What is a data analyst?

To put it simply, data analysts transform raw numbers into clear insights and recommendations that help teams make better decisions. They answer questions as diverse as “Which marketing channel brings in the best customers?”, “Where do users drop off in the funnel?”, and “Which product lines are most profitable?”.

Typical daily tasks of a data analyst

Data handling

• Write and run SQL queries to extract data from databases

• Clean and transform data so it is usable (handling missing values, duplicates, inconsistent formats)

• Build or improve reports in BI tools (Power BI, Tableau, Looker, Metabase, etc.)

• Check and update dashboards to ensure key metrics are up to date

Collaboration

• Help define and track key performance indicators (KPIs)

• Answer questions from business teams (marketing, product, sales, operations)

• Communicate results in a simple, visual way in meetings and propose next steps

Core skills for data analysts

Technical skills

• SQL, the foundational language for querying databases

• Programming basics (often Python or R for more advanced analysis)

• Dashboards and reporting tools like Power BI, Tableau, Looker, Qlik, or Metabase.

• Data extraction and integration, sometimes with a light use of ETL/ELT tools (Fivetran, Airbyte, dbt)

Analytical and statistical skills

• Basic statistics (averages, medians, percentiles, variance, distributions)

• Ability to interpret trends and seasonality in time‑series data

• Understanding concepts like correlation vs. causation and common pitfalls in analysis

• Familiarity with A/B testing basics (in more product/growth‑oriented roles)

Business and communication skills

Effective data analysis starts with the ability to understand business objectives and translate them into clear, measurable questions. Analysts must then structure their work as a coherent narrative through data storytelling, to communicate insights clearly to non-technical stakeholders. Strong stakeholder management skills are also critical to clarify requests, manage expectations, and prioritize analytical efforts.

How to become a data analyst

Typical data analyst profiles often come from degrees in statistics, math, computer science, economics, engineering, or business. But many enter via specialized master’s programs, professional training, or bootcamps.

Career switchers are common too: marketers, finance or operations professionals, and self‑taught people who build a solid portfolio can all move into analytics. What matters most is not the original diploma but the ability to work hands‑on with data and answer concrete business questions.

1. Learn the fundamentals

Start with spreadsheets: clean data, calculate metrics, build simple dashboards.

You’ll also need to learn SQL to extract data from databases, and get comfortable with at least one BI tool (even if it’s a free/open‑source one).

2. Build real projects & create a portfolio

Pick publicly available datasets (e.g., open data from governments, e‑commerce samples, mobility/transport datasets), and create dashboards that answer realistic questions: revenue by product, retention by cohort, marketing funnel, etc.

You can then showcase these projects on GitHub or in a personal website/Notion page, including screenshots and a clear documentation.

💡 To understand how analysts collaborate with other data roles, read our overview of data jobs

If you’re more interested in building data systems, see data engineer

If you prefer machine learning and advanced modeling, see data scientist

The data analyst role offers a practical entry into the world of data careers, blending technical skills like SQL and BI tools with immediate business impact. Whether you're a student, a career switcher, or someone who loves turning numbers into stories, this path rewards hands-on projects, clear communication, and curiosity about real-world problems.

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