Online degree BSc (Hons) Data Science

• Introduction to data science

  Learn how to integrate and use data-driven approaches in business to make effective operational decisions and create value from data. Gain insight into statistics and machine learning, including an overview of relevant methods and approaches. 

• Introduction to programming with Python

  Develop a foundational understanding of the Python programming language. Learn about Python’s role in data science-related programming and programming concepts like variables, data types, and statements.  

• Mathematics: analysis

  Learn the fundamentals of differential and integral calculus and understand their related concepts. This module builds skills to formulate and solve various science and technology-related problems.   

• Statistics – probability and descriptive statistics

  Learn the essentials of probability and descriptive statistics. Explore random variables, probability density distributions and key statistical concepts. Understand inequalities and limit theorems. 

• Object-oriented and functional programming with Python

  Build upon your knowledge of Python programming to understand advanced concepts, functions, and object-oriented programming notions like classes, objects, and design principles.  

• Data quality and data wrangling

  Explore techniques for acquiring, formatting, and tidying data to make it suitable for subsequent analysis to unlock business value. Learn about key methods for data quality management.  

• Mathematics: linear algebra

  Numerous scientific and engineering challenges can be solved with linear algebra. This module introduces the subject and basic notions like vectors and matrices. Learn about solutions for problems in analytical geometry and develop a problem-solving skillset. 

• Statistics – inferential statistics

  An introduction to statistical analysis and Bayesian techniques. Learn to estimate and optimise parameters. Understand statistical and systematic uncertainties, statistical testing, and decision theory. 

• Introduction to academic work

  The application of good scientific practice is an academic fundamental. Develop the ability to write strong scientific papers and get an overview of different examination formats and requirements. 

• Database modelling and database systems

  Develop knowledge of relational database systems, data schemas and modern DB systems (NoSQL) for storing and accessing data. Learn how to store data in relational models and access stored data with SQL. 

• Explorative data analysis and visualisation

  This module introduces useful approaches, tools, and techniques to analyse complex data sets. Examine detailed visualisation principles and techniques to present analytical outcomes.  

• Data science software engineering

  This module gives a detailed overview of data science methods and paradigms to develop enterprise-grade models and bring them into production. This module explores traditional and agile project management techniques and software development paradigms, including pair, mob, and extreme programming. 

• Machine learning – supervised learning

  Develop knowledge of large margin classifier concepts and tree-structured models. Gain an understanding of machine learning, with a focus on supervised learning, like labelled data. 

• Machine learning – unsupervised learning and feature engineering

  This module provides tools and techniques for unsupervised learning, like machine learning approaches, and feature engineering. Learn relevant machine learning methods to find robust and meaningful features. 

• Elective A (see "Electives A" section below)

• Elective A (see "Electives A" section below)

• Neural nets and deep learning

  Learn how feed-forward networks are set up and trained, and how to avoid overtraining. This module also covers common network architectures and highlights design choices and data collection impacts. 

• Seminar: ethical considerations in data science

  This module explores ethical issues in relation to data science methodologies and techniques which are an everyday part of contemporary life. Develop your proficiency in applicable techniques and understand how to practice ethical data science. 

• Elective B (see Electives B section below)

• Elective B (see Electives B section below)

• Elective C (see Electives C section below)

• Elective C (see Electives C section below)

• Undergraduate (bachelor) thesis

  Apply the subject-specific and methodological competencies learned throughout your course to present an academic dissertation. You’ll also learn how to tackle a practical-empirical or theoretical-scientific problem.

• Project: from model to production

  Gain hands-on experience with integrating predictive models into enterprise-grade applications or services. In this project-based module, consider aspects like data storage, processing, and service availability.   

• Project: build a data mart in SQL

  In this module, apply database theoretical knowledge, methods, and approaches to solve a real-world scenario. In this case study, implement your design and architectural choices in a functioning database.

• Agile project management

  Gain a practical introduction to agile project management and learn to distinguish it from plan-driven approach. Learn the values, activities, and roles of typical agile procedures and practice with an example project.

• Self-arranged internship I (*)

  Gain practical and analytical skills by securing and completing an internship, enhancing your employability. In preparation tutorials, explore workplace dynamics and set personal goals for your placement.

• Self-arranged internship II (*)

  Building on your first experience, this self-arranged internship lets you apply academic skills in a real-world entrepreneurial setting. Focus on refining your communication, problem-solving, and time management skills.

*You'll be responsible for arranging a suitable internship for your field of study. Once arranged, please complete an application to the internship module via myCampus, to make sure your internship can be recognised and credited towards your course requirements.

Business intelligence and data analytics

• Business intelligence

  This module introduces the procedures and models for data provision, information generation and analysis. Explore data warehousing and develop techniques to optimise business activities. 

• Project: business intelligence

  Develop your very own BI prototype, mastering how to overcome common challenges while in the design and practical implementation of small business BI solutions.

Sales and marketing

• Applied sales I

  Globalised demand and intense competition mean that winning customers is increasingly difficult. So, effective sales thinking is vital. Examine key concepts like sales organisation and alternative channels – plus, fine-tune your negotiation abilities.

• Applied sales II

  Deepen your knowledge of fundamental sales principles. Explore how customer satisfaction and loyalty contribute to successful sales management and apply your learning in a real-world case study.

