Data Science with Foundation Year

At least 3 GCSEs including

• GCSE Maths, Grade C or Grade 4 or above. 

• GCSE English, Grade C or Grade 4 or above. 

If you don't hold GCSEs, we accept 45 credits at Level 2 or equivalent.

If your qualifications are older than 3 years we also ask to see a professional CV.

We strive to support students to succeed, whatever their background, and we encourage you to apply to study with us even if you don’t meet the academic requirements.

We view all applications holistically – and will consider a minimum of 12 months professional work experience (evidenced with a CV and reference) if you don't meet the academic entry requirements.

You'll also be required to submit a motivation letter that outlines:

• your motivations for choosing this course

• how your work experience has prepared you for the Foundation Year course

• how the course aligns with your career goals.

Thinking of transferring institutions or have you studied before? Your prior learning could mean you're exempt from some modules, bringing you closer to your goals.

Unlock the value of your past learning and experience with our recognition of prior learning process. Simply consult with our Study Advisers, and submit a recognition of prior learning form alongside your application.

If you’re a mature student and you don’t meet our entry criteria, you may be eligible to enrol through our mature student process.

You may be required to submit a CV and a short supporting statement of 500 to 600 words. This should set out why you want to study this programme and how it will support your career plans.

We also consider a variety of qualifications including Functional Skills Qualifications, BTEC Firsts and National Vocational Qualifications.

Overseas qualifications may be accepted, and will be subject to evidence of equivalency normally verified through ECCTIS (UK ENIC).

If you’re a non-native English speaker, you’ll be asked to provide proof of your English language proficiency in one of the following forms.

Alternatively, you may be accepted if you have previously studied in English at an appropriate level and attended a recognised institution.

IELTS grade of 6.0 overall or above, with no element below 5.5.

An online English test is offered (SPEEXX) if IELTS not available. 

TOEFL minimum 80 points.

Trinity College ISE – ISE III at minimum pass. Please note that Trinity College ISE academic results are only valid for two years.

Duolingo English test minimum 95 points. Take Duolingo’s online English test. It takes just 45 to 60 minutes, and you’ll receive your results within two days. You can then send your certificate to us.

Cambridge Certificate minimum B grade overall.

• Foundations of learning

  Discover how to effectively learn and communicate within teams, manage your time, and set goals, laying a crucial foundation for your academic success.

• Academic integrity and writing for business

  Learn about the demands of academic writing, the importance of sourcing correctly, and how to communicate effectively in a business context.

• Personal career plan

  Explore various job search processes, identify your core strengths, and learn how to build a personal brand that stands out.

• Elective: Foundation Year: (see Electives: Foundation Year)

• Applied learning strategies

  Enhance your self-study techniques, develop communication skills, and learn how to effectively present and reflect on challenges and setbacks.

• Maths foundations – tech related track

  Deepen your understanding of essential math concepts and learn the proper terminology for math problems, preparing you for more advanced studies.

• Digital skills – tech related to track

  Examine the impact of digital transformation on various professions and gain the digital skills needed for a successful career.

• Project: innovation and investigation

  Combine all the knowledge acquired during the Foundation Year in a comprehensive project related to your subject area.

• Introduction to data science

  Understand what data science entails, its significance in business, and the tools and techniques used to make data-driven decisions.

• Introduction to programming with Python

  Learn the fundamentals of Python programming, focusing on its application in data science tasks and projects.

• Mathematics: analysis

  Explore differential and integral calculus, setting the groundwork for more complex mathematical concepts.

• Statistics: probability and descriptive statistics

  Dive into the basics of probability and statistics, laying the foundation for understanding data analysis and inference.

• Object oriented and functional programming with Python

  Advance your Python skills by learning about object-oriented and functional programming paradigms and their application in data science.

• Data quality and data wrangling

  Learn the importance of data quality and techniques for preparing data for analysis and visualisation.

• Mathematics: linear algebra

  Study the fundamentals of linear algebra, focusing on vectors, matrices, and linear transformations.

• Statistics - inferential statistics

  Gain deeper insights into inferential statistics, focusing on hypothesis testing and estimation methods.

• Introduction to academic work

  Explore the fundamentals of producing academic work, understanding the distinction between scientific and everyday knowledge and learning good research practices.

• Database modelling and database systems

  Learn about relational databases, SQL, and the basics of NoSQL databases to manage and access large datasets.

• Explorative data analysis and visualisation

  Understand how to perform exploratory data analysis and use visualisation techniques to communicate data insights effectively.

• Data science software engineering

  Discover the software engineering principles underlying data science, including project management techniques and the application of agile methodologies.

• Machine learning – supervised learning

  Delve into supervised learning techniques, focusing on regression, classification, and decision trees.

• Machine learning - unsupervised learning and feature engineering

  Explore unsupervised learning methods, dimensionality reduction, and techniques for designing effective features for machine learning models.

