Fsdss003 !!top!! -

| Era | Primary Storage Model | Strengths | Weaknesses | |-----|-----------------------|----------|------------| | | Local block devices (FAT, ext, NTFS) | Low latency, simple design | Single‑point failure, limited scalability | | 1990‑2010 | Network file systems (NFS, CIFS/SMB) | Centralized access, shared files | Bottleneck at the server, complex HA setups | | 2010‑2020 | Object storage (Amazon S3, OpenStack Swift) | Massive scale, eventual consistency | No POSIX semantics, higher latency for small files | | 2020‑Present | Distributed file systems (CephFS, GlusterFS) | Strong consistency + scalability | Operational complexity, high resource overhead | | 2026 | FSDSS003 | Global consistency, low‑latency edge caching, native observability | Still early‑adopter phase (but rapidly maturing) |

| Item | Details | |------|----------| | | FSDSS003 | | Delivery Mode | 2 × 2‑hour live lectures + 1 × 2‑hour lab (in‑person or virtual) + weekly discussion forum | | Prerequisites | Intro to Programming (any language) and Basic College‑level Math (Algebra/Pre‑calc) | | Target Audience | Undergraduate students, career‑switchers, and professionals who want a solid, tool‑agnostic grounding in data‑driven problem solving | | Instructor | Dr. Maya R. Patel – PhD Statistics, 10 y industry + 8 y teaching experience | | Textbook | Data Science from the Ground Up – O’Reilly, 2023 (or any open‑source equivalent) | | Software Stack | Python 3.11 (NumPy, pandas, SciPy, scikit‑learn), R 4.3 (tidyverse), JupyterLab, Git/GitHub | fsdss003

Create automated scripts to handle missing values, outliers, and feature scaling. | Era | Primary Storage Model | Strengths

Dear Students,