Micro-app · Junior Data Analyst Practice · Portfolio Evidence

Data Analyst Sprint Board.

A small interactive practice tool for turning daily study into visible evidence: SQL questions, Python cleaning reps, dashboard thinking, and clear analyst storytelling.

Start a 25-minute sprintBuild a sprint noteSee the learning path
Today's analyst prompt

Find the business question before writing code.

Pick one dataset or receipt table and write the plain-English decision it should support. Then list three fields needed to answer it cleanly.

Skill: Analyst thinkingTimebox: 25 minutesOutput: one portfolio note
Sprint note builder

Turn one practice rep into a polished analyst story.

Fill in what you actually did, then copy a concise summary for a portfolio log, GitHub README update, or interview practice note. The fields save in this browser only.

Practice path

Small reps that become interview stories.

The goal is not random studying. Each sprint should produce a small artifact Daniel can mention in a portfolio update, GitHub note, or interview answer.

Why this helps

Turns learning into proof.

Junior data analyst applications need visible evidence: clean questions, reproducible analysis, understandable charts, and concise recommendations. This board nudges every practice session toward that evidence.

SQLPythonStorytellingPortfolio proof
How to use it

One prompt, one artifact, one note.

Open the board, run a 25-minute sprint, check off the evidence habits, and save the result as a short project note or dashboard improvement.

25-minute sprintDaily habitCareer growth
Interview signal

Explain your process.

Each finished sprint gives Daniel language for interviews: how he framed the problem, cleaned the data, chose the visual, and communicated the result.

Portfolio signal

Ship tiny improvements.

The best practice reps can become improvements to the grocery inflation project, Dataset Gym, or a short written case-study update.

Agentic AI signal

Use Hermes as a reviewer.

After a sprint, Hermes can check the SQL, review the explanation, or turn the result into a polished portfolio entry while keeping the analysis honest.

  1. Pick a real question.
    Example: which grocery category changed most month over month, or which store had the lowest unit price for repeated items?
  2. Build the smallest reliable dataset.
    Clean names, units, dates, and categories before making conclusions.
  3. Show the result clearly.
    Use one chart, one table, and one concise recommendation.
  4. Save the evidence.
    Commit the query, notebook, screenshot, or written summary so the practice compounds.