Data analyst

A data analyst collects, analyzes, and interprets data to help companies make informed decisions.

On this profession page, you will learn:

Who is data analyst

A data analyst immerses themselves in the world of data every day, searching for valuable information and trends. They start by collecting data from various sources, such as databases, surveys, or reports. Then they analyze this information using specialized programs and methods.After collecting the data, the data analyst cleans it, removes inaccuracies, and prepares it for further analysis. They actively use statistical methods to test hypotheses and identify significant relationships. Through data visualization, they create understandable graphs and charts that help highlight key results.On a daily basis, the data analyst collaborates with teams, discussing findings and proposing solutions for projects. They often present the results of their research, articulating their thoughts clearly and accessibly so that all project participants can understand the implications of the conclusions drawn.Additionally, the data analyst is always learning new technologies and methods, constantly improving their skills. They attend seminars, read scientific articles, and exchange experiences with other professionals. Through this activity, the data analyst remains at the forefront of data understanding, applying new approaches in their work.Thus, the data analyst does not just work with numbers but transforms raw information into insights that can change company strategies and influence decision-making. Moreover, they become a bridge between data and business goals, helping teams achieve success.

AI impact on data analyst

Medium risk

AI replacement risk

50%

Most of the time you spend pulling data, cleaning it, and running standard analyses — like building dashboards, spotting simple trends, or fitting basic models — will increasingly be handled by AI‑driven tools that can do it faster. What stays is the work of deciding what numbers actually matter for the business, explaining those insights to non‑technical teammates, and making sure the data you use follows privacy rules. You’ll also still be involved in shaping how databases are set up and guiding projects that need human judgment. Expect your day to shift from crunching numbers to more conversation and sense‑making.

Tasks at risk of automation

Data cleaning and preparation
Routine trend identification
Automated report generation
Basic predictive model building

Tasks that will remain human

Defining business metrics/KPIs
Interpreting results for stakeholders
Ensuring data privacy/compliance
Cross‑functional collaboration

Key skills of data analyst

analytical thinking system thinking calculation and forecasting data analysis and mathematical modeling project management data mining cloud computing cybersecurity and application security time management and prioritization coordination and time management

Work schedule and conditions

The work schedule of a data analyst typically involves working in an office from Monday to Friday. The duration of the workday is usually 8 hours, but exceptions may occur depending on the company and specific projects. Some companies offer flexible work schedules that allow for remote work a few days a week or flexible starting hours. The possibility of remote work may also depend on the specific role and level of experience of the data analyst. Data analysts working on confidential projects or with sensitive data may need to work in the office or in a secure work environment. In some cases, the job may require travel, especially if it involves freelance work or consulting services for various clients.

What a data analyst does

Definition of metrics and key performance indicators (KPI).
Creation and maintenance of databases.
Data analysis and the identification of trends and patterns.
Development of predictive models and scenarios.
Preparation of reports and presentations of analysis results.
Collaboration with teams from different departments.
Compliance with privacy and data protection standards.

Benefits of the data analyst profession

Interesting work

Allows you to explore data, find hidden patterns, and make discoveries.

Influence on Decision-Making

The results of data analysis impact the strategic decisions of the company.

Professional Development

The opportunity to constantly learn, develop, and master new technologies and tools.

Disadvantages of the data analyst profession

Detailed work

The need to pay attention to details, which can be exhausting and requires focus.

Technical issues

Possible difficulties with software, data processing, and technical limitations.

Versatility

The necessity to combine various skills: technical, analytical, communication, and business knowledge.

How to become a data analyst

You can become a data analyst without a degree — what matters most is hands-on skill with data and a portfolio of projects. The fastest route in the UK is a practical bootcamp or short course covering SQL, Python, Excel and visualisation tools like Power BI. A maths or statistics degree helps but isn't required, and no licence is needed.

1. Bootcamps and short courses

The quickest entry is a data-analytics bootcamp or short course, lasting from a few weeks to a few months. You'll learn SQL, Excel, Python and visualisation (Power BI or Tableau) and build a portfolio, which is enough to apply for junior roles without a degree.

2. Apprenticeships

A data analyst apprenticeship lets you earn while you learn, combining paid on-the-job work with structured training. It's a strong UK route that builds real experience and a recognised qualification, with no tuition fees and no degree required.

3. Self-teaching and certifications

Many analysts are self-taught using free and paid online courses and vendor certifications (for example Google, Microsoft Power BI or AWS). Working through real datasets and publishing the results builds the portfolio that employers actually look at.

4. University degree

A degree in statistics, economics, maths or computer science gives a solid theoretical grounding and an understanding of analytical methods. It's the longest path (three to four years) but useful for more complex analytical and research roles.

The fastest start is a practical bootcamp or short course in SQL, Python and visualisation, backed by your own portfolio of projects. An apprenticeship is a great earn-while-you-learn alternative, and a degree helps for deeper analytical roles. What counts is being able to work with data, not just holding a diploma.