Supply chain management and industry 4.0

• Supply chain management I

  Gain a theoretical and practical view of supply chain management (SCM). You’ll consider logistical and modern processes, flows and network standards for SCM. 

• Supply chain management II

  Learn how to build and maintain a competitive advantage through robust SCM. Analyse strategic activities and instruments in the Plan, Source, Make, Deliver, and Return process categories. 

Data engineering and big data technologies

• Big data technologies

  This module introduces the four ‘Vs’ of data – and data sources and types. Learn about the most common data storage formats and the challenges large amounts of data pose for underlying infrastructure.

• Cloud computing

  An introduction to cloud computing, its enabling technologies, and analytics capabilities. Learn about cutting-edge advances like serverless computing, storage, and popular cloud offerings. 

Artificial Intelligence

• Artificial Intelligence

  Artificial intelligence has captured our attention for decades. Explore the successes and setbacks of AI over the years, learn about modern systems and find out how you can be a part of AI’s rapid development.

• Project: Artificial Intelligence

  Take on the challenge of designing and developing your own AI system. You’ll consider the application requirements, practical constraints and desired output as you put your knowledge into practice.

Banking and finance

• Crypto and blockchain

  Consider the growth of crypto and blockchain and the evolution of money, from coins to fiat currencies, plastic, and crypto assets. Develop an understanding of the current state of the market and its principles. 

• Fintech

  There has been a huge increase in the number of technological finance solutions in recent years. Uncover the main sectors targeted by Fintech companies. Learn about the current state of the market, technological solutions, and its future direction. 

Business intelligence and data analytics

• Advanced data analysis

  Get up to speed with the many analytics platforms widely used in business today. This module looks at everything from measuring business performance to text mining, from social media analytics to the current trends in experimental design and setup.     

• Project: data analysis

  It’s time to put your analysis techniques and knowledge to the test in the real world. Develop a report as you apply advanced analytical methods to simulate work in a professional data science environment.

Sales and marketing

• Online marketing

  Examine different types of online marketing such as advertising campaigns and email marketing. You’ll compare channels, consider legal aspects like GDPR and evaluate campaigns with web analytics.

• Social media marketing

  Social media has evolved from a private means of communication to a commercial advertising tool. Explore how to integrate channels like Facebook, Instagram and Pinterest into a company’s marketing mix. 

Supply chain management and industry 4.0

• Product development in industry 4.0

  In the context of the fourth industrial revolution, you’ll look at the impact of new trends on product development. Consider how alternative approaches to design put the consumer at the centre.

• Project: smart product solutions

  In a practical project framework, showcase how you’ve applied agile engineering methods to smart product solutions. And demonstrate you can choose the right tools to analyse different business models.

Data engineering and big data technologies

• Data engineering

  Explore the important foundational concepts in data engineering: storage infrastructure, data security, systems architecture, developments in storage technology, and the logic of data pipelines.

• Project: data engineering

  Create your own portfolio by working on a real data engineering project. Perhaps you’ll want to set up a Docker container environment. Or how about building a data pipeline according to DataOps principles? The choice is yours.

Artificial intelligence

• Self-driving vehicles

  Focus on the safety standards and IT security of autonomous vehicles. There’s a lot to explore – sensor fusion, feature detection, calibration, localisation, satellite-based systems, and motion planning are just a few areas to get you started.

• Seminar: current topics and trends in self-driving technology

  Uncover the most recent developments of autonomous vehicles. Discover the technical advances and philosophical issues and how they’ll affect law, society and many industries. You’ll then apply your new knowledge in a research essay.

You can find more details about your BSc (Hons) Data Science course in these PDF files:

• Online programme schedule: BSc (Hons) Data Science

• Programme specification: BSc (Hons) Data Science

• Once enrolled, you will be expected to meet Walbrook's policies and standards.

• Walbrook has Taught Degree Awarding Powers (TDAP).

An online BSc Data Science is a worthwhile investment to kickstart your career in this exciting field. With this qualification, you'll gain foundational knowledge and practical skills that will prepare you for success. You'll enjoy the flexibility to study online at your own pace and on your own schedule, while still receiving the same high-quality education as an on-campus programme.

Furthermore, our programme emphasises programming, machine learning, and deep learning, equipping you with the expertise needed for a career in today’s data science industry. Put simply, data science degree is a valuable opportunity to propel your professional journey forward.

Learning data science online can be challenging, but our programme is designed to make complex topics in data science accessible, with engaging course content, extensive online resources, and learning support from your Study Coach.

Whether you’re new to data science or building on existing knowledge, you’ll have all the resources you need to learn confidently and make progress at your own pace.

Yes, a BSc Data Science course includes coding as a fundamental component of the curriculum. In the first year of study, you'll study modules on topics such as Introduction to Programming with Python and Object-Oriented and Functional Programming with Python. These modules introduce you to essential programming concepts, equipping you with the skills needed to work with data using programming languages.

As data science continues to be a rapidly growing field, proficiency in coding is essential for success in the industry, making these courses vital for a bachelor's degree in data science.

Our BSc Data Science and BSc Computer Science degrees both provide a solid foundation for aspiring data scientists. These online degrees offer comprehensive training in data principles, statistical models, programming, and machine learning.

With flexible study options, you can fit your studies around your life and commitments, making it easier to pursue a bachelor's degree and gain valuable and highly sought-after skills in data science. Both of these degree courses will equip you with the knowledge and skills employers look for when employing data scientists.