The last map-phase episode: we build the workshop and set the strategy. We construct the development environment the whole course uses, then make the case that shipped portfolio projects beat certificates in a tougher entry-level market. **Why Python won.** Python is glue over compiled cores; NumPy [brings C and Fortran power to Python](https://inferencebysequoia.substack.com/p/why-python-became-the-language-of) by dispatching array math to BLAS/LAPACK. Proof of the hybrid design: SciPy is roughly 50% Python, 25% Fortran, 20% C ([SciPy 1.0, Nature Methods](https://www.nature.com/articles/s41592-019-0686-2); [Array Programming with NumPy](https://arxiv.org/pdf/2006.10256)). Python 3.14 (Oct 2025) ships a supported free-threaded build, but pin 3.11 or 3.12 for ML since CUDA wheels lag ([Astral](https://astral.sh/blog/python-3.14)). **Environments & packaging.** venv+pip (baseline), conda/Miniforge (binary deps like CUDA), and uv (Rust, 10-100x faster, the 2026 default). Note the [Anaconda licensing landmine](https://www.datacamp.com/blog/navigating-anaconda-licensing): paid license required for 200+ employee orgs; prefer Miniforge/conda-forge. Lockfiles give reproducibility. **Notebooks.** Great for exploration, terrible for git. [Pimentel et al.](https://leomurta.github.io/papers/pimentel2019a.pdf) analyzed 1.45M notebooks: only ~24% ran clean, only 4% reproduced results. Cure: Restart Kernel and Run All. Tools: nbstripout, jupytext, nbdime; marimo as a reactive alternative. **Git & GitHub.** Commit code/configs/lockfiles, not data/weights/secrets (100MB cap). README in Problem-Solution-Impact format. Later: Git LFS and [DVC](https://www.datacamp.com/tutorial/data-version-control-dvc). **Editors & hardware.** VS Code dominates (73.6% in SO 2024); Cursor, Copilot, Claude Code are the AI trio. You don't need a GPU early; use free Colab (T4) and Kaggle. **Portfolio over credential.** [WEF Future of Jobs 2025](https://www.weforum.org/press/2025/01/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/): net +78M jobs by 2030; AI/ML specialists among the fastest-growing. ML Engineer pay ranges widely ([Coursera](https://www.coursera.org/articles/machine-learning-salary)). Entry-level is genuinely tough ([IEEE Spectrum](https://spectrum.ieee.org/ai-effect-entry-level-jobs)), which is exactly why a portfolio matters: 3-5 repos, one deployed demo, one write-up ([Careery](https://careery.pro/blog/data-science-careers/data-science-portfolio-projects)). **News:** SpaceX/xAI's record $75B IPO ([NPR](https://www.npr.org/2026/06/11/nx-s1-5853199/spacex-ipo-price-elon-musk)); Moonshot's open-weight [Kimi K2.7 Code](https://cryptobriefing.com/kimi-k2-7-code-open-source-release/); OpenAI retires GPT-5.2; Google's [DiffusionGemma](https://www.marktechpost.com/2026/06/10/google-ai-releases-diffusiongemma-a-26b-moe-open-model-using-text-diffusion-for-up-to-4x-faster-generation/).