Free data‑analytics roadmaps and interview lists

Two posts on X turned a messy career question into a shopping list: learn Microsoft Excel, then Structured Query Language, then Python, then a dashboard tool, and finish with interview drills instead of random tutorials. The first post bundled free roadmaps across Microsoft Excel, Structured Query Language, Python, Microsoft Power BI, Tableau, and advanced topics, while the second shared 50-plus junior data-analyst interview questions. (x.com 1) (x.com 2) That order matches how the job usually works in practice. A junior analyst often starts in spreadsheets, pulls data from a database with Structured Query Language, cleans larger files with Python, and then shows the result in Microsoft Power BI or Tableau. (support.microsoft.com) (learn.microsoft.com) (pandas.pydata.org) (learn.microsoft.com) (tableau.com) Microsoft Excel stays first because companies still hand analysts comma-separated value exports, budget sheets, and sales trackers before they hand over a clean data warehouse. Microsoft’s own training for Power Query and Power Pivot is built around cleaning, combining, and modeling data inside Excel, which is exactly the work many entry-level analysts do. (support.microsoft.com) Structured Query Language comes next because databases are where the bigger tables live. Microsoft Learn’s introductory path teaches querying and modifying relational data, which is the step between “I can use a spreadsheet” and “I can answer questions from millions of rows without breaking a workbook.” (learn.microsoft.com) Python usually shows up after that for one reason: repetition. The pandas project describes itself as a Python library for data analysis and manipulation, and that is what turns a one-time spreadsheet cleanup into a script you can rerun every Monday morning. (pandas.pydata.org) The dashboard tools sit on top of those earlier skills instead of replacing them. Microsoft says Power BI connects to data sources, cleans and models data, and turns them into interactive insights, while Tableau says its free learning paths cover building visualizations and dashboards from beginner to expert. (learn.microsoft.com) (tableau.com) The interview list matters because hiring managers rarely test only one tool. Recent interview guides for data analysts still cluster questions around Structured Query Language joins and aggregations, Microsoft Excel functions and pivot tables, statistics basics, and case-style business problems, which is almost the same stack those X posts organized into a study plan. (interviewquery.com) (dataquest.io) (simplilearn.com) That makes the best use of these lists much narrower than “learn everything.” A finance student can use Microsoft Excel, Structured Query Language, and Microsoft Power BI to build reporting skills fast, while a career changer aiming at product analytics can spend more time on Structured Query Language, Python, and case questions about funnels, retention, and experiments. (support.microsoft.com) (learn.microsoft.com) (pandas.pydata.org) (learn.microsoft.com) The hidden advantage is not that the resources are free. The hidden advantage is that a roadmap plus an interview list gives you a sequence: learn one tool, build one small project, answer one set of questions, and then move to the next tool without guessing what “job ready” means. (x.com 1) (x.com 2)