| Date | Slides | Week and topic | Homework and logistics |
|---|---|---|---|
| 9/3 |
Week주차 1
Course intro, dev environment setup, NoSQL vs. SQL
Course intro, dev environment setup, NoSQL vs. SQL
| Install MongoDB Community + free Atlas account, Compass, mongosh, driver (Python/Node), VS Code extension; diagnostic Kahoot
| |
| 9/10 |
Week주차 2
Introduction to MongoDB & Architecture
Introduction to MongoDB & Architecture
📖 Guide Ch.1–2 | Document model, BSON, replica sets
| |
| 9/17 |
Week주차 3
Developer Tools
Developer Tools
📖 Guide Ch.3 | Compass, mongosh, drivers, AI-assisted IDE workflows
| |
| 9/24🔴 No Class휴강 |
No Class Chuseok!~
No Class Chuseok!~
| ||
| 10/1 |
Week주차 4
Data Modeling & Index Optimization
Data Modeling & Index Optimization
📖 Guide Ch.4 | Embedding vs. referencing, schema patterns
| |
| 10/8 |
Week주차 5
Queries
Queries
📖 Guide Ch.5 | CRUD, aggregation pipeline basics
| |
| 10/15 |
Week주차 6
Database Operations & Security
Database Operations & Security
📖 Guide Ch.6–7
| Admin, backup/restore, auth, RBAC, encryption
| |
| 10/22📝 Exam시험 |
Midterm Test Midterm Test | ||
| 10/29 |
Week주차 7
MongoDB Atlas & Atlas Search (preview)
MongoDB Atlas & Atlas Search (preview)
📖 Guide Ch.8–9 | Cloud deployment; Individual Project due
| |
| 11/5 |
Week주차 8
Atlas Vector Search & Intro to RAG
Atlas Vector Search & Intro to RAG
📖 Guide Ch.9 (deep dive)
| Embeddings, vector indexes, RAG architecture; Team Project proposals due
| |
| 11/12 |
Week주차 9
Building a RAG Pipeline
Building a RAG Pipeline
📖 Guide Ch.9 + supplementary | Hands-on: LangChain/LlamaIndex + MongoDB as vector store
| |
| 11/19 |
Week주차 10
Performance-Oriented Schema Design & Indexing at Scale
Performance-Oriented Schema Design & Indexing at Scale
📖 HP Ch.2–3 | Apply a performance lens to the RAG app's schema
| |
| 11/26 |
Week주차 11
Aggregations for Analytics & Real-Time Pipelines
Aggregations for Analytics & Real-Time Pipelines
📖 HP Ch.4, 8 | Aggregation framework; Change Streams for event-driven ingestion; Progress checkpoint due
| |
| 12/3 |
Week주차 12
Scaling for Big Data: Replication & Sharding
Scaling for Big Data: Replication & Sharding
📖 HP Ch.5–6 | Team project work session
| |
| 12/10 |
Week주차 13
Team Project Presentations
Team Project Presentations
| Demo + technical report due
| |
| 12/17📝 Exam시험 |
Final Test Final Test | ||
- No formal prerequisites. Curiosity required. 공식 선수 과목 없음. 호기심 필수.
Course Objectives:
- Master core MongoDB data modeling, querying, and administration skills to a production-ready level.
- Understand how MongoDB’s document model and Atlas Vector Search support modern AI applications, specifically Retrieval-Augmented Generation (RAG).
- Apply performance and scaling principles (indexing, sharding, replication, real-time pipelines) to realistic, high-volume workloads.
- Design, build, and present a complete AI-powered application with a MongoDB backend.
Key Topics:
- Part 1, Weeks 1–8 (Foundations): document model & architecture, developer tooling, schema/index design, CRUD & aggregation queries, administration, security, Atlas cloud deployment.
- Part 2, Weeks 9–15 (AI & Big Data): Atlas Vector Search, RAG architecture, performance-oriented schema design at scale, real-time pipelines (Change Streams), horizontal scaling (replication/sharding), monitoring.
Course Methodology and Assessment:
“Follow-me” live coding each session → immediate individual lab applying the same pattern to a new dataset → weekly/biweekly homework. Midterm (Week 8, written + hands-on practical). Open-ended team capstone (Weeks 8–14, proposal → checkpoint → demo). Final exam (Week 15, cumulative, weighted toward Part 2).
Aaron Snowberger earned his Ph.D. in Information and Communications Engineering from Hanbat National University in South Korea in 2023. He also holds degrees in Computer Science and Media Design. He has taught technology courses for over 8 years, English for over 15 years, and has freelanced as a web developer and magazine designer for over 5 years. His current research interests include computer vision, natural language processing, image processing, signal processing, and machine learning.
Aaron Snowberger는 2023년 한국 한밭대학교에서 정보통신공학 박사 학위를 취득했습니다. 그는 또한 컴퓨터 과학 및 미디어 디자인 학위를 취득했습니다. 그는 8년 이상 기술 과정을 가르쳤고, 15년 이상 영어를 가르쳤으며, 5년 이상 웹 개발자 및 잡지 디자이너로 프리랜서로 일했습니다. 현재 연구 관심사는 컴퓨터 비전, 자연어 처리, 영상 처리, 신호 처리, 기계 학습입니다.