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Students
Tuition Fee
Start Date
Medium of studying
Blended
Duration
48 months
Program Facts
Program Details
Degree
Bachelors
Major
Data Science
Area of study
Information and Communication Technologies
Education type
Blended
Timing
Full time
Course Language
English
About Program

Program Overview


The Data Science with Industrial Placement Year program at the University of Sussex combines theoretical foundations in data handling, analysis, and statistics with practical experience gained through an optional placement year. Students develop advanced skills in programming, machine learning, and data visualization, while also learning to think critically and solve real-world data-intensive problems. The program prepares graduates for roles as data scientists, data analysts, and other data-centric professionals.

Program Outline


Data Science (with an industrial placement year) BSc (Hons) - University of Sussex


Degree Overview:

This 4-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 proficiency in handling data and using computational and statistical methods.
  • Develop critical knowledge in programming and statistics, as well as analytical and modelling skills.
  • Become proficient in industry-standard software such as Python and R.
  • Work with researchers on a final-year project.

Outline:


Year 1:

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

Year 2:

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

Placement Year:

  • Students can apply for an optional placement year in industry.
  • The Careers team provides support in finding employers, drafting applications, and preparing for interviews.
  • If a placement is not secured, students will transfer to a non-placement version of the course.

Year 3:

  • 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 (L6)
  • Teaching: Lectures, small-group workshops, computer laboratory sessions, one-to-one sessions.
  • Assessment: End-of-term examinations, coursework (problem sheets, online quizzes, programming exercises), written dissertation and presentation.
  • Contact Hours and Workload: Approximately 1,200 hours (230 hours contact time, 970 hours independent study).

Assessment:

  • Assessment methods typically include end-of-term examinations and coursework such as problem sheets, online quizzes, and programming exercises.
  • Year 3 includes a written dissertation and presentation.

Teaching:

  • Teaching methods typically include lectures and small-group workshops.
  • Programming-based modules incorporate sessions in computer laboratories.
  • Students are guided by an academic advisor throughout their degree.

Careers:

  • Graduates are prepared for roles such as:
  • Data Analyst
  • Data Engineer
  • Business Data Analyst
  • Database Administrator
  • Data Scientist
  • Software Engineer
  • The program also prepares students for further study, such as a Masters degree.

Other:

  • The University of Sussex is home to Sussex Artificial Intelligence, one of its Centres of Excellence, and the Data-Intensive Science Centre (DISCUS).
  • Students have opportunities to engage with potential employers.
  • The Careers and Entrepreneurship team can help students find part-time work while studying.
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