Graduate Apprenticeship in Data Science Course with BSc (Hons) Degree

Program Overview

This Graduate Apprenticeship in Data Science provides students with the knowledge and skills to drive innovation and efficiency through data analysis, management, and artificial intelligence. The program combines online learning with work-based experience, tailoring the curriculum to meet the specific needs of the student's employer. Graduates are well-prepared for careers in data science, machine learning, and related fields. The program emphasizes sustainability and aligns with United Nations Sustainability Goals.

Program Outline

Degree Overview:

This Graduate Apprenticeship in Data Science with a BSc (Hons) Degree is a work-based learning program designed to provide students with the knowledge, understanding, and skills necessary to help their organizations gain insights from their data and drive innovation and efficiency. The program aims to equip students with a thorough grounding in data analytics, data management, data engineering, machine learning, and artificial intelligence.

Outline:

The program is structured over four years of full-time study, with a combination of course learning and work-based learning. Modules are undertaken sequentially rather than in parallel.

Year 1:

• Introduction to the data analytics lifecycle including data preparation, exploration, and visualization.
• Principles of statistical and mathematical techniques for data analysis and problem-solving.
• Fundamental programming principles.
• Transferable professional skills.

Year 2:

• Application of data mining techniques.
• Advanced programming including modeling, design, implementation, and testing of systems.
• Database design principles and developing a relational database.
• Security threats against information systems.
• Understanding of ethical and legal issues related to the IT business environment.

Year 3:

• Design, implementation, and evaluation of scalable program solutions using a big data computation framework.
• Use of business intelligence tools to support decision-making.
• Development of data warehousing solutions.
• Application of machine learning and artificial intelligence techniques to solve real-world problems.
• Creative idea generation and entrepreneurial skills to start or grow a business through digital transformation.

Year 4:

• Undertake a substantial professional computing project in data science.
• Principles and practices underlying the retrieval, extraction, and mining of text data, including web data.
• Architecting and developing cloud-based applications.
• Keep abreast of the latest trends in techniques and applications of data science at the forefront of technology.

Assessment:

Students are typically assessed each year through a combination of methods, including:

• Written assignments (essays, reports)
• Portfolios
• Set exercises
• Oral assessments
• Practical skills assessments
• Group critiques
• Project outputs
• Dissertations
Feedback on assessments is aimed to be provided within 20 working days of hand-in.

Teaching:

• The program is delivered through supported online study using the university's Virtual Learning Environment (VLE), CampusMoodle.
• This provides students with the flexibility to study when and where they choose.
• The online platform offers full open access to tutors and other class members, fostering an active learning community.
• On-campus workshops are scheduled throughout the course, providing opportunities for professional networking, sharing insights and experiences, and engaging with staff in a campus setting.
• Webinars are conducted with groups of up to 15 students, lasting up to 30 hours per module.
• Work-based learning is a significant component, with groups of up to 15 students engaging in up to 240 hours of learning per module under the guidance of a Workplace Mentor.
• Online delivery through Blackboard Collaborate sessions for up to 2 hours per week, supplemented by additional enhancement materials.
• Modules are 10 weeks in length and run sequentially for 40 weeks (4 modules per year), departing from the traditional term calendar.
• Staff delivering the course includes lecturers from traditional RGU programs and potential guest lectures by industry experts.

Careers:

• The Graduate Apprenticeship synchronizes theoretical learning with practical experience, tailoring the course to meet the specific needs of the company.
• This program offers benefits to both the employee and the employer, addressing the long-term development needs of both business and student.
• Graduates are well-positioned for careers in various fields, including data analysis, data science, machine learning, artificial intelligence, and other related areas.

Other:

• The program incorporates sustainability as a key topic in the real-world project in Year 2.
• This project challenges students to tackle sustainability issues with entrepreneurial thinking, preparing them for the working environment.
• The course is committed to United Nation's Sustainability Goals 3, 9, and 11.
• Students must be employed full-time in a role related to the course of study, with the employer committing to providing a suitable workplace environment, guidance, and mentoring support.
• The company must be willing to partner with the university through a Collaboration Agreement to facilitate the student's experience and learning outcomes.
• The university offers facilities for the course, including dedicated IT labs, an Apple Mac Teaching Lab, and the CampusMoodle platform.
• Students have access to library, labs, and support services, including academic writing, study skills, maths and statistics, English language, information technology, and library support.

For Academic Year 2024/2025 £1,820 per academic year For Academic Year 2023/2024 £1,820 per academic year