What is data science?
Data science involves transforming raw data into valuable insights through data processing and predictive analysis. It combines statistical and mathematical skills, algorithms, and technology to create meaningful patterns from vast datasets. At its core, it aims to solve real-world problems by using relevant data – enhancing customer experiences, streamlining supply chains, or advancing medical research and innovation.
Data science professionals work with information at every stage – from collection and cleaning to analysis. By blending computer science with industry-specific expertise, they develop tailored solutions to address unique organisational challenges. This field often intersects with business analytics to make data-driven decisions, playing an increasingly critical role in nearly every industry.
How to become a data scientist?
Study a degree in data science
Many data scientists begin with a degree in data science, computer science or a related field. At master’s level, you can explore data science more deeply – studying topics like data processing, machine learning, and advanced analytics. Our MSc Computer Science with Data Science is a great option, combining a solid computer science foundation with in-demand data science skills.
Develop practical job experience
Hands-on experience is essential for becoming a data scientist. Many start with internships or entry-level roles in data entry, data reporting, and data analysis.
These roles allow you to build relevant skills in practical settings, often working with large amounts of data, and allow you to showcase your abilities to potential employers.
Certifications and continual learning
Specialised certifications in areas like cloud computing, and data architecture can enhance your skills. These courses deepen your understanding of key tools and techniques, complementing formal education.
Some online platforms offer certifications that improve coding skills, making you even more competitive in the job market.
Data science skills
Programming languages
Proficiency in languages like Python and R is essential for cleaning, analysing, and manipulating data. These tools support data mining and allow you to build data models that automate tasks. SQL is also critical for querying databases to extract data for everyday tasks.
Statistics and probability
Understanding statistics and probability is key for working with qualitative and quantitative data. These concepts help identify data patterns and make predictions. Knowledge of statistical tests, distributions, and data modeling ensures you can interpret data accurately and make informed decisions.
Data visualisation
Data visualisation tools help simplify complex datasets into easy-to-understand visuals. Effective data visualisation builds models that enable businesses to act quickly on insights. It’s crucial for making data science innovations accessible and useful.
Machine learning and AI
Allow systems to process data and make decisions on their own. Algorithms like regression, clustering, and decision trees help build predictive models, driving many data science innovations in automation and decision-making processes.
Big data technologies
Working with vast datasets requires expertise in specialised data-processing tools and cloud-based platforms. These technologies enable data scientists to process, store, and perform data cleansing at scale, crucial for making big data actionable.
Data ethics and governance
Ensuring data privacy and security is crucial. Data scientists must follow laws like GDPR, maintain data integrity, and responsibly handle sensitive information. Ethical data use builds trust and prevents misuse, making it a core aspect of data science.
Data scientist job description
As a data scientist, you'll play a central role in helping organisations make decisions. A typical day might involve uncovering meaningful insights from existing data to forecast customer behaviour or conducting qualitative analysis to optimise business processes.
Key responsibilities as a data scientist include:
Data acquisition, including gathering and organising data from multiple sources
Applying statistical methods to support data analytics
Data extraction, preparation, and cleansing
Preparing and organising training data for use in predictive models
Creating visualisations to present data insights
The role of a data scientist requires technical expertise and problem-solving skills to turn business data into actionable insights. Effective communication skills are crucial for working with teams across different departments and ensuring the effective implementation of data-driven strategies.
Data science tools
There’s no single toolkit for data scientists use to analyse, visualise, and manage information. The best way to build confidence is by exploring a wide range of tools and platforms that reflect how people actually work in the field.
Programming environments
Tools like Python and libraries such as Pandas or Matplotlib help clean, analyse, and visualise data efficiently. These are essential for scripting tasks, developing models, and working with large datasets.
Databases and querying tools
Understanding both SQL and NoSQL systems is vital for working with structured and unstructured data. Knowing how to extract and manipulate data is core to most roles in data science.
Visualisation and dashboarding platforms
Software like Power BI and Tableau allows you to create charts and dashboards that bring data stories to life – helping non-technical audiences make sense of complex patterns.
Machine learning libraries
Libraries such as Scikit-learn, TensorFlow and PyTorch are commonly used to build models that can classify, predict, or recommend – whether you're automating processes or personalising user experiences.
Cloud and collaborative tools
Tools like Google Colab let teams experiment with models and share findings easily. As remote and distributed teams become more common, experience with cloud-based workflows is increasingly important.
