Day 05 Part 2 — Mock Interview & Resume Review¶
The final session. You've built the skills — now learn to land the job. This covers the data analyst interview process, how to present your projects, resume writing with metrics, and a mock interview structure you can practise with.
Learning Objectives¶
- Understand the data analyst interview process
- Write a resume that gets past screening
- Present your portfolio projects compellingly
- Practise answering technical and behavioural questions
- Negotiate and evaluate offers
The Data Analyst Interview Process¶
| Stage | What they assess | How to prepare |
|---|---|---|
| Resume screen | Relevant skills, keywords | Tailor resume, quantify achievements |
| Recruiter call | Fit, motivation, salary | Know the role, have a salary range |
| Technical screen | SQL, sometimes Python/stats | Practise on LeetCode, Stratascratch |
| Take-home / case | End-to-end analysis | Use your project workflow |
| Onsite / panel | Depth, communication, fit | Present a project, whiteboard SQL |
| Behavioural | Teamwork, conflict, ownership | STAR-method stories |
Resume Writing for Analysts¶
The resume's only job is to get you the interview. Make it scannable and quantified.
The formula for bullet points: Action + Result + Metric¶
| Weak | Strong |
|---|---|
| "Responsible for SQL queries" | "Wrote SQL queries that reduced monthly reporting time from 5 days to 4 hours" |
| "Built dashboards in Power BI" | "Built a Power BI dashboard adopted by 40+ stakeholders, replacing manual Excel reports" |
| "Analysed customer data" | "Analysed churn drivers, identifying a £130k profit recovery opportunity through discount policy" |
Quantify everything
Numbers jump off the page and prove impact. £ saved, % improved, hours reduced, people served, rows processed. If you can't quantify it, estimate it honestly ("~", "approx").
Resume structure¶
- Header — name, contact, LinkedIn, GitHub/portfolio link
- Summary — 2 lines: who you are + your strongest skills
- Skills — SQL, Python, Excel, Power BI/Tableau, statistics (match the job description's keywords)
- Projects — your portfolio pieces with quantified outcomes (huge for career switchers)
- Experience — quantified bullet points
- Education — degree, relevant courses, this bootcamp
Presenting Your Projects¶
When asked "tell me about a project," use this structure (it's the data story arc):
- The problem — "A telecom company's churn had risen to 8%..."
- Your approach — "I analysed the data with SQL and Python, focusing on..."
- The finding — "I discovered overtime was the biggest driver — 3× higher churn..."
- The impact — "I recommended a discount cap that could recover £130k..."
- What you learned — shows growth and self-awareness
Keep it to 2 minutes. Have a screenshot ready. Be ready to go deeper on any part.
Technical Interview Prep¶
SQL (the most-tested skill): - JOINs, GROUP BY, HAVING, window functions, CTEs, subqueries - Practise on Stratascratch, LeetCode, HackerRank - The classic: "find the second-highest salary" / "top N per group" / "running total"
Python/Pandas: - Loading, filtering, groupby, merge, apply - See Pandas interview questions
Statistics: - p-values, confidence intervals, A/B testing, correlation vs causation - See Statistics interview questions
Case/product: - "How would you measure the success of feature X?" - "Our metric dropped 10% — investigate." - See Business case studies
Behavioural Questions — The STAR Method¶
Structure every behavioural answer as Situation, Task, Action, Result.
Common questions to prepare: - "Tell me about a time you found an insight that changed a decision." - "Describe a time your analysis was wrong or challenged." - "How do you handle a stakeholder who disagrees with your findings?" - "Tell me about a time you had to explain something technical to a non-technical person."
Prepare 5-6 STAR stories
Most behavioural questions are variations on a few themes (impact, conflict, failure, teamwork, communication). Prepare 5-6 solid stories and adapt them to whatever's asked.
Evaluating and Negotiating Offers¶
- Research the salary range (Glassdoor, levels.fyi, Levels, local market data) before the recruiter call
- When asked your expectations, give a researched range, not a single number
- Negotiate — most first offers have room; politely ask "is there flexibility?"
- Evaluate the whole package — salary, learning opportunity, manager quality, tools used, growth path. Your first analyst role is a launchpad, not a destination.
Mock Interview Structure (Practise This)¶
45-minute mock: - 5 min: intro + "walk me through your background" - 10 min: project deep-dive ("tell me about a project") - 15 min: live SQL (a query or two) - 5 min: a stats/case question - 5 min: behavioural (STAR) - 5 min: your questions for them
Practise with a peer, alternating roles. Record yourself if solo. The discomfort of practice is far cheaper than the cost of fumbling the real thing.
Final Checklist Before Applying¶
- [ ] Resume quantified and tailored to the job description
- [ ] LinkedIn updated and matching the resume
- [ ] Portfolio live (GitHub/personal site) with 1-2 polished projects
- [ ] SQL practice: 30+ problems solved
- [ ] 5-6 STAR stories prepared
- [ ] Project deep-dive rehearsed to 2 minutes
- [ ] Salary range researched
Interview Questions (Meta)¶
[Beginner] How would you describe a project you're proud of in two minutes?
[Mid-level] Walk me through how you'd answer "our key metric dropped 15% last week — what do you do?"
[Senior] How do you decide which job offer to accept when comparing salary, learning, and team quality?
You've completed the course
You now have: SQL, Python, Pandas, EDA, statistics, visualisation, Power BI, Tableau, business analytics, and 7 portfolio projects. The skills are real. Now go build the portfolio, practise the interviews, and land the role. Good luck.