicitly: two pointers and sliding window, graph algorithms and shortest paths, backtracking, and how to recognize which applies to an unfamiliar problem. This pattern recognition is what makes interviews feel tractable rather than terrifying.
The program is language-agnostic, so you work in whatever language you are strongest in, and it culminates in a problem-solving capstone that pulls the patterns together. The result is a documented solutions portfolio that demonstrates not just that you can code, but that you can think.
Pattern recognition is the meta-skill: once you can name the shape of a problem, the solution follows, which is why the program teaches the patterns explicitly rather than leaving you to rediscover them problem by problem.
See the method, not just the topic
A representative worked example from the program, so you can see the level of concreteness the curriculum works at.
# Longest substring without repeating characters.
# Sliding window: expand the right edge, shrink the left when
# a repeat appears. Each character enters and leaves once.
def longest_unique(s):
seen = {} # char -> last index seen
start = best = 0
for i, ch in enumerate(s):
if ch in seen and seen[ch] >= start:
start = seen[ch] + 1 # move window past the repeat
seen[ch] = i
best = max(best, i - start + 1)
return best
# Time O(n): each index advances start or right at most once.
# Space O(k): k = size of the character set in the window.
# Recognizing this as a "sliding window" problem is the skill;
# the code follows once the pattern is named.The full syllabus
Four modules of five chapters each, sequenced so the material builds cumulatively. Each chapter carries a note on what it teaches.
Module 1Complexity and core structures
- 01Big-O and analyzing complexityReasoning about how code scales with Big-O. This is the vocabulary every performance discussion uses.
- 02Arrays and dynamic arraysArrays and the cost of their operations. You learn when an array is the right and wrong choice.
- 03Linked listsLinked lists and when they beat arrays. Linked lists teach pointers and trade-offs together.
- 04Stacks and queuesStacks and queues and their uses. These simple structures appear inside countless algorithms.
- 05Hash tables and how they workHash tables and why lookups are fast. Hashing is the trick behind most fast lookups.
Module 2Trees, heaps, and graphs
- 06Trees and binary search treesTrees and binary search trees. Trees model hierarchy and enable fast search.
- 07Balanced trees and their trade-offsBalanced trees and their trade-offs. Balancing is what keeps tree operations fast.
- 08Heaps and priority queuesHeaps and priority queues. Heaps power scheduling and shortest-path work.
- 09Graphs and their representationsGraphs and how to represent them. Graphs model networks, maps, and dependencies.
- 10Traversals: BFS and DFSBreadth-first and depth-first traversal. BFS and DFS are the backbone of graph problems.
Module 3Essential algorithms
- 11Sorting algorithms and their trade-offsThe sorting algorithms and their trade-offs. You learn why one sort beats another in context.
- 12Searching and binary searchBinary search and searching efficiently. Binary search is deceptively powerful once mastered.
- 13Recursion and divide and conquerRecursion and divide-and-conquer. Recursion becomes a tool rather than a puzzle.
- 14Greedy algorithmsGreedy algorithms and when they work. Greedy is elegant when it applies, and a trap when it does not.
- 15Dynamic programmingDynamic programming, made clear. Dynamic programming stops being intimidating.
Module 4Problem-solving patterns
- 16Two pointers and sliding windowThe two-pointer and sliding-window patterns. These patterns solve a huge share of interview questions.
- 17Graph algorithms: shortest pathsShortest-path graph algorithms. Shortest paths appear in routing, games, and networks.
- 18BacktrackingBacktracking through a search space. Backtracking systematically explores possibilities.
- 19Pattern recognition for interviewsRecognizing which pattern a problem fits. Naming the pattern is most of the solution.
- 20The problem-solving capstonePulling the patterns together on hard problems. The capstone proves you can think, not just code.
How the program is taught
The program pairs clear explanation with a large bank of problems, because data structures and algorithms are learned by solving, not just reading. Each structure and algorithm is explained with how it works and what it costs, then immediately exercised on problems that force you to apply it.
