BSc Acoustics and Music Technology
The BSc Acoustics and Music Technology program at The University of Edinburgh offers a ...
MSc High Performance Computing with Data Science
Central Campus
INTAKE: September
Program Overview
The MSc High Performance Computing with Data Science program at the University of Edinburgh offers students a unique opportunity to combine expertise in high-performance computing (HPC) with skills in data science, providing a comprehensive education in two rapidly growing fields. This interdisciplinary program equips students with the knowledge and tools needed to tackle complex computational problems, analyze large-scale data sets, and extract actionable insights for decision-making and innovation. With a focus on both theoretical foundations and practical applications, students learn to design, develop, and optimize HPC systems and algorithms, as well as apply data science techniques such as machine learning, data mining, and visualization to real-world challenges. Through a blend of coursework, hands-on projects, and research opportunities, students graduate prepared to pursue careers at the forefront of computational science, engineering, and data-driven decision-making.
-
Integration of High-Performance Computing and Data Science: The program integrates concepts and techniques from high-performance computing and data science, providing students with a holistic understanding of computational approaches to data analysis and processing. Students learn to leverage HPC resources and algorithms to address the scalability, efficiency, and complexity challenges of data-intensive applications.
-
Advanced Computing Architectures and Technologies: Students explore advanced computing architectures and technologies, including multi-core processors, graphics processing units (GPUs), parallel computing clusters, and cloud computing infrastructures. They gain insights into the design, optimization, and deployment of HPC systems for data-intensive workloads, ensuring they are equipped to harness the full potential of modern computing platforms.
-
Data Analysis and Machine Learning Techniques: The program covers a range of data analysis and machine learning techniques, including statistical analysis, predictive modeling, clustering, and deep learning. Students learn to apply these techniques to diverse data sets from various domains, including scientific research, business analytics, healthcare, and social media, to extract insights and drive decision-making.
-
Big Data Processing and Management: Students learn strategies and tools for processing, managing, and analyzing large-scale data sets, often referred to as big data. They explore distributed computing frameworks such as Hadoop and Spark, as well as database technologies and data storage solutions optimized for high-performance and scalability, enabling them to handle the volume, velocity, and variety of big data.
-
Practical Applications and Real-World Projects: The program emphasizes practical applications and hands-on projects, allowing students to apply their skills to real-world challenges and scenarios. They may work on interdisciplinary projects with industry partners, research institutions, or governmental organizations, gaining valuable experience and insights into the application of HPC and data science in diverse domains.
Location
Central Campus
Score
IELTS: 7
Tuition Fee
£ 35300
Postgraduate entry requirements:
-
Academic Qualifications: Prospective postgraduate applicants to the University of Edinburgh are typically required to have achieved a minimum academic qualification of approximately 60%, based on their previous academic achievements and qualifications.
-
English Language Proficiency:
-
IELTS (International English Language Testing System): Minimum overall score of 7.0, with at least 6.0 in each component (Listening, Reading, Speaking, Writing).
-
TOEFL (Test of English as a Foreign Language): Minimum score of 100 on the internet-based test (iBT), with at least 20 in each component (Reading, Listening, Speaking, Writing).
-
PTE (Pearson Test of English): Minimum overall score of 70, with at least 59 in each component (Listening, Reading, Speaking, Writing).
-
Students must provide:
- academic marksheets & transcripts
- letters of recommendation
- a personal statement - SOP
- passport
- other supporting documents as required by the university.
Work experience: Some postgraduate courses may require relevant work experience in the field.
It is important to note that meeting the minimum entry requirements does not guarantee admission, as the university considers factors such as availability of places and competition for the program. Additionally, some courses may have higher entry requirements or additional selection criteria, such as interviews or portfolio submissions.
The University of Edinburgh offers a range of scholarships and funding opportunities specifically designed to support international students pursuing undergraduate, postgraduate, and research programs. These scholarships aim to promote diversity, academic excellence, and global engagement.
Global Scholarships: The University of Edinburgh offers Global Scholarships to outstanding international undergraduate students. These scholarships provide financial assistance towards tuition fees and living expenses, enabling talented students from around the world to access quality education at Edinburgh.
Edinburgh Global Research Scholarships: International postgraduate students pursuing research-based programs (Ph.D., MSc by Research) can apply for Edinburgh Global Research Scholarships. These scholarships provide full or partial funding for tuition fees and living expenses during the research program.
School-specific Scholarships: Some academic schools and departments within the university offer scholarships targeting international students in specific disciplines or programs. These scholarships may be merit-based or need-based and vary in terms of eligibility criteria and funding amounts.
Commonwealth Scholarships: The University of Edinburgh participates in various Commonwealth scholarship schemes, providing opportunities for students from Commonwealth countries to study in the UK. These scholarships are funded by the UK government and other organizations.
External Funding Sources: International students are encouraged to explore external funding sources, such as government scholarships, private organizations, and international foundations, to support their studies at the University of Edinburgh.
It is important to note that scholarship availability, criteria, and application deadlines may change from year to year.
Graduates of the MSc High Performance Computing with Data Science program at the University of Edinburgh are highly sought after by employers in various sectors seeking professionals with expertise in both high-performance computing (HPC) and data science. With their interdisciplinary skills and practical experience, graduates are well-equipped to pursue diverse and rewarding career opportunities in fields such as scientific research, engineering, finance, healthcare, technology, and more.
-
Data Scientist/Analyst: Graduates may work as data scientists or analysts, applying their expertise in data science and high-performance computing to analyze large-scale data sets, extract insights, and make data-driven decisions. They may work in industries such as finance, healthcare, e-commerce, and social media, using advanced analytics techniques to solve complex problems and drive innovation.
-
Computational Scientist/Engineer: Graduates may pursue careers as computational scientists or engineers, leveraging their skills in high-performance computing and data science to solve computational problems in scientific research, engineering simulations, and computational modeling. They may collaborate with researchers in fields such as physics, chemistry, biology, climate science, and engineering to develop and optimize computational algorithms and workflows.
-
Machine Learning Engineer/Researcher: Graduates may specialize in machine learning, working as engineers or researchers to develop and implement machine learning models and algorithms for various applications, including predictive analytics, pattern recognition, and natural language processing. They may work in technology companies, research institutions, or startups, pushing the boundaries of AI and machine learning technology.
-
Big Data Engineer/Architect: Graduates may work as big data engineers or architects, designing and implementing scalable data processing and storage solutions for handling large-scale data sets. They may work with distributed computing frameworks such as Hadoop and Spark, as well as cloud-based platforms, to develop data pipelines and analytics infrastructure for organizations dealing with big data.
-
Research Scientist/Engineer in Computational Biology/Healthcare: Graduates may pursue careers in computational biology or healthcare, applying their skills in high-performance computing and data science to analyze biological and healthcare data, conduct genomic research, and develop computational models for drug discovery and personalized medicine. They may work in pharmaceutical companies, research labs, or healthcare institutions, contributing to advancements in medical science and healthcare delivery.
-
Quantitative Analyst/Financial Engineer: Graduates may work in the finance industry as quantitative analysts or financial engineers, using their skills in data science and high-performance computing to develop quantitative models, algorithms, and trading strategies for financial markets. They may work for hedge funds, investment banks, or financial technology companies, leveraging data-driven insights to optimize investment decisions and risk management strategies.
-
Consultant/Technical Specialist: Graduates may work as consultants or technical specialists, providing expertise and guidance on high-performance computing and data science solutions to clients in various industries. They may offer consulting services, training workshops, and technical support to help organizations leverage HPC and data science technologies effectively and achieve their business objectives.
