Fresh roadmaps and interview kits
Several recent social posts collected end‑to‑end learning roadmaps and interview practice: an A–Z data science roadmap covering Python, SQL and deployment; a structured data‑engineer track with 52 SQL problems and PySpark practice; and a full‑stack ML engineer tech stack listing tools from PyTorch to LangChain. Multiple posts also share practical interview drills like building backend systems and a Mastercard senior data‑engineer interview breakdown. ((x.com), (x.com), (x.com), (x.com), (x.com))
A cluster of recent career posts is turning data and machine learning job prep into checklists: learn the stack, build projects, then rehearse the interview. (github.com) The common pattern is broad and sequential. Public roadmaps now bundle Python, statistics, Structured Query Language, machine learning, deep learning, deployment, and portfolio work into one path instead of scattered tutorials. (github.com) One widely shared GitHub roadmap labels itself “Data Science Roadmap from A to Z,” has 4,200 stars and 594 forks, and starts with business understanding, project life cycle, Python, mathematics, and statistics before moving into modeling. (github.com) Another beginner-to-advanced roadmap breaks the work into 8 phases over 12 to 18 months, with 300 to 500 total hours, then ends with projects, networking, and job preparation. (github.com) Data engineering tracks are getting more specific about practice. Data Vidhya says its platform includes 150-plus Structured Query Language, Python, and PySpark problems, interactive data-modeling drills, and pipeline design exercises using tools like Apache Spark, Apache Kafka, Apache Airflow, dbt, Amazon Web Services, and Google Cloud Platform. (datavidhya.com) That focus reflects how data engineering interviews are usually run. Data Vidhya’s 2025 explainer split prep into 5 stages: coding, database work, technical case studies, system design for data, and hands-on labs for tools like Airflow, Kafka, and dbt. (medium.com) Machine learning roadmaps are widening in the same way. One public “Full Stack Machine Learning Engineer” guide starts with Python, NumPy, pandas, visualization, and statistics, then moves into Scikit-learn, deep learning, natural language processing, and deployment. (github.com) Interview kits are also shifting from theory to simulation. Recent mock-interview materials emphasize designing a scalable backend, explaining tradeoffs out loud, and walking through failure cases instead of only solving whiteboard-style coding prompts. (youtube.com) A recent Mastercard-focused interview breakdown posted by educator Sumit Mittal described 4 rounds for a senior data engineer candidate: 3 technical and 1 managerial. The questions it listed centered on Structured Query Language joins, PySpark optimization, Apache Kafka, Databricks, Delta Lake, fraud detection, and systems that can process “millions of transactions every second.” (youtube.com) Mastercard’s own careers site says every interview includes a behavioral component and an assessment of problem-solving skills, though the exact format varies by role. The company tells candidates to know their resume in depth and asks them to prepare for a conversation, not just a test. (careers.mastercard.com) Put together, the newer roadmaps describe the same hiring market from two angles. The study plan now ends where the interview starts: with projects, system design, and clear explanations of how data systems actually run. (github.com)
