📊 Data Science Week 1 of 14 BSc · Y3 S2 ⏱ ~50 min

Week 1: Cloud Architecture, Managed Services & Cost Optimization

Deploy data science workloads in the cloud: managed compute, cloud storage, serverless functions, auto-scaling, and cost-efficient architectures.

University of America

DS305 — Lecture 1 · BSc Y3 S2

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed (CNCF)

🎯 Learning Objectives

Deploy a data science workflow on AWS, GCP, or Azure
Use managed ML services (SageMaker, Vertex AI, AzureML)
Containerize applications with Docker
Build auto-scaling data pipelines with serverless compute

Topics Covered This Lecture

Cloud Fundamentals: IaaS, PaaS, SaaS

Docker & Container Orchestration

Managed ML Services

Cost Optimization & FinOps

📖 Lecture Overview

This first lecture establishes the foundational framework for Cloud Computing for Data Science. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Deploy data science workloads in the cloud: managed compute, cloud storage, serverless functions, auto-scaling, and cost-efficient architectures. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Cloud Fundamentals: IaaS, PaaS, SaaS, Docker & Container Orchestration, Managed ML Services, Cost Optimization & FinOps. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for DS305
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Cloud Fundamentals: IaaS, PaaS, SaaS and Docker & Container Orchestration. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

DS305 Project 1: Cloud-Deployed ML Pipeline

Deploy a complete ML training and inference pipeline on a cloud platform. Use managed storage, compute, and containerized inference. Measure and optimize cloud costs.

Docker container for model training and serving
Cloud deployment manifest (AWS/GCP/Azure)
Auto-scaling inference endpoint with load test results
Cost breakdown and optimization analysis

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

Explain the difference between IaaS, PaaS, and SaaS with data science examples for each.

Analysis Short Answer

What are the advantages of using Docker containers for ML deployment?

Applied Code / Proof

Describe a cloud architecture for running a daily batch ML training job on 100GB of data.