Program start date | Application deadline |
2024-06-24 | - |
Program Overview
This 100% online MSc Computer Science with Data Analytics program from the University of York equips students with skills in computational thinking, data and text analysis techniques, and their application to real-world problems. The program focuses on developing students' theoretical and foundational understanding of data analysis, as well as practical skills in programming, computer and network infrastructure, security risks, and data and text analysis techniques. Graduates are prepared for careers in data science, software engineering, machine learning, artificial intelligence, and other data-driven fields.
Program Outline
Extracted Information from MSc Computer Science with Data Analytics Online Program:
Degree Overview:
Overview:
This 100% online MSc Computer Science with Data Analytics program is designed for working professionals and graduates from other disciplines who want to transition into a career in computer science. The program equips students with skills in computational thinking, data and text analysis techniques, and their application to real-world problems.
Objectives:
- Develop theoretical and foundational understanding of data analysis.
- Gain practical skills in programming, computer and network infrastructure, security risks, data and text analysis techniques.
- Explore various data and text analysis techniques like classification, clustering, regression, data cleaning, text preparation, and data privacy management.
- Apply theoretical concepts to real-world challenges and scenarios through practical components and assessments.
Program Description:
This program focuses on developing students' theoretical and foundational understanding of data analysis. As a master's-level student, you will learn about programming techniques, computer and network infrastructure, security risks, and data and text analysis techniques. You will explore various data and text analysis techniques, including classification, clustering, regression, data cleaning, text preparation, and data privacy management. The program also emphasizes the practical application of these concepts through practical components and assessments.
Outline:
Course Content:
- Algorithms and Data Structures: This module focuses on utilizing algorithms and data structures to solve problems. It covers computational thinking, theoretical underpinnings, practical applications of computer science, programming, control structures, methods, inheritance, arrays, and complexity and implementation of algorithms in programs.
- Big Data Analytics: This module equips students with data science skills in data analytics, including data preparation, data handling, formulating precise questions, and using tools from statistics and data mining to address those questions.
- Data Mining and Text Analysis: This module delves into the concepts of data mining and the algorithms and tools commonly used in this field. It explores text processing, covering linguistic theory, algorithms, and techniques for computer-assisted text processing. Students apply these tools and algorithms to various data sets.
- Advanced Programming: 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, including the social impact of computers and the internet, professionalism, codes of ethics, responsible conduct, copyrights, intellectual property, and software piracy.
- Computer Architecture and Operating Systems: This module provides an understanding of modern computer architecture and system software. It covers an overview of computer architecture, how computer systems execute programs, store information, and communicate. Students also learn the principles, design, and implementation of system software such as operating systems.
- Artificial Intelligence and Machine Learning: This module explores the field of artificial intelligence, covering the principal ideas and techniques in problem-solving, knowledge representation, and machine learning. It also discusses the implications of AI for business and society.
- Computer and Mobile Networks: This module focuses on internet architecture, protocols, technologies, and their real-world applications. It discusses networks and the internet, network architecture, communication protocols, design principles, wireless and mobile networks, network security issues, and networking standards. The module also covers related social, privacy, and copyright issues.
- Software Engineering: This module focuses on designing and building software systems. It teaches students about principles and patterns of software design, their application, and how they inform design choices. They learn techniques for ensuring their systems behave correctly, and how the application of these principles enables effective and rigorous systems evolution.
- Research Methods: This module equips students with various approaches to research, including individual research projects. It helps students formulate research questions appropriate to an area of interest and evaluate the relationship between question, methodology, and method.
- Research Proposal: This module involves developing an extended research proposal for the final Individual Research Project. The module ensures students are prepared for the IRP before undertaking final studies. It provides flexibility in developing a proposal, exploring a work-based or student-driven problem.
- Individual Research Project: The 30-credit Individual Research Project builds on the Research Project Proposal. Students define and develop a research plan within a field of their choice. The IRP involves the implementation and write-up of these results. Students draw on skills acquired throughout the degree, including self-management, deadlines, and subject knowledge.
Course Structure:
- The program 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.
- The research project focuses on a real-world application of the student's choice and takes place over 16 weeks (300 hours' work).
- Students study one module at a time, and the modules run consecutively.
- There are two short breaks during the year, in August and December.
Assessment:
- Individual written reports
- Academic essays
- Programming assignments
- Open book timed exams
- Formative assessments (do not contribute to the final mark but help develop skills and understanding)
- All assessments take place online and to strict deadlines.
- Students receive written feedback for each piece of assessed work to support their development and learning.
