Computer Science
Programming Fundamentals Challenges
Programming Fundamentals challenges put you inside the work of writing code that is correct, readable and fast. You'll build core skills in functions & data structures, object-oriented design and design patterns, work in Python or JavaScript, and learn to do code reading and refactoring the way teams expect.
From there you'll tackle the harder edges — algorithm analysis, complexity analysis, graph algorithms and generics & type systems — pushing into performance engineering, low-latency programming patterns and systems-language proficiency (Go, Rust, C++). Each challenge you solve earns a verified credential you can share with recruiters.
- CodeSeniorNew
Bulk Synchronous Parallel PageRank for a Web-Graph Lab
Working from the lab situation record (`lab-case-file`), implement 25 iterations of PageRank under a Bulk Synchronous Parallel (BSP) model using either Apache Spark with GraphX …
- Parallel Algorithms
- Bsp
- Graph Algorithms
Open coursework - CodeSeniorNew
Cache-Optimize a Graph-Analytics Kernel for a Social Platform
Receive the existing kernel (C++ + OpenMP, around 1,200 lines), the 1.8B-edge graph (CSR format, around 14GB), and target hardware (dual-socket AMD EPYC 9354, 384GB DDR5, 256MB …
- Cache Optimization
- Graph Algorithms
- Memory Hierarchy
Performance Engineering of Software Systems - CodeIntermediateNew
GPU-Parallel Graph Coloring for an EDA Tools Vendor
Implement Jones-Plassmann graph coloring in CUDA (or HIP if AMD hardware available). Input: a 12-million-node graph in CSR format (compressed sparse row). Output: a valid colori…
- Parallel Algorithms
- Gpu Programming
- Cuda
Parallel and Distributed Algorithms - CodeSeniorNew
Min-Cost Max-Flow for Cloud Workload Placement
Receive 30 days of anonymized placement requests (workload CPU/memory shape, affinity rules), zone capacity per day, and cross-zone bandwidth costs. Model the placement as a min…
- Network Flows
- Algorithm Analysis
- Graph Algorithms
Advanced Algorithms Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeBeginnerNew
Design an Effort-Aware Bike Routing Feature for Lisbon
Working only from the provided materials — the Lisbon street-graph edges (lisbon-bike-graph), the matching node coordinates and elevations (lisbon-graph-nodes), the 50 origin-de…
- Graph Algorithms
- Dijkstra
- Algorithm Analysis
Open coursework
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
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