Data science is one of the most used, and most misunderstood, terms in the modern workplace. Executives talk about it in board meetings. Job boards list thousands of vacancies for it. Yet most people cannot give a clear, precise answer to a simple question: what does a data scientist actually do?
This article answers that question plainly, explains what skills you need, and how to break into the field, without a degree in mathematics or computer science. If you are already clear on data science and want to go further, read our guide on why everyone needs to learn AI implementation.
What is data science?
Data science is the practice of extracting insights, patterns, and predictions from data using a combination of statistics, programming, and domain knowledge. At its core, a data scientist's job is to help an organisation make better decisions by turning raw data into actionable intelligence.
This could mean predicting which customers are likely to churn, identifying fraud in financial transactions, forecasting demand for a retailer, or building a recommendation engine for a streaming service. The outputs vary enormously, the underlying process does not.
What does a data scientist actually do day-to-day?
A typical day for a data scientist involves some combination of these activities:
- Data collection and cleaning, gathering data from databases, APIs, or files and making it usable (often the most time-consuming part of the job)
- Exploratory data analysis (EDA), visualising and summarising data to understand its shape and find patterns
- Modelling, applying statistical or machine learning techniques to build predictive or descriptive models
- Evaluation, measuring model performance and iterating to improve it
- Communication, presenting findings to stakeholders in plain English, often through dashboards or reports
What skills does a data scientist need?
You do not need a PhD. The skills that employers actually look for are:
- Python, the primary language for data science (pandas, NumPy, scikit-learn, Matplotlib). New to Python? See our beginner's guide to Python for data science.
- SQL, for querying databases, which most real-world data lives in
- Statistics, understanding distributions, hypothesis testing, and probability
- Machine learning fundamentals, regression, classification, clustering, cross-validation
- Communication, translating technical findings into business recommendations
Do I need a degree to become a data scientist?
Increasingly, no. Employers care about demonstrable skills and a portfolio of real work, not your degree subject. The most effective way to demonstrate competence is to complete a recognised qualification, build projects using real datasets, and show your work on GitHub or in interviews.