Sophie's Data Orchard

Data Scientist Job Market Research (2026) - by my first agent Compiled: 2026-08-06 1. Data Scientist Job Requirements: 2026 vs. 2025 — AI Knowledge on the Rise Clear, sharp increase in AI/GenAI requirements. Core skill shifts (2025 → 2026) SQL remains the #1 most-demanded skill both years — unchanged. Communication moved up to #2, now ranked above Python — companies increasingly want data scientists who can explain findings, not just build models. Python slipped from #2 to #3 in ranking (still essential, just relatively less differentiating). ML skills required in 77% of postings in 2026. The big AI-knowledge jump NLP demand : 5% of postings (2024) → 19% (2025) → NLP mentions up 155% year-over-year — one of the fastest-growing technical asks. Generative AI / LLMs / prompt engineering : mentioned in 31% of data scientist postings in 2026 , up from effectively 0% in 2023 . This is the single starkest change in the market. MLOps now appears in ~8% of postings, reflecting demand fo...

Intro I've been learning to use Claude Code for data science projects, and my first real test was building a simple agent from scratch — starting with a CLAUDE.md file that defines how the agent should behave. Along the way I hit a classic gotcha (files quietly saving to a temp folder instead of my project directory), fixed it, and then put the whole setup through a real research task. Here's what I learned. Building a Simple Agent with Claude Code (VS Code) The prompt is saved in a CLAUDE.md file, with the following content: # try to build an agent ## Project Context This is an AI agent playground. ## About me I'm a DS, I'm learning to use Claude Code for DS projects. ## Rules - Always ask clarifying questions before starting a complex task - Show your plan and steps before execution - Keep reports and summaries concise — bullet points over paragraphs - Save all reports to the output folder - Cite sources when doing research ## Project Structure - `resources/` -- r...

By the final year of OMSA, I could clearly see how the curriculum was coming together. Spring focused on broadening my analytics toolkit—from visual analytics to classical Natural Language Processing—while Summer shifted toward modern Generative AI and an industry-sponsored practicum. Looking back, these four courses complemented one another surprisingly well, bridging traditional analytics, modern AI, and real-world data science practice. CSE 6242 – Data & Visual Analytics CSE 6242 is one of the most practical and technically diverse courses in OMSA. Rather than concentrating on a single subject, the course introduces a wide range of technologies commonly used in data analytics, including D3.js, Tableau, Spark, Docker, SQL, and machine learning workflows. Every homework felt like learning a new technology stack, making the course both exciting and challenging. The workload was among the heaviest in the program. Much of the effort was spent configuring environments, debugging dep...