100% online MSc Computer Science with Data Analytics
This 100% online MSc Computer Science programme is aimed at working professionals and graduates from other disciplines who are seeking to move into a career in computer science. You will develop skills in computational thinking and an academic understanding of data and text analysis techniques and their application to real world problems.
This programme is designed to develop your theoretical and foundational understanding of Data Analysis. As a Masters level student you will read and learn about programming techniques, computer and network infrastructure and protocols, security risks and data and text analysis techniques. You will explore a range of data and text analysis techniques such as classification, clustering and regression, text preparation and data cleaning, and managing data privacy.
Practical components and assessments are designed to enable you to further explore these concepts from a theory lead perspective and develop a better understanding of their application in real world challenges and scenarios.
The programme’s eight-week modules provide an introduction to and experience of computational thinking and problem solving across software, hardware, artificial intelligence, and with a specialism in Data Analysis. The research methods and research proposal modules develop your critical evaluation, academic research and writing skills providing a sound basis for your individual research project.
Research project development
The modules research proposal and independent research project should focus on ideas and
areas of interest within the scope of Data Analysis
Every course at York is built on a distinctive set of learning outcomes. These will give you a clear understanding of what you will be able to accomplish at the end of the course and help you explain what you can offer employers. Our academics identify the knowledge, skills, and experiences you’ll need upon graduation and then design the course to get you there.
Apply computational thinking to big data problems, using skills in analysis, design and implementation of computing systems, drawing on the foundations of data analytics and computer science and the current research literature.
Evaluation and Synthesis
Analyse a big data problem from a written description, derive requirements and specifications from an understanding of problems, and create and/or justify designs to satisfy given requirements, applying knowledge of machine learning, data analytics, data mining and system analysis and design.
Software and hardware
Build computer-based systems of varying levels of complexity to serve the needs of users, making effective use of the variety of physical implementations on which that software may be running, and applying the theory and
practice of programming and software engineering.
Critically evaluate and effectively apply data mining tools and algorithms for use to address a complex problem including big data, underpinned by a knowledge of how those systems work
Scope, plan, manage and execute an individual research project of significant size in data analytics, demonstrating critical engagement with the discipline.
Communicate complex computational problems and their solutions in written format to technical and non-technical professional colleagues, in a clear and organised manner and using compelling and convincing arguments drawn from relevant evidence.
Operate as responsible Computer Science professionals, by maintaining awareness of key legal and ethical issues and risk management.
You will be taught by a dedicated team of academic tutors with expertise in online delivery as well as a range of topics including cyber security, machine learning, networks, and software engineering. Our approach to teaching will provide you with the knowledge, opportunities, and support you need to grow and succeed in a global workplace.
The programme consists of 150 credits of taught modules and a 30 credit research project. Each 15-credit module is taught and assessed over eight weeks and represents 150 hours of work. All taught modules are asynchronous and structured for independent learning to fit around the other demands on your time. The research project consists of a real-world application of your choice and takes place over 16 weeks (300 hours’ work). You’ll study one module at a time and the modules run consecutively. Over the year there are two short breaks, in August and December.
You’ll begin with a solid grounding in computational thinking and the theoretical underpinnings of computer science, and apply them through basic programming techniques in the introductory module Algorithms and Data Structures. You’ll then move through the other taught modules, which are delivered in rotation. You’ll be assigned a Student Success Coordinator who will provide you with your schedule of modules and will also contact you prior to the start of each module to explain what happens next.
All modules are delivered through the Canvas learning management system. Within each module, you will participate in weekly activities which could include reading chapters from academic textbooks and journal articles, watching short educational videos, including micro- lectures, and taking part in discussion boards. You’ll study alongside a diverse cohort, students resident in different countries and bringing different backgrounds and experiences.
Assessment and feedback
To pass each module you will be required to submit at least one piece of summative assessment. You may also be expected to undertake formative assessments, which won’t contribute to your final mark but will help you develop the skills and understanding needed to do well in your summative work. Summative assessments will include individual written reports, academic essays, programming assignments and open book timed exams. All assessments take place online and to strict deadlines. You will receive written feedback for each piece of
assessed work to support your development and learning.