What tools will you learn studying data science at Walbrook?
At Walbrook, you won’t just hear about data tools – you’ll use them. Throughout the MSc Computer Science with Data Science degree, you’ll gain hands-on experience with platforms used in industry today, including:
Python and cloud-based notebooks like Google Colab for coding, analysis, and machine learning.
SQL and NoSQL databases through modern database systems modules.
Data science libraries and frameworks that could include Pandas, Matplotlib, Scikit-learn, TensorFlow and PyTorch.
This practical experience gives you the confidence to: build realw-rodl data worklflows, understand how modern data teams operate, and confidently apply for technical roles that require more than just theoretical understanding.
What will you learn in a data science degree?
A data science degree, or a computer science degree with a data science specialism, prepares you for a field that blends technology, mathematics, and creativity. You'll explore various topics that build the essential skills to work with data at scale. Here’s a glimpse of what you’ll typically cover:
Programming and data engineering: learn core programming languages like Python or R and how to structure data using modern data engineering principles.
Statistical analysis and data modelling: explore statistical methods, from descriptive statistics to complex predictive models, and learn the tools to model and interpret vast datasets.
Machine learning and AI: dive into the mechanics of neural networks and deep learning, which will pave the way for automation, AI systems, and big data innovation.
Data visualisation and reporting: master the art of transforming raw data into clear visualisations that help teams make informed, actionable decisions.
Database systems: understand how to design and maintain the underlying systems that store and manage your data, ensuring it's structured, accessible, and secure.
Is a computer science degree good for a career in data science?
If you're interested in data science but want to future-proof your career across the wider tech industry, a computer science degree with a data science specialisation could be the best way forward.
Our MSc Computer Science with Data Science combines essential computer science foundations with in-demand data science skills. You’ll explore everything from data modelling and analytics to machine learning, while also studying areas like modern databases, software engineering and cloud computing.
Studying an MSc in Computer Science with Data Science helps you:
Build strong data science capabilities on top of core computing skills
Work across tech and data-focused roles, from analyst to engineer
Understand how to manage and model data at scale
Keep your options open for roles beyond data science
What data science degrees can you study to become a data scientist?
If you’re aiming for a career as a data scientist, several degrees can help you get there. Each provides a unique perspective on data and the methods to turn information into powerful insights. Here’s a look at some of the key options:
Computer science degrees
A computer science degree provides essential programming, algorithms, and system architecture skills. These abilities are crucial for managing data, developing algorithms, and supporting large-scale data systems – all vital for a career in data science.
Data science degrees
A degree in data science is your gateway to the field. It focuses on converting raw data into actionable insights through programming, data analytics, and visualisation, laying the foundation for a career where data drives decision-making.
Business intelligence (BI) degrees
Focus on transforming information into strategic insights that fuel business performance. A BI degree helps you master techniques like data warehousing, reporting, and real-time analytics, helping organisations make informed decisions and monitor key performance metrics.
Data management degrees
Organise, secure, and make sense of vast amounts of data. A data management degree prepares you to work with sensitive information, teaching you how to manage data responsibly in industries like healthcare, finance, and beyond.
Business information systems degrees
Bridge the gap between IT and business operations. Studying business information systems gives you the skills to design systems that manage data and streamline business processes, turning information into actionable strategies that drive efficiency.
Artificial intelligence degrees
Expand the possibilities of technology through data. An AI degree takes you deep into the world of smart technology. You’ll study how machines learn, exploring systems that mimic human thinking –like recognising patterns, making predictions, and adapting over time.
If you're considering one of our online degrees, enter your details, including a valid phone number, and we’ll call to tell you more about the programme you’re interested in.
Data scientist salary
The demand for data talent in the UK is at an all-time high. According to Robert Half’s 2024 Salary Guide, data science professionals are among the most in-demand technical skills, and employers are finding it increasingly difficult to fill roles in this field.
The average salary for a data scientist in the UK is approximately £57,740 per year.*
Entry-level roles start around £42,000, and senior roles exceed £70,000, reflecting data expertise's growing importance and value.*
With the increasing demand for data scientists and the ongoing talent shortage in the UK, now is the perfect time to step into this dynamic field. Companies are eager to recruit skilled professionals, paving the way for a rewarding and future-proof career in a growing market.
*Salary information from glassdoor.co.uk is accurate as of October 2024.
