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Data and Analytics

Turn messy records into answers a team can act on — and explain the answer clearly.

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Career overview

Data and analytics roles sit between a question someone has and the records that might answer it. The work is part detective, part editor: finding the relevant data, checking whether it can carry the weight of the question, and presenting what it shows without overstating it.

Titles vary widely — data analyst, business intelligence analyst, reporting analyst, insights analyst — and the day-to-day differs more by industry and team size than by title. In smaller organisations one person often covers the whole span from data cleaning to presentation.

Typical responsibilities

What the work looks like

  • Clarifying what a stakeholder actually wants to know before pulling any data
  • Querying databases and combining data from several sources
  • Cleaning, validating and documenting datasets so results are reproducible
  • Building dashboards and recurring reports people rely on
  • Investigating anomalies — a metric moved, and someone needs to know why
  • Presenting findings to people who will not read the SQL

Relevant skills

What it tends to ask for

Technical

  • SQL
  • Spreadsheet modelling
  • Python or R basics
  • Data cleaning
  • Dashboard tools

Analytical

  • Descriptive statistics
  • Framing a question
  • Sanity-checking results
  • Segmentation

Human

  • Written explanation
  • Stakeholder interviewing
  • Visual communication
  • Scope negotiation

Example tools and technologies

PostgreSQL / MySQL · Excel / Google Sheets · Power BI · Tableau · Looker Studio · dbt · Python (pandas)

Illustrative. Specific stacks vary a lot by organisation.

Transferable entry points

Where people come into this from

Most people arrive from somewhere adjacent. These are common starting points and what tends to carry over.

  • Operations or reporting roles

    You already know what the numbers mean in context, which is usually the slower half to learn.

  • Finance or accounting

    Reconciliation instincts, comfort with large spreadsheets, and a habit of checking totals.

  • Customer support or success

    Direct knowledge of why customers behave the way the data shows, plus clear written communication.

  • Research or academic work

    Study design, statistical caution, and the discipline of documenting method.

Suggested development areas

Where the gap usually is

Common areas people need to build when moving toward this path. Useful, not disqualifying.

  • 01SQL beyond simple selects — joins, window functions, CTEs
  • 02Data modelling: how a warehouse is structured and why
  • 03Visual design choices that do not mislead
  • 04Version control, so analysis can be reviewed like code

Possible learning directions

Where to start learning

Conceptual curricula, not enrolled courses. Career Mine does not provide accreditation, certification or placement.

Frequently asked

Questions people ask about this path

Many analysts do not have one. A working grasp of descriptive statistics, distributions and the common traps — selection bias, confusing correlation with cause — covers a large share of everyday analytics work. Deeper statistical training matters more for experimentation and modelling roles.

Related career paths

Adjacent directions worth a look

Next step

Is Data and Analytics worth exploring further?

Work through the discovery questions, or compare this against the skills you can already evidence.

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