Teaching:
- Teaching format:
- 100% online
- Asynchronous (flexible learning around your schedule)
- 15-credit modules taught and assessed over eight weeks
- Faculty:
- Dedicated team of academic tutors with expertise in online delivery, cyber security, machine learning, networks, and software engineering.
- Approach:
- Provides knowledge, opportunities, and support to succeed in a global workplace.
- Modules:
- Delivered through the Canvas learning management system.
- Include weekly activities like reading academic texts, watching educational videos, and participating in discussion boards.
- Cohort-based learning with students from diverse backgrounds and countries.
Careers:
- Potential career paths:
- Data Scientist
- Data Analyst
- Software Engineer
- Machine Learning Engineer
- Artificial Intelligence Engineer
- Network Engineer
- Security Analyst
- Research Scientist
- Career opportunities:
- Work in a variety of industries, including technology, finance, healthcare, government, and research.
- Pursue leadership roles in technical and data-driven fields.
- Career outcomes:
- Graduates are prepared to tackle complex problems, make informed decisions, and thrive in a rapidly evolving technological landscape.
Other:
- Start date: New students begin within a few weeks.
- Total program cost: £9,000 (pay-per-module).
- Ranked 18th in the UK for Computer Science (Complete University Guide 2024).
- 100% online, study from anywhere.
- Member of the Russell Group of major research-intensive universities.
- The University of York is accredited by the UK Quality Assurance Agency for Higher Education.
- Note: For complete and up-to-date information, please refer to the official University of York program website.
Entry Requirements:
Academic Qualifications:
- home and international students:
- A 2:2 (or equivalent) undergraduate degree in any subject.
- OR a Masters degree (or equivalent) in any subject.
- If your degree was earned outside the UK, make sure it is equivalent to a 2:2. You can consult the University's country-specific pages for more information.
English Language Requirements:
- If English is not your first language, you will need to provide evidence of your proficiency. This can be done through:
- International English Language Testing System (IELTS): 6.5 overall, with 6.0 in each component.
- TOEFL IBT (internet-based and special home edition): 87 overall, with 21 in each component.
- Trinity ISE: Level 3 with Merit in each component.
- PTE Academic: 61 overall, with a minimum of 61 in Writing and no less than 55 in all other components.
- Cambridge Certificate in Advanced English (CAE) and Cambridge Proficiency (CPE): 176 overall, with 169 in each component.
- GCSE: B / 6 or above
- Duolingo: 120 overall, with 105 in each component.
- LanguageCert B2 Communicator: High Pass with 33/50 in each component.
- LanguageCert Academic: 70 overall with a minimum of 65 in each component.
- KITE: 459-494 overall, with 426-458 in all other components.
- Skills for English B2: Merit overall, with Pass with Merit in each component.
- Oxford ELLT: 7 overall with a minimum of 6 in each component.
- The test must have been completed within two and a half years of the start date of your programme. You cannot combine scores from more than one test sitting.
Exemptions from English Language Requirements:
You will not need to provide evidence of your English language abilities if:
- You are from a specified majority English-speaking country.
- You have completed a degree in English in a specified country within seven years of your intended start date.
Language Proficiency Requirements:
The University of York requires all applicants who are not native English speakers to demonstrate their English language proficiency. This ensures that students have the necessary skills to succeed in their studies. The specific requirements vary depending on the applicant's chosen program and their country of origin. For the MSc Computer Science with Data Analytics program, the following English language tests are accepted:
- IELTS: 6.5 overall, with 6.0 in each component.
- TOEFL IBT: 87 overall, with 21 in each component.
- Trinity ISE: Level 3 with Merit in each component.
- PTE Academic: 61 overall, with a minimum of 61 in Writing and no less than 55 in all other components.
- Cambridge Certificate in Advanced English (CAE) and Cambridge Proficiency (CPE): 176 overall, with 169 in each component.
- GCSE: B / 6 or above
- Duolingo: 120 overall, with 105 in each component.
- LanguageCert B2 Communicator: High Pass with 33/50 in each component.
- LanguageCert Academic: 70 overall with a minimum of 65 in each component.
- KITE: 459-494 overall, with 426-458 in all other components.
- Skills for English B2: Merit overall, with Pass with Merit in each component.
- Oxford ELLT: 7 overall with a minimum of 6 in each component. The test must have been completed within two and a half years of the start date of your programme. You cannot combine scores from more than one test sitting. If you are from a specified majority English-speaking country or have completed a degree in English in a specified country within seven years of your intended start date, you will not need to provide evidence of your English language abilities.