Evidenced based technical report
Design and develop a solution to a problem, and provide a written report (structure often provided) discussing your decision process, highlighting benefits and limitations of the techniques or approach used. Drawing on the academic literature and examples from the development (sample code, design work, research) to support discussion.
Open book examinations
Timed examinations with a 48 hour window for completion with access to course notes and completed exercise. These are comprised of, multiple choice questions, short and long answer questions, producing diagrams that are uploaded to the examination point. These examinations require preparation, organising notes, and references sources and require application of understanding to unfamiliar scenarios and problems.
Given specific case studies or references present a discussion and concluding argument to a set of questions, or statements. Requiring the research, synthesis, and evaluation of the concepts presented in the reference material, presented as a discussion supporting distinct or required points and conclusions.
Students of the MSc Computer Science with Data Analytics may also benefit from the following scholarship opportunities.
Office for Students Artificial Intelligence and Data Analytics scholarships
The Office for Students (OfS) scholarship will support more than 60 UK students from non-computing backgrounds looking to move into computer science careers with funding of up to £10,000. Scholarship holders will also receive a 10 per cent discount on their tuition fees.
These scholarships are designed to improve access and diversity in data science and are available to: women, Black, care-experienced, estranged, Traveller, Gypsy or Roma students, refugees and those from lower socio-economic backgrounds, or military backgrounds.
If English isn’t your first language you may need to provide evidence of your ability, such as:
You will not need to provide evidence of your English language abilities:
Algorithms and Data Structures (15 credits)
This module provides techniques for using algorithms and associated data structures. It also covers computational thinking and the theoretical underpinnings and practical applications of computer science, covering: programming; control structures; methods; inheritance; arrays and mechanics of running and testing; and complexity and implementation of algorithms in programs.
Big Data Analytics (15 credits)
This module provides data science skills in data analytics, including the preparation of data, data handling, formulating precise questions and using tools from statistics and data mining to address them.
Data Mining and Text Analysis (15 credits)
This module covers the concepts that underpin data mining and the algorithms and tools commonly used. It explores text processing, where linguistic theory, algorithms and techniques for computer-assisted text processing will be provided. You will have the opportunity to apply the tools and algorithms for data mining and text processing to a variety of data sets.
Advanced Programming (15 credits)
This module details advanced programming concepts such as file manipulation, event-driven programming, multi-threaded programming, programming for data analysis and the use of packages and documentation. It also covers the social context of computing: social impact of computers and the internet; professionalism; codes of ethics and responsible conduct; copyrights, intellectual property; and software piracy.
Computer Architecture and Operating Systems (15 credits)
The module covers the concepts of modern computer architecture and system software. After an overview of computer architecture, it then delves into how computer systems execute programs, store information, and communicate. You will also learn the principles, design and implementation of system software such as operating systems.
Artificial Intelligence and Machine Learning (15 credits)
This module explores the field of artificial intelligence along with the principal ideas and techniques in three core topic areas: problem solving, knowledge representation and machine learning. The implications of AI for business and society are also covered.
Computer and Mobile Networks (15 credits)
A sound understanding of internet architecture, protocols and technologies and their real-world applications forms the core of this module. Discussions around networks and the internet, network architecture, communication protocols and their design principles, wireless and mobile networks, network security issues and networking standards feature. The module also covers related social, privacy and copyright issues.
Software Engineering (15 credits)
This module focuses on designing and building software systems. You will look at principles and patterns of software design, where to apply them, and how they inform design choices. Learn techniques for ensuring systems you build behave correctly. We demonstrate how the application of these principles makes it possible to evolve systems effectively and rigorously.
Research Methods (15 credits)
This module provides you with a range of approaches to research and individual research projects. Formulate research questions appropriate to an area of interest, and evaluate the relationship between question, methodology and method.
Research Proposal (15 credits)
This is an extended research proposal for your final Individual Research Project. The module is created to ensure you are prepared for the IRP in sufficient depth before undertaking final studies. Designed to give you the flexibility of developing a proposal, it explores a work-based problem or one that is driven by your own findings.
Individual Research Project (30 credits)
The 30-credit Individual Research Project (IRP) builds on your Research Project Proposal, defining and developing a plan for research within a particular field of your choice. The IRP is the implementation and write-up of these results. A self-study module, you’ll draw on skills acquired throughout the degree, including self-management, deadlines and subject knowledge.