add course.adoc for gds-applied-algorithm

Author: adam-cowley

asciidoc/courses/gds-applied-algorithms/course.adoc

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+= Applied Algorithms in GDS
+:usecase: recommendations
+:categories: data-scientist:2, data-analysis:11, intermediate:4, analytics:2
+:duration: 4-5 hours
+:caption: Apply graph algorithms to solve real-world industry problems
+:status: draft
+:key-points: Root cause analysis, Fraud detection, Supply chain optimization, Citation networks, Node embeddings
+:graph-analytics-plugin: true
+
+== Course Description
+
+This course demonstrates how to apply GDS algorithms to solve real-world industry problems.
+
+You've learned the fundamentals of graph projections, algorithm execution, and configuration in the link:https://graphacademy.neo4j.com/courses/gds-fundamentals/[Getting Started with GDS^] course.
+Now you'll see these techniques solve actual challenges across manufacturing, fraud detection, logistics, research, and machine learning.
+
+Each module focuses on a different industry use case, showing not just *how* to run algorithms, but *when* and *why* professionals choose specific approaches.
+You'll work with realistic datasets, implement complete analytical workflows, and understand the business reasoning behind each technique.
+
+By the end of this course, you'll be able to design and implement graph-based solutions for complex industry problems.
+
+The course automatically creates a new `movie recommendations` sandbox within link:https://sandbox.neo4j.com/?usecase=recommendations[Neo4j Sandbox] that you will use throughout the course.
+
+
+== Prerequisites
+
+This course is intended for analysts and data scientists who have:
+
+* Completed link:https://graphacademy.neo4j.com/courses/gds-fundamentals/[Getting Started with GDS^] or equivalent experience
+* Understanding of graph projections (monopartite, bipartite, multipartite)
+* Familiarity with algorithm execution modes (stream, write, mutate)
+* Basic knowledge of algorithm configuration (orientation, weights)
+
+
+== Duration
+
+4-5 hours
+
+
+== What you will learn
+
+* **Manufacturing optimization:** Use centrality and community detection for root cause analysis in production systems
+* **Fraud detection:** Build network-based fraud identification systems using graph patterns
+* **Supply chain logistics:** Optimize routes and logistics with pathfinding algorithms
+* **Citation networks:** Map research influence and identify key papers using centrality measures
+* **Node embeddings:** Create structural representations for machine learning pipelines
+
+
+// [.includes]
+// == This course includes
+
+// * [lessons]#20+ lessons# across five industry-focused modules
+// * [challenges]#Hands-on challenges# with realistic datasets
+// * [quizes]#Validation exercises# integrated throughout
+// * Complete analytical workflows from problem definition to implementation
+