Data Analyst Bootcamp — Program
Data Analyst Bootcamp — Program
A paced, ~10-week program that turns the data-analyst path from a self-serve course into a bootcamp: it sequences the lessons (Topics 1–11), the capstone deliverables (real artifacts, AI-reviewed against rubrics), the interview drills, and career services into one journey with weekly milestones.
It's self-paced — the weeks are a recommended rhythm (roughly 5–8 focused hours each), not hard deadlines. The capstone is one coherent investigation: you carry FreshBox (the meal-kit subscription company whose retention you're asked to investigate) through all six deliverables — same company, same data, one answer-first readout at the end.
Core program (Weeks 1–10)
| Week | Learn (topics) | Capstone deliverable | Interview practice | ~Hours |
|---|---|---|---|---|
| 1 | Topic 1 (What is a Data Analyst?), Topic 2 (Data Analyst vs. other data roles) — plus general Core Tech Baseline alongside | — (meet the FreshBox scenario) | Behavioral: start drafting your STAR stories | 5 |
| 2 | Topic 3 (How data flows in a company), Topic 4 (Spreadsheets — the analyst's first tool) | — | Behavioral drills (D1–D2) | 6 |
| 3 | Topic 5 (Databases and SQL basics) | D2 — SQL Data-Pull Workbook | SQL-technical drills (D1–D3) | 8 |
| 4 | Topic 6 (Defining the question and the metrics) | D1 — Analysis Brief & Metric Definitions | Metrics-and-analysis drills (D1–D2) | 7 |
| 5 | Topic 7 (Cleaning and preparing data) | D3 — Data Cleaning Log | Case-study drills (D1–D2) | 7 |
| 6 | (consolidate: revise D1–D3 from AI feedback) | (revise D1–D3) | Mock-interview checkpoint #1 (SQL-technical + metrics-and-analysis) | 6 |
| 7 | Topic 8 (Descriptive statistics that matter) | D4 — Findings Memo with Descriptive Stats | Metrics-and-analysis drills (D3–D4) + Case-study (D3) | 8 |
| 8 | Topic 9 (Data visualization and dashboards) | D5 — FreshBox Retention Dashboard | Stakeholder-storytelling drills (D1–D2) | 7 |
| 9 | Topic 10 (Telling stories with data) | D6 — Executive Readout & Recommendation | Stakeholder-storytelling drills (D3–D4) + Case-study (D4) | 8 |
| 10 | Topic 11 (Landing the data analyst job) + general Employability & Job Hunting | Assemble the portfolio (all 6 deliverables) | Mock-interview checkpoint #2 (full mock) + career services (below) | 8 |
By the end of Week 10 you have: the full curriculum learned, a 6-artifact FreshBox portfolio proving the end-to-end analyst workflow, repeated interview practice across all five drill banks, and your resume/LinkedIn ready.
A note on sequencing: the SQL Data-Pull Workbook (D2) is built in Week 3 right after you learn SQL (Topic 5), then the Analysis Brief (D1) frames the question in Week 4 (Topic 6) — in practice you'll loop back and tighten both as the question sharpens. That's the real analyst rhythm: pull, look, re-question, re-pull.
Career services
Delivered mainly in Week 10, drawing on the general Employability & Job Hunting track (content/general — Topics 6–10: planning your pivot, LinkedIn, networking, resumes, interviewing). These are self/AI-reviewed against rubrics (no human mentor required). This is the part you pay a bootcamp for instead of a free course, so each piece below ships with a worked example tied to FreshBox — not just a rubric to grade yourself against, but a concrete model to copy from.
Resume review rubric
- Outcome-focused bullets — 1: duties only · 2: some results · 3: quantified outcomes ("cut the weekly retention report from 3 hours to 20 minutes by templating the SQL pull", "surfaced the Week-2 onboarding gap driving 38% of 90-day churn").
- Named tools, not just "data" — 1: no tooling named · 2: vague ("BI tools", "databases") · 3: the actual stack a screener filters on — SQL (joins, window functions, CTEs), the BI tool you built D5 in (Tableau / Power BI / Looker Studio), spreadsheets (pivot tables, lookups), and the warehouse flavor if you know it (Postgres, BigQuery, Snowflake).
- Analyst workflow reframed — 1: generic · 2: some data terms · 3: bullets that map to the real loop (metric definition, SQL data-pull, cleaning/QA, descriptive stats, dashboard, executive readout) so the reader sees you can run a question end to end.
- Portfolio linked — 1: none · 2: mentioned · 3: linked and tailored — the FreshBox investigation shown as proof, with one line naming the result ("10-week investigation into FreshBox meal-kit churn; recommendation projected to recover ~X% of at-risk subscribers").
- Clarity & format — 1: cluttered/typos · 2: clean · 3: crisp, one page, scannable in seconds.
Worked example — three FreshBox resume bullets (level-3):
- Investigated 90-day subscriber churn for FreshBox (meal-kit subscription) end to end: defined the retention metric, pulled cohorts in SQL (CTEs + window functions over a Postgres orders table), and QA'd 14k rows down to a clean analysis set.
- Built a Tableau retention dashboard segmenting churn by signup cohort and onboarding completion; isolated the Week-2 drop-off that accounted for ~38% of churn.
- Delivered an answer-first executive readout recommending an onboarding-nudge experiment; quantified the at-risk revenue and defined the success metric and guardrail for the test.
LinkedIn review rubric
- Headline signals target role + stack — 1: old title only · 2: mentions data · 3: target role with the screen-able tools up front ("Data Analyst | SQL · Tableau · A/B & retention analysis | ex-[field]"). Recruiters keyword-search the headline — the tool names belong here, not just in the About.