It is deliberately language-agnostic, so you work in whatever language you are strongest in and focus on the ideas rather than the syntax. This keeps the emphasis where it belongs, on choosing the right approach and reasoning about complexity, which are the skills that transfer to every language and every system.
Prerequisites and pace
The program assumes you can already write basic code in some language, so it pairs naturally with or after a first language program. It does not assume any prior computer-science background, building complexity analysis and every structure from the ground up.
The pace rewards consistency over cramming. Working a few problems regularly, and revisiting the patterns, builds the pattern recognition that the subject is really about. The program is structured to support that steady practice rather than a single intense push that fades.
What makes this program different
Many algorithm courses present a catalog of techniques without teaching the meta-skill of recognizing which one a problem needs. This program teaches the patterns explicitly, two pointers, sliding window, backtracking, dynamic programming, and how to spot which applies, because that recognition is what actually makes problems solvable.
The other distinction is the dual purpose held honestly in view: this material makes you a better engineer and prepares you for interviews, and the program serves both without pretending they are the same. You come away able to build efficient systems and to demonstrate that ability under interview conditions.
What you will be able to do
- Analyze the time and space complexity of code
- Choose the right data structure for a problem
- Implement and reason about the core algorithms
- Recognize and apply common problem-solving patterns
- Approach technical interviews with confidence
Who should take it
- Developers preparing for technical interviews
- Self-taught engineers who skipped the CS core
- Students strengthening their fundamentals
- Anyone who wants to write efficient, correct code
Common questions
People often ask whether this matters if they just want to build web apps. It does: the difference between code that scales and code that crawls is usually an algorithmic choice, and understanding complexity is what lets you make that choice deliberately rather than discovering the problem in production.
Another common question is how much to grind. The answer is enough to internalize the patterns, not to memorize every problem. Once you can recognize the shape of a problem, you can derive the solution, which is far more durable than memorizing solutions you will forget.
Where it leads
Strong data-structures-and-algorithms skills open the door to the roles that gate on technical interviews, which includes most of the strongest engineering positions. The documented solutions portfolio the program produces is concrete evidence of that capability.
Beyond interviews, this is the foundation that makes senior engineering possible. The ability to reason about performance and choose the right approach is what distinguishes engineers who can tackle genuinely hard problems, and it is a prerequisite for the systems work later in the track.
How it fits the journey
This is the second foundation program, and it is designed to accompany the core-language programs rather than block them. Many learners take it alongside Python, Java, or C++, applying each new structure in the language they are learning.
Its performance thinking connects especially well to the C and C++ program, where cache-awareness and data layout make algorithmic cost tangible, and to Application Engineering, where system-level decisions rest on the same complexity reasoning.
What you build and keep
Work a curated set of problems that exercises every major structure and pattern, then assemble a documented solutions portfolio that explains the approach, the complexity, and the trade-offs for each, the kind of portfolio that demonstrates real problem-solving skill in an interview.
Format: Self-paced with a large problem bank and worked solutions; language-agnostic.
Run this program for your team
Every program can be delivered as a private, tailored cohort for your organization, aligned to your systems, policies, and career frameworks.
Scope a corporate cohortFrequently asked questions
What is the Data Structures & Algorithms program?
The computer-science core every strong engineer shares: the essential data structures, the algorithms that operate on them, complexity analysis, and the problem-solving patterns that interviews and real systems demand.
Who is this program for?
It suits developers preparing for technical interviews, along with others described on this page.
How is it delivered?
Self-paced with a large problem bank and worked solutions; language-agnostic.
Is there a project or capstone?
Work a curated set of problems that exercises every major structure and pattern, then assemble a documented solutions portfolio that explains the approach, the complexity, and the trade-offs for each, the kind of portfolio that demonstrates real problem-solving skill in an interview.
How does this fit the wider journey?
This is the second foundation program. It is language-agnostic and pairs with any of the core-language programs that follow, giving them the computer-science depth that turns a coder into an engineer.
Can my organization run this as a private cohort?
Yes. Every program can be delivered as a tailored corporate cohort. Contact us to scope it.