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
Most data scientists enter the profession with a degree in data science, computer science, or a related field.
At LIBF, our degrees are designed for different stages of your education. Our BSc Data Science with Foundation Year helps you build a strong foundation in core principles. Our BSc Data Science and MSc Data Science allow you to master advanced data science techniques, including quantitative algorithms, statistical learning, and data processing.
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
Tools like Tableau, Power BI, and Matplotlib 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 tools like Hadoop, Spark, 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
Many data scientists use various tools to analyse, visualise, and manage information. Common things used in the data science process include:
Python and R
Data scientists use these programming languages for data analysis, mining, and automation. They'll also use libraries like Pandas and NumPy to make data manipulation efficient.
SQL
SQL is vital to querying and managing large databases. It's also used for data extraction and organising structured datasets for analysis.
Tableau and Power BI
These programmes help visualise data, turning complex datasets into interactive dashboards and charts. They enable businesses to quickly apply insights for data analysis and decision-making.
Apache Hadoop and Spark
Big data frameworks that process and analyse massive datasets. These tools support data mining and data staging, helping manage large-scale information across distributed systems.
Jupyter Notebooks
An interactive environment that simplifies data exploration and visualisation, it’s ideal for documenting workflows and sharing insights in real time.
SAS and SPSS
Software used for advanced data analytics and statistics. These tools generate insights from large datasets to support decision-making.
What will you learn in a data science degree?
A data science degree 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 visualization 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.
At LIBF, our BSc Data Science and MSc Data Science programmes are designed to give you hands-on experience, building on these essential skills while offering specialised electives. Whether you want to focus on areas like supply chain analytics, sales and marketing, or even computer engineering, our degrees are built to align with your career aspirations.
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:
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.
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.
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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 LIBF, 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.
Learn data science with LIBF
At LIBF, you can study data science 100% online with complete flexibility, whether you’re at the start of your career or looking to advance. Our programmes, from foundation year to master’s, fit around your life, with 24/7 access to personalised support from your module lecturers, study coaches, and AI-powered tools.
Courses cover essential topics like big data, machine learning, and predictive analytics. You can tailor your studies with specialised electives, preparing you to excel in data-driven industries.