Expected salaries for data science careers
Because of the sought-after range of skills you'll have, data scientists tend to have a wide range of career opportunities across various industries. Salary can differ based on the role, location, and level of experience, but here are some other roles you can do:
Data analyst: £34,606
Focus on interpreting structured data to generate reports and visualisations that help organisations understand trends and performance.BI analyst: £40,809
Use data to identify trends, make forecasts, and create dashboards that allow organisations to make informed business decisions based on past performance.Data engineer: £54,979
Build and maintain the systems and pipelines needed to effectively gather, process, and store large datasets.Machine learning engineer: £57,539
Develop machine learning models that enable systems to learn from data and automate decision-making processes.Big data engineer: £64,419
Build and manage the infrastructure for handling massive datasets. Design systems that ensure the smooth flow and processing of data, making it accessible for analytics and decision-making at scale.Quantitative Analyst: £76,451
Quants apply mathematical models and statistical techniques to financial data. Develop complex data models to assess market risks and optimise trading strategies.
Salary information from uk.indeed.com is accurate as of October 2024.
How data science interacts with other data disciplines
Data science vs business intelligence
Data science explores and interprets vast amounts of data to extract meaningful insights and create models that predict future trends. A data scientist relies on advanced automation techniques to uncover hidden patterns, often working with unstructured data. Their goal is to use data to drive innovation and inform strategic decisions.
In contrast, BI focuses on analysing historical data. BI professionals create reports and dashboards from structured data, offering insights into past performance to guide current business decisions.
Data science and machine learning
Machine learning is embedded into data science, allowing systems to create models and make predictions from data without needing explicit programming skills for each decision.
While data scientists use programming to set up and train these models, once operational, the models learn and improve over time, handling large datasets and automating decision-making. This approach is critical for tasks like recommendation engines, fraud detection, and predictive analytics, addressing challenges traditional methods can’t solve.
Data science vs data engineering
Data engineers build and maintain the infrastructure – data pipelines, databases, and systems –that ensures smooth data flow. They focus on creating reliable, scalable environments that can handle large volumes of data, using tools like Hadoop or Spark to prepare it for analysis.
A data scientist, in contrast, uses prepared data to extract meaningful insights. They use advanced analytical skills, statistical techniques, and machine learning to develop models that inform decision-making.
Data science career options
Data scientist
As a data scientist, you’ll lead the way in data analysis, using data wrangling and programming skills to work with large, unstructured datasets. You’ll use machine learning models and algorithms to make predictions, automate processes, and deliver insights that inform business strategy.
Data scientists work across finance, healthcare, and retail sectors, helping organisations stay competitive with data-driven solutions.
Data analyst
Data analysts turn information into usable insights for businesses. You’ll work with tools like Tableau to create visual representations and dashboards, summarising key findings in a way accessible to decision-makers. While your work may overlap with data scientists, you’ll focus more on analysing historical and structured data, providing insights that help guide present business choices.
Data engineer
Data engineers build and maintain the backbone of an organisation’s data infrastructure. You’ll design pipelines and systems that allow data to flow efficiently, working with big data tools like Hadoop and Spark to manage massive datasets. By ensuring data is accessible, organised, and reliable, data engineers are crucial in enabling both analysts and scientists to perform their roles effectively.
Data science degree requirements
Specific course entry requirements will vary according to course level and degree provider. At Walbrook, our entry requirements offer flexible entry criteria to ensure nothing is beyond your reach.
Typical entry requirements usually include:
Previous qualifications
A-levels or equivalent in mathematics, physics, or computer science.
Postgraduate applicants need a related undergraduate degree.
Mathematics skills
Strong grasp of maths, including algebra and calculus, for core topics like algorithms.
Knowledge of data systems and machine learning techniques is often helpful.
Relevant experience
Hands-on experience through internships or coding projects is highly beneficial.
Practical exposure helps show your readiness for data challenges.
Take the next step in data science with Walbrook Institute London
The demand for data talent is growing fast – and this could be your moment to join the field. Whether you're changing direction or building on your existing strengths, our MSc Computer Science with Data Science gives you a flexible, practical way to break into the industry.
You'll develop a strong foundation in programming, databases and systems, while specialising in key data science areas like analytics, machine learning, and big data modelling. And because it’s designed for non-computing graduates, you don’t need a tech background to get started.
Study 100% online, in a way that fits your life – and graduate ready to turn data into real-world impact.
Explore MSc Computer Science with Data ScienceFAQs about studying data science
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