What is data science and data analytics?
From predicting market trends to personalising healthcare, data-driven decisions power modern organisations. As a result, nearly every industry needs skilled data professionals. Whether you’re exploring new career paths or looking to build on your existing technical skills, understanding the fundamentals of data science is your first step. Here’s what you need to know about becoming a data professional.
What is data science?
Data science uses data to answer questions, solve problems, and guide better decisions. It’s hugely important across all industries. Retailers rely on data science to forecast demand. Businesses apply it to understand customer retention. Public-service organisations use it to pinpoint where they can improve services.
Organisations collect information from websites, customer interactions, financial systems, digital products, research studies, and operational processes. Data scientists transform that raw information into actionable insight.
This work involves far more than creating charts or reports. Data scientists must define the right questions, organise and analyse complex datasets, test hypotheses, and clearly explain their findings.
What does a data professional do?
Data professionals regularly:
- collect and prepare data
- audit and improve data quality
- identify patterns and trends
- build predictive models
- apply machine learning techniques where appropriate
- create visualisations and interactive dashboards
- explain complex findings to non-technical colleagues
- advocate for ethical, private, and responsible data use
Their work usually begins with a specific problem. They investigate why a process takes too long, what factors drive demand, or which customers require better support. They then determine which data source will best answer that question.
What skills do data professionals need?
Data professionals draw on a blend of technical and practical skills, including statistics, programming, data management, and communication.
While technical expertise forms the foundation of data science, contextual understanding matters just as much. Exceptional data scientists understand their organisation’s goals, recognise the limitations of their data, and evaluate the real-world impact of their findings.
Is data science the same as data analytics?
The two fields overlap significantly.
Data analytics focuses primarily on examining historical data to measure performance, answer defined questions, and highlight current trends.
Data science encompasses analytics but expands further – building predictive models, implementing machine learning algorithms, and processing unstructured or highly complex datasets.
Because employers define job titles differently, you should evaluate the listed responsibilities in a job description rather than relying solely on the job title.
Where do data professionals work?
Data professionals drive decision-making across almost every industry, including:
- technology
- finance
- retail and e-commerce
- healthcare
- education
- government and public services
- manufacturing
- marketing
- transport and logistics
This broad application means existing industry knowledge can accelerate your data career. For instance, a background in healthcare helps you deliver immediate value in health-data projects, while commercial experience allows you to apply data science directly to customer and revenue analysis.
How can you become a data professional?
Professionals transition into data roles from diverse backgrounds, including computing, engineering, mathematics, natural sciences, finance, and business management.
Earning a master’s degree provides a structured framework for upskilling in data analysis, technical tools, and problem-solving methods – giving you the knowledge to kickstart your career.
The Roehampton difference
Roehampton’s online data programmes provide a flexible learning route designed for busy professionals. We build our curricula around modern workplace demands to ensure you graduate with the exact skill set employers seek.