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Practice Guide

Portfolio Evaluation Checklist

  • Anything missing? Steps in projects? Details in descriptions?
  • If you have a website, are all pages accounted for?
  • If hosted on a platform, are projects uploaded properly?
  • Is there too much info?
  • Could descriptions be revised for brevity?
  • Places where you include more data than needed? Could anything be cut without losing meaning?
  • Anything you shouldn't include?
  • Have you included references to others' work without citing them? Replace with links.
  • Anything seem extraneous or unprofessional?
  • Is portfolio hosted on the most appropriate platform?
  • Considered options: GitHub, Kaggle, Tableau Public, personal site

Case Study Structure

What to include in a case study:

  1. Introduction — purpose, scenario, real-world relevance (optional: assumptions/theories)
  2. Problems — major problems identified, how you analyzed, supporting facts
  3. Solutions — outline solution + alternatives, pros/cons each
  4. Conclusion — key takeaways, what you learned
  5. Next steps — chosen solution and recommendation; explain why; specify what/who/when

Optional: AI Implementation if relevant.

Discovery process (EDA) example

Self-questions

  • How can I break this data into smaller groups to understand it better?
  • How can I prove my hypothesis?
  • In its current form, can this data give me the answers I need?

Questions to ask

  • Which months have the most passenger traffic?
  • Which weeks, dates, or holidays have the highest passengers?
  • When are tickets typically purchased?

Hypothesis

If the airline lowers prices for Tuesdays/Wednesdays during non-holiday weeks, they will sell more tickets.

Test hypothesis

Analyze the data to see if lowering prices on those flights would attract more customers.

Organize / alter data

  • Regroup entries into months/years or age ranges
  • Group customer ages into age ranges
  • Combine or split data columns
  • Change date formats or time zones

Interview Questions

Common analyst interview questions:

  • What is your process for cleaning data?
  • What tools do you use for creating data visualizations?
  • How and why do data visualizations enhance the stories data tells?
  • What considerations are top of mind when sharing data stories with non-technical stakeholders?
  • Walk me through a project you're proud of
  • How do you decide which metric to track?
  • Describe a time you found an unexpected insight
  • How do you handle conflicting requests from stakeholders?
  • What's the difference between correlation and causation?
  • Explain p-value to a non-technical PM

Practice platforms

Build a portfolio in 4 projects

A solid analyst portfolio needs breadth more than depth:

  1. One messy real-world dataset — show cleaning skill (e.g., NYC 311 complaints)
  2. One business analysis — frame a question, answer with data, give recommendation (e.g., Bellabeat case study)
  3. One dashboard — Tableau Public or Looker Studio (anyone can click around)
  4. One end-to-end project — SQL data pull → Python analysis → visualization → write-up

Host on:

  • GitHub — code and notebooks
  • Personal site (this kind of static site works) — narrative
  • Tableau Public — dashboards
  • Kaggle — notebooks with built-in audience

SMART questions practice scenario

You are three weeks into a junior data analyst job. Your company has just collected weekend sales data. Your manager asks you to perform a thorough exploration.

Ask before doing:

  • When is the project due?
  • Are there specific challenges to keep in mind?
  • Who are the major stakeholders, and what do they expect?
  • Who am I presenting the results to?

Topic-based:

  • Objectives: What are the goals? What questions should be answered?
  • Audience: Who's interested or concerned about results?
  • Time: Time frame for completion?
  • Resources: What's available?
  • Security: Who should have access?

References