MSci (Hons) Data Science (with a year in industry)

Program Overview

The MSc (Hons) Data Science program at the University of York equips graduates with a solid foundation in data science principles and practices, combining mathematical foundations, computational thinking, and engineering skills. Through a year-long industry placement, students gain practical experience and apply their knowledge in real-world settings. The program emphasizes the societal implications of data science and prepares graduates for careers in various industries, including software, finance, and research.

Program Outline

Degree Overview:

This program aims to produce graduates who are equipped to:

• Contribute significantly to their chosen career field.

• Understand the implications of their work for themselves and society.

• Combine mathematical foundations, computational thinking, and engineering skills.

• Develop a solid foundation in the principles and practices of data science, including:
• Relevant computer science components (e.g., coding, machine learning)
• Mathematical areas (e.g., probability, statistics, calculus)
• Gain knowledge of theoretical aspects of data science:
• Linear algebra
• Algorithmics
• Master real-world implementations and societal impacts of data science.

• Develop technical skills to analyze, mine, and manage data to discover patterns.

• Draw actionable conclusions from data assessments.

• участвовать эффективно в многопрофильных группах.

• Complete a focused research project in data science.

Outline:

Year 1:

• Core modules:
• Software 1: Foundations of Programming for Computer Science
• Introduction to Probability and Statistics
• Foundations and Calculus
• Software 2: Object Oriented Data Structures and Algorithms
• DATA: Introduction to Data Science
• Multivariable Calculus and Matrices
• Additional mandatory modules: Academic Integrity

Year 2:

• Core modules:
• Systems and Devices 2: Operating Systems, Security, and Networking
• Engineering 1: Software and Systems Engineering
• Probability and Markov Chains
• Intelligent Systems: Machine Learning and Optimisation
• Linear Algebra
• Statistical Inference and Linear Models

Year 3:

• Industrial Placement: Students participate in industry placements to gain work experience and apply their acquired knowledge.
Students are supported by the dedicated Industrial Placements Team.
• Elective modules (optional): Students can replace one option module with an elective module in a complementary subject, language, or interdisciplinary topic.

Year 4:

• Core modules:
• Computer Science project or Mathematics project
• Option modules:
• Governance of Data Science
• Cloud-Based Data Analysis
• Computer Vision and Graphics
• Qualitative Approaches to Investigating UX
• Autonomous Robotic Systems Engineering
• Engineering 2: Automated Software Engineering
• Interaction Design and Evaluation
• Statistical Pattern Recognition
• Generalised Linear Models
• Decision Theory and Bayesian Statistics
• Mathematical Finance in Discrete Time
• Intelligent Systems: Probabilistic and Deep Learning
• AI Problem Solving with Search and Logic
• Quantum Computation
• Operations Research
• Numerical Analysis
• Multivariate Data Analysis
• Time Series
• Mathematical Finance in Continuous Time
• Cryptography Theory and Practice or Cryptography
• Elective modules (optional): Students can replace one option module with an elective module in a complementary subject, language, or interdisciplinary topic.

Year 5:

• Core modules:
• Advanced Project: Computer Science or Extended Independent Project: Mathematics
• Option modules:
• Cryptography Theory and Practice
• High-Integrity Systems Engineering
• Qualitative Approaches to Investigating UX
• Computer Vision and Graphics
• Autonomous Robotic Systems Engineering
• Interaction Design and Evaluation
• Mathematical Methods of Finance
• Mathematical Finance in Discrete Time
• Statistical Pattern Recognition
• Generalised Linear Models
• Decision Theory and Bayesian Statistics
• Governance of Data Science
• Cloud-Based Data Analysis
• Intelligent Systems: Probabilistic and Deep Learning
• Engineering 2: Automated Software Engineering
• AI Problem Solving with Search and Logic
• Quantum Computation
• Statistics for Finance and Insurance
• Multivariate Data Analysis
• Computational Finance with Python

Assessment:

• Various assessment techniques are used to provide students with diverse practice opportunities.
• Examples of assessment techniques include:
• Report writing
• Presentations
• Live demos
• Timed programming assessments
• Closed exams
• Timely feedback is provided on assessments.

Teaching:

• Students are taught by active researchers and experts in their field who are passionate about their subjects.
• The program utilizes lectures, lab sessions, programming classes, and tutorials.
• Individual supervisors guide students throughout their studies.
• Students are expected to engage in independent learning outside of scheduled classes.
• As students progress, they become more independent learners and work on individual research projects.
• The Department of Computer Science boasts cutting-edge facilities.
• The Department of Mathematics offers a community atmosphere with a focus on small groups.
• Teaching locations are distributed across campus on both the east and west sides.

Careers:

• Graduates are equipped with skills highly sought after in the digital economy.
• Career paths often include:
• Software and electronics industries
• Financial Services
• Further study (e.g., PhD)
• Other industries are also accessible due to the program's emphasis on numeracy and analytical skills.
• Examples of companies employing graduates include:
• Mars Inc
• Cancer Research UK
• Amazon
• BAE Systems
• Morgan Stanley
• G Research
• Thales
• Civil Service
• M&G Investments
• Ubisoft
• Rapita Systems
• Sky
• BT
• Raspberry Pi
• IBM
• JP Morgan
• Hut Group
• Automaton Games
• Career opportunities may include:
• Computer programmer
• Software engineer
• Software developer
• Business analyst
• Research scientist
• Network manager
• IT Systems manager
• The program equips graduates with:
• Analytical skills
• Research skills
• Management skills
• Communication skills
• Flexibility to adapt to various fields

Other:

• The program's objectives are to:
• Provide multi-skilled, highly competent graduates.
• Ensure graduates understand the implications of their work.
• Develop a combination of mathematical foundations, computational thinking, and engineering skills.
• The program is designed to provide students with clear and ambitious learning outcomes.
• Learning outcomes detail the abilities students will possess upon completion of the program.
• Each course is designed to develop modules that guide students towards these learning outcomes.
• Students are able to explain their acquired knowledge and skills to potential employers.

Summary:

The MSc (Hons) Data Science (with a year in industry) program at the University of York provides a comprehensive and in-depth education in data science, preparing graduates for successful careers in the field.

Tuition Fees and Payment Information:

UK (home) fees: £9,250 per year International and EU fees: £28,800 per year