In this module, you’ll explore how data science fits within the wider worlds of computing, software engineering and real-world applications. You’ll look at the varied roles data scientists play in organisations. You'll understand how data management systems support effective analysis and decision-making. Alongside this, you’ll examine the ethical and legal responsibilities that shape data science practice and explore how scientific knowledge evolves over time. The module has a strong practical focus. We'll introduce you to command-line computing and programming using Python, building the skills needed to work confidently with data. You’ll design and develop data analysis code using tools such as NumPy and Pandas and create clear visual outputs using libraries like Matplotlib. You’ll apply these skills to realistic scenarios. You'll learn how to build standards-compliant data analysis models to support real organisational tasks. The module also introduces current web technologies and relevant scripting languages. It'll help you understand how data analysis operates within enterprise and web-based environments. By the end of the module, you’ll be better prepared to analyse data responsibly, write effective data analysis code, and apply data science techniques in ways that are practical, ethical and relevant to modern organisations.
Entering apprenticeship education is exciting; but it can also be a daunting experience. At ARU, we want all apprentices to make the most of the opportunities their training institution provides, so they reach their potential, become lifelong learners and find fulfilling careers. This module is designed to ease that transition. You will be enrolled on to this module as soon as you are fully registered with ARU, so you can begin to learn about university life before your apprenticeship starts. Through Into ARU, you will explore a virtual land modelled around ARU’s values: courage, innovation, community, integrity, responsibility and ambition. This innovative module is designed as a game, where you collect knowledge and complete various mini tasks. You will proceed at your own pace, though we expect all apprentices to have completed their Into ARU exploration by week 6 of the first trimester. If, for whatever reason, you are unable to complete by that date, you will be supported to complete the module before the end of your first year of study.
In this module, you’ll look at relational databases, how to analyse, design and implement them using industry standard relational database management system (RDBMS). We’ll explore when and when it’s not appropriate to use RDBMS systems and any potential issues. There’ll be a focus on modelling techniques, such as Entity Relationship Diagramming and Normalisation. You’ll learn to construct queries using Structured Query Language (SQL) and practice applying these queries to extract information from relational databases. You’ll develop a simple RDBMS using standard techniques such as requirement gathering, Entity & Attribute identification and concept modelling. You’ll put in place and test your model, developing a range of queries to retrieve, update, insert and delete data. By the end of the module, you’ll have experience of modelling, implementing and testing a data-driven system, and a solid foundation in relational database design that you can apply in academic and professional settings.
You’ll develop the study skills to maximise your learning potential that will help you in both academic and workplace settings. Your workplace will be a key learning environment where you can gain core skills and learn from the individual formative support available. The module looks at three connected strands: · ‘Learning about work’ develops your knowledge and understanding of the workplace. · ‘Learning through work’ looks at how you can gain practical skills in a real working environment. · ‘Learning for work’ develops the relevant skills for the workplace. For example, strategic decision making, understanding quality control and issues, mapping business needs and delivering effective technology solutions. You’ll reflect on your own work practice, learning to critically appraise your own performance and report on what you’ve learnt in the workplace. You’ll demonstrate your ability to gather information, research and communicate your findings, showing your awareness of relevant ethical issues and how it effects your role and studies.
This module supports you in preparing for the End Point Assessment (EPA) of your apprenticeship. You’ll build a portfolio of evidence that shows how you’ve applied your learning in the workplace and how your skills, knowledge and professional behaviours meet the requirements of the apprenticeship standard. You'll develop your portfolio gradually throughout your apprenticeship. It will help you track your progress and achievements over time and show ongoing competency in your role. You’ll also keep an accurate record of your off-the-job (OTJ) learning. This includes all planned learning activities that take place during working hours and supports your development as an apprentice. Across your programme and up to Gateway, this learning must make up at least 20% of your contracted working hours. Regular tripartite review meetings form an important part of your support. These meetings bring together you, your employer and an ARU representative to review your progress, discuss targets and agree next steps. They provide a structured opportunity to reflect on your development, ensure you’re on track, and make the most of your apprenticeship experience.
This module builds a strong foundation in mathematical techniques and analysis, helping you develop the confidence to apply them in practical settings. You’ll strengthen your core mathematical knowledge and learn how to use it to solve basic scientific and engineering problems. You’ll explore key areas including: · calculus (differentiation and integration), · probability and statistics, · linear programming, · matrices and determinants, and how they’re used to solve simultaneous equations. Statistical analysis and probability are introduced to support evaluation, insight and informed decision-making. A strong emphasis is placed on application throughout the module. You’ll work with real-life case studies and use your own data wherever possible, applying mathematical modelling techniques to situations you may encounter in a professional context. By the end of the module, you’ll be able to interpret, manipulate and simplify mathematical information with confidence, and use these skills to tackle real-world enterprise problems effectively.
This module will give you an understanding of the mathematics, statistics and analysis you’ll require to understand and interpret data effectively. You’ll learn about numerical data types and basic statistical concepts including mean, mode, standard deviation and variance. You’ll analyse datasets using techniques including regression, correlation and principal component analysis (PCA), and learn how to visualise data clearly using appropriate charts and graphical methods. You’ll also look at data distributions: · exploring different data distributions, · learning how to assess which models best fit a dataset, · understanding how to fit models to these distributions to produce p-values, · and carrying out hypothesis testing to support evidence-based conclusions. You’ll look at probability theory and how it’s used to generate models. You’ll explore and discuss various approaches to data classification from simply clustering to introductory machine learning and other approaches such as deep-learning. This will provide deeper understanding of large and complex datasets. The module is delivered by a mixture of classroom-based lectures and practical sessions, and you’ll have access to a virtual learning environment to access notes, exercises, and discussions.