Fsdss003 ^new^ Link

I can create a professional, well-structured exam centered on "fsdss003." To proceed, I need one clarification (you can skip if you're okay with my assumptions):

3. Weekly Schedule (12 Weeks)

| Week | Topic | Core Lecture (2 h) | Lab / Activity (2 h) | Deliverable | |------|-------|-------------------|----------------------|-------------| | 1 | Intro & Data‑Science Workflow | Course orientation, “What is Data Science?” | Set up environment (conda, GitHub repo) | Personal repo created | | 2 | Data Types & Acquisition | Structured vs. unstructured, APIs, web‑scraping | Pull data from a public API (e.g., OpenWeather) | Raw data dump | | 3 | Exploratory Data Analysis (EDA) | Summary stats, visualisation principles | EDA notebook: histograms, box‑plots, correlation matrix | EDA report | | 4 | Data Cleaning & Feature Engineering | Missing data, outliers, encoding, scaling | Clean the Week 2 dataset, create new features | Cleaned dataset | | 5 | Probability Refresher | Discrete/continuous distributions, Bayes theorem | Simulate distributions in Python/R | Simulation notebook | | 6 | Statistical Inference I | Estimation, confidence intervals, hypothesis testing | t‑tests & ANOVA on the cleaned dataset | Test results summary | | 7 | Statistical Inference II | Linear regression assumptions, diagnostics | Fit & diagnose a multivariate regression model | Regression report | | 8 | Intro to Predictive Modeling | Supervised learning, train‑test split, cross‑validation | Build a k‑NN classifier for a classification task | Model notebook | | 9 | Decision Trees & Ensembles | CART, bagging, random forests | Train a random‑forest model; feature‑importance analysis | Model performance chart | |10 | Model Evaluation & Selection | Metrics (RMSE, AUC, F1), bias‑variance, grid search | Hyperparameter tuning with scikit‑learn | Tuned model artefact | |11 | Communicating Results | Story‑telling with data, dashboards, reproducible reports | Create a mini‑dashboard (Plotly Dash / Shiny) | Interactive dashboard | |12 | Capstone Presentations & Reflection | Project showcase, peer review, next steps | Final project presentations (15 min each) | Portfolio PDF + GitHub repo | fsdss003

3.3. Metadata Service (CRDT‑Based)

  • Stores directory trees, inode attributes, ACLs in a CRDT that converges automatically after network partitions.
  • Guarantees POSIX‑compatible rename semantics even under concurrent updates.

FSDSS003 is born out of the lessons learned from the above generations. It takes the strong consistency guarantees of CephFS, merges them with the elastic durability of object stores, and adds a developer‑centric data‑plane that can be extended with eBPF, WebAssembly, or custom plugins. I can create a professional, well-structured exam centered

Key Highlights