• Elective A (see Electives A section below)

• Elective A (see Electives A section below)

• Neural nets and deep learning

  Study the principles of neural networks and deep learning, covering network architectures, training techniques, and their applications.

• Seminar: ethical considerations in data science Examine the ethical implications of data science practice, including the responsible use of data and the impact on society.

• 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 your comprehensive Data Science knowledge to tackle a theoretical or empirical problem, presenting your findings in an academic dissertation.

• Foundations of academic English

  Enhance your writing skills by mastering the nuances of language, grammar, and the importance of academic integrity. This elective is compulsory for international students.

• Skills for business

  Learn the essentials of navigating a business environment, from writing emails to delivering presentations and conducting meetings.

• Developing a business mindset*

  Gain insight into building a business mindset, embracing failure, and learning sustainability practices within business and society.

• Business ethics

  Understand the role of ethical principles in business practices, focusing on social justice and sustainability.

*Please note, this elective will be available to study from April 2025.

• Project: from model to production

  Learn how to deploy a predictive model into a production environment, addressing practical challenges such as data storage and model monitoring.

• Project: build a data mart in SQL

  Apply your knowledge of databases to design and implement a data mart using SQL, focusing on effective data schema design and query optimisation.

• Agile project management

  Understand agile project management principles and apply them to manage projects efficiently in a data science context.

• 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 business environment. 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

  Understand the principles of business intelligence, learning to manage and analyse business data effectively.

• Project: business intelligence

  Implement a BI solution to a real-world problem, demonstrating your ability to apply BI concepts practically.

Sales and marketing

• Applied sales I

  Dive into the world of sales with a focus on the frameworks, strategies, and skills necessary for effective sales in diverse economic sectors, preparing you for roles that require persuasive communication and strategic thinking.

• Applied sales II

  Build on your sales knowledge with advanced concepts in customer relationship management, sales promotions, and strategic sales planning.

Supply chain management and industry 4.0

• Supply chain management I

  Gain insights into the complex dynamics of supply chains. Learn about modern supply chain management concepts, including logistics, procurement, and the optimisation of production and delivery processes.

• Supply chain management II

  Explore strategic supply chain management with an emphasis on planning, sourcing, making, delivering, and returning processes. Learn to assess and mitigate risks while optimizing cost efficiency and customer satisfaction.

Data engineering and big data technologies

• Big data technologies

  Understand the infrastructure behind big data projects. Learn about data types, database concepts, and the frameworks required for effective data storage, processing, and analysis in large-scale data environments.

• Cloud computing

  Discover the fundamentals and strategic importance of cloud computing in data science and AI. Learn about different cloud service models, enabling technologies, and how to make informed decisions for cloud-based solutions.

Artificial intelligence

• Artificial intelligence

  Delve into the historical and contemporary perspectives of artificial intelligence. Understand the principles of machine learning, natural language processing, and computer vision, and their applications in developing intelligent systems.

• Project: artificial intelligence

  Apply your AI knowledge in a practical setting. Design and implement an AI solution for a given problem, considering theoretical and practical aspects of AI to develop an effective, innovative application.

Banking and finance

• Crypto and blockchain

  Explore the revolutionary technologies behind cryptocurrencies and blockchain, understanding their roles, evolution, and impact on global finance and digital transactions.

• Fintech

  Dive into the transformative world of financial technology, covering innovations that reshape banking, investment, and financial services through AI, data analysis, and emerging digital platforms.

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

  Master the art of online marketing. Learn to craft strategies that leverage digital channels to enhance brand presence, engage customers, and drive sales in a digital-first world.

• Social media marketing

  Harness the power of social networks. Develop skills to create content, manage communities, and measure the impact of social media campaigns, aligning social media strategies with broader marketing goals.

Supply chain management and industry 4.0

• Product development in industry 4.0

  Navigate the product development landscape in the era of industry 4.0. Understand digital approaches to design, prototyping, and manufacturing that meet the demands of modern markets.

• Project: smart product solutions

  Design a smart product by applying concepts of IoT connectivity, and user-centred design. Demonstrate how digital technologies can create innovative product solutions that address real-world needs.

Data engineering and big data technologies

• Data engineering

  Equip yourself with the skills needed for effective data management. Learn about database architectures, data processing patterns, and the tools and techniques used to build scalable data pipelines.

• Project: data engineering

  Implement a data engineering project that showcases your ability to collect, store, process, and manage large datasets. Design a solution that demonstrates best practices in data engineering for real-world applications.

Artificial intelligence

• Self-driving vehicles

  Explore the technologies driving autonomous vehicles. Learn about the sensors, algorithms, and machine learning models that enable vehicles to navigate and operate without human intervention.

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

  Investigate the latest breakthroughs and challenges in autonomous driving. Analyse current research, technological advancements, and the ethical and societal implications of self-driving vehicles.