- About tells the pivot story — 1: empty/generic · 2: present · 3: confident pivot story (where heading, what you bring, why, proof), closing on the FreshBox investigation as evidence.
- Featured / proof — 1: none · 2: skills listed only · 3: the FreshBox portfolio actually linked in the Featured section (dashboard screenshot + readout), so a recruiter can click into proof, plus skills listed (SQL, statistics, the named BI tool).
- Activity — 1: inactive · 2: occasional · 3: connecting/engaging with the data community.
Mock-interview checkpoints
Use the AI mock interview (interview/mock-interview.md): Checkpoint #1 (Week 6) — SQL-technical + metrics-and-analysis (your hard-skill screens); Checkpoint #2 (Week 10) — a full mock across all five banks (sql-technical, case-study, metrics-and-analysis, stakeholder-storytelling, behavioral). Track which rubric criteria you repeatedly miss and focus there.
Portfolio walkthrough — the stakeholder round
Most analyst loops include a round where you present your work to a non-technical stakeholder (often a hiring manager role-playing a marketing or ops lead). The mistake juniors make is narrating all six artifacts in order, tool by tool. Don't. Lead with the answer, then use the artifacts as evidence on demand. Here's a ~3-minute script for presenting the FreshBox portfolio in that round:
(Answer first, ~30s) "FreshBox loses about [X]% of subscribers within 90 days, and the single biggest driver is a Week-2 onboarding drop-off, not price. My recommendation is a Week-2 onboarding-nudge experiment; I'll walk you through how I got there."
(The question & the data, ~30s) "I started by defining churn precisely — [definition] — because 'retention' meant three different things across the team. That's the Analysis Brief (D1). Then I pulled the cohorts in SQL — that's the Data-Pull Workbook (D2) — and logged every cleaning decision in D3 so the numbers are defensible."
(The finding, ~45s) "The descriptive cut in my Findings Memo (D4) is where the Week-2 pattern showed up, and this dashboard (D5) lets you see it by cohort — [point to the drop-off]. I'd pause here if you want to interrogate any number."
(The recommendation, ~30s) "So my Executive Readout (D6) recommends [the experiment], with [success metric] as the win condition and [guardrail] so we don't trade churn for margin. If it works, it recovers roughly [X]% of at-risk subscribers."
(Hand back) "Happy to go deeper on the SQL, the cleaning calls, or the stat behind the Week-2 number — where would you like to start?"
Why this works: you've shown the whole workflow (D1→D6) in three minutes, led with a decision a stakeholder can act on, and invited the technical drill-down instead of forcing it — which is exactly the senior behavior interviewers are scoring for. Rehearse this against the stakeholder-storytelling drills; the AI can play the stakeholder and interrupt with "but isn't this just price?" so you practice defending the finding.
Salary negotiation — worked example
The generic advice ("ask for time, research the range, counter politely") is fine but useless without numbers and a script. Here's the real drill, with a concrete analyst offer to negotiate against.
Step 1 — Know the band. Market ranges for an entry/junior Data Analyst vary widely by region; anchor to the right one before you respond. Indicative base-salary bands (verify against live data — Levels.fyi, Glassdoor, local salary surveys — at the time you negotiate, since these drift):
| Region | Junior / entry analyst (base) | Mid analyst (2–4 yrs, base) |
|---|---|---|
| US — major metro (SF/NYC) | $75k–$95k | $95k–$120k |
| US — other metros / remote | $60k–$80k | $80k–$105k |
| UK — London | £35k–£48k | £48k–£65k |
| Germany | €45k–€58k | €58k–€75k |
| India — metro (Bangalore/NCR) | ₹6L–₹11L | ₹12L–₹20L |
| Israel | ₪18k–₪26k / mo | ₪26k–₪38k / mo |
Step 2 — The sample offer. A US-remote company offers you, a career-switcher into your first analyst role: $68,000 base, standard benefits, no signing bonus. That's real but sits in the lower third of the remote junior band above — and you have a portfolio most first-time applicants don't.
Step 3 — Buy time (don't accept on the call):
"Thank you — I'm genuinely excited about this role and the team. Could I take 48 hours to review the full package? I want to come back to you thoughtfully, not just react."
Step 4 — The counter (3 lines, tied to the FreshBox portfolio):
"I've reviewed the offer and I'm ready to accept — I'd just like to align base closer to $75,000. Two things support that: the market range I'm seeing for remote junior analysts centers higher than $68k, and I'm not coming in cold — my FreshBox capstone is a full end-to-end investigation (SQL pulls, a cleaned dataset, a Tableau dashboard, and an executive readout) that I can be productive from in week one. Is there room to move base toward $75k?"
Notes on why this counter lands: it (1) signals you will accept so the recruiter isn't gambling, (2) anchors to a specific number with a reason, not a vague "more," (3) ties the ask to demonstrable value — the portfolio — rather than your needs, and (4) asks one clean question. If base is truly capped, pivot the same energy to a signing bonus, an earlier review, or extra PTO.
Step 5 — Evaluate the whole offer, not just base: learning budget, mentorship, the tools you'd actually use (does the role run SQL + a real BI stack you can grow on?), review/promotion cadence, and remote/hybrid fit. A $72k role that puts you on Snowflake + dbt with a senior analyst mentoring you can beat a $78k dead-end seat.
Drill it: the AI role-plays the recruiter — including the awkward "$68k is our band for this level" pushback — and grades your counter on the four notes above.
How it fits together
Lessons teach the concept → the matching capstone deliverable makes you do it on FreshBox (and the AI reviews it against a rubric) → interview drills rehearse explaining it under pressure → career services package it for the job hunt. That loop — learn, do, get feedback, present — is what makes this a bootcamp rather than a reading list. The six FreshBox artifacts assemble into a portfolio that proves you can run the full analyst workflow end to end, not just describe it.