Foundation
- Spreadsheet reasoning, SQL, descriptive statistics and Git basics.
- Tables, joins, aggregation, missing data and data types.
- Questions, decisions, measures and ethical handling of data.
Work With Data · Career guide
Data Analytics & BI
Turn messy questions and trustworthy data into decisions, metrics and explanations—not merely attractive dashboards.
Career snapshot
A work-pressure profile, not a personality score
Three dots never means ‘better.’ It only means this dimension appears more often in the work.
Coding
Mathematics
Systems depth
Investigation
Creativity / design
Communication
Production responsibility
What would I actually do?
The work behind the title
- Clarify the decision behind a vague request.
- Query, clean and validate data from several sources.
- Define metrics with owners, grain and caveats.
- Build accessible reports and investigate unexpected changes.
- Explain findings, uncertainty and limits to non-specialists.
Reality check
Finishing a tutorial and owning production consequences are different skills. The point is to see the gap, not hide it with hype.
You might enjoy this if…
Signals worth noticing
- You ask what decision a chart should change.
- You enjoy finding why two reports disagree.
- You can explain numbers without hiding uncertainty.
You might dislike this if…
The uncomfortable part
- Stakeholder questions feel like interruptions to the real work.
- You want every dataset to arrive clean and documented.
- You prefer visual polish to checking whether a metric is valid.
A realistic day
No day is identical, but the pressure may feel like this
This is a plausible mix of recurring tasks, not a scripted career fantasy.
Clarify why a retention metric changed.
Trace a duplicate through a query and source definition.
Review a report with operations and record its caveats.
What should I learn?
A layered sequence, not a technology dump
Core
- Metric definitions, grain, cohorts and comparison baselines.
- Data cleaning, validation and reproducible analysis.
- Visual encoding, accessibility and explanatory writing.
Practical
- Answer one decision question from a small documented dataset.
- Create checks for duplicates, missing values and impossible ranges.
- Present the finding, caveat and next question in one page.
Later
- Experiment design, causal inference and forecasting.
- Semantic layers, governed metrics and analytics engineering.
- Domain depth in product, finance, operations or public policy.
What not to learn yet
Not yet—because sequence matters
- A dashboard before agreeing what the metric means.
- Causal claims from a correlation or a tiny convenience sample.
- Collecting certificates for five BI tools at once.
Future You says
‘Later’ does not mean useless. It means you will learn it faster once you have a real problem for it.
AI & this career
Task Automation Exposure
This is not a replacement probability. Capabilities will change, so separate tasks, judgment and responsibility.
Drafting queries and chart variants
AI can draft familiar SQL and chart code, but plausible syntax can encode the wrong grain or denominator.
Student response: Define the metric first, inspect rows and test totals against an independent calculation.
Investigating changes and anomalies
AI can suggest hypotheses, but it cannot know which business event or data defect actually occurred.
Student response: Keep an evidence log and separate observations, hypotheses and verified causes.
Framing decisions and accountable communication
Choosing what to measure and how to communicate consequences needs context and accountable judgment.
Student response: Practice asking who is affected, what is missing and what decision the evidence supports.
Curated learning resources
Learn → Practice → Go deeper
Resource count is not progress. Finish the recommended scope and build something.
SQLBolt
Fast feedback for turning SQL syntax into usable skill.
Do this much: Complete lessons 1–12 and the exercises.
Last reviewed:
Pandas
Quick practice for selecting, cleaning, grouping and checking tabular data.
Do this much: Complete the short exercises, then repeat one analysis on a small CSV you understand.
Last reviewed:
OpenIntro Statistics
A rigorous, openly available statistics foundation for honest analysis.
Do this much: Use the free text for distributions, study design, inference and the difference between association and causation.
Last reviewed:
Next choices
What evidence will you gather next?
Nearby directions
From skills to a destination
Where could you take this?
The career roadmap explains what to learn. A goal guide helps you put those skills to work. Start with one—not every route at once.