Data Science and Computational Intelligence MSc

Program Overview

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Coventry University's MSc Data Science & Computational Intelligence program equips graduates with advanced data analysis techniques and computational intelligence applications. The program focuses on machine learning, neural networks, and statistical methods, preparing students for careers in data science, machine learning engineering, and business intelligence. Graduates are well-prepared to contribute to organizations in various sectors, including finance, marketing, and research.

Program Outline

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Degree Overview:

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Objectives:

• Equip graduates with advanced knowledge and skills in cutting-edge machine learning techniques for analyzing big data sets.
• Develop expertise in assessing the statistical significance of data mining results.
• Foster proficiency in applying advanced data analytics techniques for complex data exploration.
• Prepare graduates for professional roles in a variety of sectors, including finance, marketing, pharmaceutics, and more.

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Program Description:

This program focuses on applications of data science methods and tools combined with computational intelligence techniques. Students will gain the following skills and knowledge:

• Data analysis: Analyze, interpret, and visualize complex data using various tools and methods.
• Machine learning: Implement and apply cutting-edge machine learning techniques for various applications.
• Neural networks: Understand and utilize the power of artificial neural networks for complex problem-solving.
• Computational intelligence: Solve real-world problems using computational intelligence techniques.
• Statistical methods: Apply statistical methods effectively for data analysis.
• Programming: Utilize programming languages and tools like Python and R to analyze data.

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Outline:

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Modules:

• Machine Learning: Introduces fundamental concepts of machine learning and practical applications.
• Data Management Systems: Explores theoretical and practical aspects of data management in centralized and distributed environments.
• Introduction to Statistical Methods for Data Science: Covers core principles of probability theory and statistics widely used in data science.
• Natural Language Processing: Focuses on automated methods for processing text data and exploring NLP applications in various fields.
• Big Data Analytics and Data Visualization: Introduces techniques for managing and visualizing big data, enabling identification of patterns and relationships.
• Artificial Neural Networks: Delves into the concepts and applications of artificial neural networks for solving real-world problems.
• Modelling and Optimisation Under Uncertainty: Provides advanced concepts in machine learning, focusing on Gaussian processes, Latent Dirichlet allocation, and probabilistic graphical models.
• Individual Research Project Preparation: Guides students in identifying research topics, supervisors, and preparing for the individual research project.
• Computing Individual Research Project: Students independently conduct research under the supervision of a dedicated tutor, applying their knowledge and skills to a practical problem or research topic.
The project culminates in a comprehensive report and presentation, demonstrating the acquired skills and competencies.

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Teaching:

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Teaching Methods:

• Lectures
• Seminars
• Tutorials
• Presentations
• Group projects
• Practical laboratory sessions

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Teaching Arrangements:

• Full-time or part-time study options available.
• Part-time teaching arrangements vary and are tailored for each course based on applicant numbers.
• Average of 12 contact hours per week during taught semesters.
• Significant student-directed study required (approximately 35 hours per week).
• Project-based semester involves supervisor-supported self-directed study (around 45 hours per week) and supervisor meetings.
• Teaching delivered through a combination of face-to-face classes, online technologies, and individual/group tutorials.
• University actively seeks to integrate emerging technologies into the curriculum for an innovative and engaging learning experience.

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Faculty:

• Experienced and dedicated faculty members supervise and guide the learning process.
• Students benefit from the expertise and knowledge of researchers actively involved in research in various areas relevant to the program.

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Assessment:

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Assessment Methods:

• Coursework
• Essays
• