Practical DS/quant roadmaps posted

A cluster of hands-on roadmaps for data science, data engineering and quant work landed this week with practice counts, interview drills and production checklists. (x.com) One post laid out a Structured Data Engineer track built around 52 Structured Query Language problems, 21 Python problems and 26 PySpark problems, turning a broad job category into a numbered practice list. A second post mapped a 10-step path from Python basics to Docker and Kubernetes deployment, putting software packaging and production operations at the end of the sequence. (x.com 1) (x.com 2) The same burst of material included a Mastercard data engineer interview walkthrough, a TabICLv2 tutorial for time-series forecasting and an ASQ risk analytics tool for backtesting and Monte Carlo simulation. Those pieces covered three different bottlenecks in the field: getting hired, building models and checking whether a trading system survives bad luck. (x.com 1) (x.com 2) (x.com 3) Data engineering is the work of moving and cleaning data so analysts and models can use it later, and the recurring basics are still Structured Query Language and Python. Dataquest’s 2026 beginner roadmap also starts with Python, Structured Query Language, cloud platforms and orchestration tools, then estimates 8 to 12 months to become job-ready. (dataquest.io) PySpark sits later in that stack because Apache Spark spreads large jobs across many machines, and PySpark is the Python interface to that system. The 26-problem PySpark segment in the roadmap mirrors that industry pattern: learn database logic first, then move to distributed processing when datasets outgrow one computer. (x.com) (apache.org) The 10-step guide pushes one step further into production, where Docker packages code with its dependencies and Kubernetes keeps those packages running across servers. That sequence matches mainstream platform-engineering training, which treats containers and orchestration as later-stage skills after coding, testing and deployment basics. (x.com) (kubernetes.io) (docker.com) Interview prep is its own lane inside this bundle. Mastercard’s careers site says candidates should expect interviews that test problem-solving in conversation form, while recent interview guides and candidate reports describe a mix of recruiter screening, Structured Query Language questions, Python coding and extract-transform-load system design. (careers.mastercard.com) (interviewquery.com) (glassdoor.com) The modeling tutorial in the thread points to TabICLv2, a tabular foundation model released in a February 11, 2026 paper and codebase. The authors say the model reaches state-of-the-art results for tabular classification and regression, and the repository includes a separate time-series forecasting tutorial that reframes forecasting as a tabular regression task with calendar and seasonality features. (arxiv.org) (github.com 1) (github.com 2) The quant side of the package is less about prediction than survival. The ASQ Risk Analytics Engine page says the tool runs 1,000 to 10,000 bootstrap-based Monte Carlo simulations and reports Value at Risk, Kelly Criterion sizing and more than 12 risk-adjusted metrics from a trading history. (mql5.com) Taken together, the week’s posts turn a vague “learn data” pitch into a ladder with counts, stages and checkpoints: solve Structured Query Language and Python problems, add PySpark, practice interview cases, then move into deployment or risk testing. (x.com) (x.com)