| Program start date | Application deadline |
| 2023-10-10 | - |
| 2024-02-01 | - |
| 2024-10-01 | - |
Program Overview
Liverpool Hope University's Data Science MSc program equips students with the skills to navigate the growing field of data science. Through a blend of theoretical and practical training, graduates gain expertise in data analytics, numerical methods, programming, and applied computer science. The program emphasizes project-based learning and research, preparing students for careers as data scientists, analysts, and other data-driven professionals.
Program Outline
Degree Overview:
Focus:
This program focuses on equipping students with the necessary skills to navigate the growing field of data science, which involves using computer science and statistical methods to glean insights from large datasets. This is achieved through a blend of theoretical and practical training, utilizing cutting-edge technology and research expertise.
Objectives:
Graduates will gain expertise in:
- Data Analytics: Processing, analyzing, and interpreting large datasets.
- Numerical Methods: Applying mathematical techniques to solve real-world problems.
- Theoretical Computer Science: Understanding the fundamental principles of computation.
- Programming: Proficiency in programming languages for data analysis.
- Applied Computer Science: Utilizing computing technologies to address practical challenges.
Core Values:
- The program promotes enthusiasm for computer science through active, project-based learning.
- Students are encouraged to explore specific areas of interest and specialize in their chosen topics.
- The curriculum integrates research and innovation through the final dissertation project, allowing students to contribute to the advancement of knowledge in the field.
Outline:
Core Modules:
- Data Analytics
- Numerical Methods
- Theoretical Computer Science
- Programming
- Applied Computer Science
Optional Modules:
- Big Data & Cloud Computing
- Artificial Intelligence
- Internet of Things
- Mobile Computing
- High Performance Computing
Dissertation:
Individual research project supervised by a specialist tutor, focusing on a chosen area within computer science.
Project Emphasis:
The curriculum emphasizes project-based learning, providing students with opportunities to apply their acquired knowledge and skills to real-world problems.
Dissertation Focus:
The dissertation allows students to delve into a specific area of research, contribute to the field, and develop research and critical thinking skills.
Assessment:
Predominantly coursework-based assessment:
- Projects
- Assignments
- Presentations
- Exams (potentially)
Dissertation assessment:
- Evaluation of the research process
- Quality of research and analysis
- Presentation of findings and conclusions
Grading system:
- Final mark reflects performance across all assessed components
- Pass/fail grades may be used in specific modules
Teaching:
Delivery method:
Lectures, seminars, workshops, and individual project supervision.
Teaching staff:
- Enthusiastic and qualified team with expertise in various areas of computer science.
- Strong research background, recognized in the recent Research Excellence Framework Exercise.
Learning environment:
- Interactive and student-centered.
- Encourages active participation and discussion.
- Utilizes cutting-edge technologies and resources.
Research Emphasis:
- Integrates research into the curriculum.
- Encourages students to participate in ongoing research projects.
- Provides opportunities for collaboration with staff on research publications.
Unique Approach:
- Focus on the blend of computer science and statistical methods for data analysis.
- Emphasizes project-based learning and active participation.
- Encourages students to pursue their individual research interests.
- Offers access to state-of-the-art facilities and technology.
Careers:
Growing demand:
- Increasing demand for Data Scientists across various industries.
- Anticipated skills shortage in the coming years.
Potential career paths:
- Data Scientist
- Data Analyst
- Statistician
- Machine Learning Engineer
Other:
Course duration:
12 months (full-time)
