BSc (Hons) Graduate Apprenticeship AI and Data Science
| Program start date | Application deadline |
| 2024-09-01 | - |
Program Overview
The BSc (Hons) Graduate Apprenticeship AI and Data Science program equips working professionals with the skills to solve real-world problems using data science and artificial intelligence. Through a blend of academic study and practical work experience, students develop technical abilities, creative thinking, and ethical awareness, preparing them for careers in various data-driven industries. The program is fully funded and emphasizes the responsible use of data science and AI.
Program Outline
BSc (Hons) Graduate Apprenticeship AI and Data Science
Degree Overview:
Overview:
The BSc (Hons) Graduate Apprenticeship AI and Data Science program equips students with the knowledge and skills to develop careers using societally beneficial data science and artificial intelligence solutions to solve real-world problems. The program is informed by both academic research and industry feedback and covers managing, exploring, analyzing, understanding, and visualizing image, text, or numerical data.
- To equip students with technical skills such as software development and machine learning.
- To prepare graduates for careers utilizing AI and data science to address real-world challenges.
Program Description:
The program combines academic study with practical work experience, allowing students to immediately apply their knowledge to real-world situations while gaining professional experience. Students attend an initial two-day induction followed by one day per week of academic instruction. They also complete work-based projects and receive mentorship from industry experts.
Outline:
Program Content:
- Data science and artificial intelligence
- Software development and machine learning
- Creative thinking and problem-solving
- Ethics and data protection
Program Structure:
- Four-year program (potentially shorter with advanced entry)
- Blended learning format: one day of academic instruction per week, initial two-day induction, and work-based projects
- Trimesters A, B, and C with specific attendance requirements for each trimester
- Work-based projects in each year of study
Course Schedule:
- Year 1: Mathematics for Data Science, Programming 0, Probability and Statistics, Database Development, Fundamentals of Software Development, Work Based Project 1
- Year 2: Programming 1, Introduction to Data Science, Programming 2, Software Process and Practice, Human Computer Interaction, Work based Project 2
- Year 3: Data Protection and Ethics, Dev Ops, Data Visualisation, Research Skill and Professional Issues, Programming 3, Work Based Project 3
- Year 4: Big Data and IoT, Advanced Data Science, Cloud Platform Development, Machine Learning, Honours Project
Individual Modules:
Year 1:
- Mathematics for Data Science: Introduces mathematical foundations for data science
- Programming 0: Introduces fundamental programming concepts
- Probability and Statistics: Covers probability theory, statistical methods, and data analysis
- Database Development: Introduces database design, implementation, and management
- Fundamentals of Software Development: Covers software development principles and methodologies
- Work Based Project 1: Applies learned skills to a real-world industry project
Year 2:
- Programming 1: Develops advanced programming skills
- Introduction to Data Science: Introduces data science concepts, tools, and techniques
- Programming 2: Further develops programming skills
- Software Process and Practice: Covers software development processes and methodologies
- Human Computer Interaction: Explores human-computer interaction principles and design
- Work based Project 2: Applies learned skills to a real-world industry project
Year 3:
- Data Protection and Ethics: Covers data protection regulations and ethical considerations in data science
- Dev Ops: Introduces DevOps principles and practices
- Data Visualisation: Covers data visualization techniques and tools
- Research Skill and Professional Issues: Develops research skills and explores professional issues in data science
- Programming 3: Further develops advanced programming skills
- Work Based Project 3: Applies learned skills to a real-world industry project
Year 4:
- Big Data and IoT: Introduces Big Data concepts and technologies, including Internet of Things (IoT)
- Advanced Data Science: Covers advanced data science concepts and algorithms
- Cloud Platform Development: Introduces cloud computing platforms and development tools
- Machine Learning: Covers machine learning algorithms, techniques, and applications
- Honours Project: Conducts an independent research project under faculty supervision
Assessment:
Assessment Methods:
- A combination of coursework assignments, practical projects, examinations, and work-based projects
- Continuous assessment throughout the program
- Emphasis on applying theoretical knowledge to practical situations
Assessment Criteria:
- Accuracy and completeness
- Understanding and application of concepts
- Critical thinking and problem-solving skills
- Communication and presentation skills
- Teamwork and collaboration skills
- Professionalism and ethics
Teaching:
Teaching Methods:
- Lectures, seminars, tutorials, workshops, and practical sessions
- Collaborative learning activities
- Online learning resources and support
Faculty:
- Experienced and qualified faculty with expertise in data science, artificial intelligence, and related fields
- Active researchers with strong industry connections
Unique Approach:
- Strong focus on practical application and industry relevance
- Work-based projects and mentorship opportunities
- Collaboration with industry partners
- Emphasis on ethical and responsible use of data science and artificial intelligence
Careers:
Career Paths:
- Data Scientist
- Machine Learning Engineer
- Machine Learning Scientist
- Applications Architect
- Data Architect
- Infrastructure Architect
- Data Engineer
- Data Analyst
- Software Developer
- Cloud Engineer
Career Opportunities:
- Healthcare
- Government organizations
- Social media companies
- Financial and education sectors
- Entertainment industry
- Other data-driven industries
Career Outcomes:
- Graduates will be prepared for successful careers using data science and artificial intelligence to solve real-world problems.
- The program is designed to meet the growing demand for data science and AI professionals in various industries.
- Graduates will possess a combination of technical skills, problem-solving abilities, and ethical awareness, making them highly competitive in the job market.
Other:
- The program is designed for working professionals and can be completed while maintaining full-time employment.
- The program is fully funded by the Scottish Funding Council for eligible applicants.
- The program provides opportunities to network with industry professionals and build valuable connections.
- The program emphasizes ethical and responsible use of data science and artificial intelligence.
Conclusion:
The BSc (Hons) Graduate Apprenticeship AI and Data Science is an excellent opportunity for working professionals to gain the skills and knowledge necessary for a successful career in the data science and artificial intelligence field. The program's practical focus, industry connections, and work-based projects prepare graduates to make a significant impact in various sectors.
