Data Science BSc (Hons)

Program Overview

The Data Science BSc (Hons) at the University of Sussex is a 3-year program designed for students seeking proficiency in data handling, analysis, and computational methods. The program emphasizes the relationship between data science and statistics, equipping students with a comprehensive understanding of theories and practical skills in industry-standard software like Python and R. Through hands-on projects, students gain valuable experience in addressing real-world data-intensive challenges. The program prepares graduates for successful careers in various data-driven fields, including data science, software engineering, and business analytics.

Program Outline

Degree Overview:

This 3-year full-time program is designed for students interested in becoming proficient in data handling, analysis, and using computational and statistical methods to solve real-world data-intensive problems. The program emphasizes the relationship between modern data science and statistics, providing a thorough grounding in theories and techniques.

Objectives:

• Gain a comprehensive understanding of data science theories and techniques.
• Develop critical knowledge in programming and statistics, along with analytical and modeling skills.
• Master industry-standard software such as Python and R.
• Gain practical experience through working with researchers on a final-year project.

Outline:

Year 1:

• Focus: Introduction to the fundamentals of mathematics and programming.
• Teaching Methods: Lectures, small-group workshops, computer laboratory sessions.
• Assessment: End-of-term examinations, coursework (problem sheets, online quizzes, programming exercises).
• Modules:
• Contact Hours and Workload: Approximately 1,200 hours, including 300 hours of contact time and 900 hours of independent study.

Year 2:

• Focus: Consolidation of programming skills, introduction to statistics.
• Teaching Methods: Lectures, small-group workshops, computer laboratory sessions.
• Assessment: End-of-term examinations, coursework (problem sheets, online quizzes, programming exercises).
• Modules:
• Core Modules:
• Autumn:
Databases, Introduction to Probability, Program Analysis, Scientific Computing
• Spring:
Applied Machine Learning, Probability and Statistics, Software Engineering
• Optional Modules:
• Spring:
Numerical Analysis, Professional and Managerial Skills
• Contact Hours and Workload: Approximately 1,200 hours, including 300 hours of contact time and 900 hours of independent study.
• Optional Opportunities:

Year 3:

• Focus: Advanced study of statistics and data science, specialization in areas of interest, individual research project.
• Teaching Methods: Lectures, small-group workshops, computer laboratory sessions, one-to-one guidance.
• Assessment: End-of-term examinations, coursework (problem sheets, online quizzes, programming exercises), written dissertation, presentation.
• Modules:
• Core Modules:
• Autumn:
Linear Statistical Models (L6), The Data Science Process, Dissertation (BSc Data Science &/w IPY)
• Spring:
Neural Networks, Wider Topics in Data Science (L6)
• Optional Modules:
• Autumn:
Advanced Numerical Analysis (L.6), Comparative Programming, Computational Imaging Methods, E-Business and E-Commerce Systems, Introduction to Computer Security, Probability Models (L6)
• Spring:
Limits of Computation, Machine Learning and Statistics for Health (L6), Monte Carlo Simulations (L6), Random processes (L.6), Statistical Inference (L.6)
• Contact Hours and Workload: Approximately 1,200 hours, including 230 hours of contact time and 970 hours of independent study.

Assessment:

• Methods: End-of-term examinations, coursework (problem sheets, online quizzes, programming exercises), written dissertation, presentation.
• Criteria: Varies depending on the module and assessment type.

Teaching:

• Methods: Lectures, small-group workshops, computer laboratory sessions, one-to-one guidance.
• Faculty: Experts in mathematics, computer science, and data science, with research experience in areas like machine learning, natural language processing, and artificial intelligence.
• Unique Approaches: Emphasis on practical application, industry-standard software, and research collaboration.

Careers:

• Potential Career Paths: Data analyst, data engineer, business data analyst, database administrator, data scientist, software engineer.
• Opportunities: The program prepares graduates for a wide range of data-driven roles in various industries.
• Part-time Work: The Careers and Entrepreneurship team assists students in finding part-time work while studying.
• Further Study: The program provides a strong foundation for pursuing a Masters degree.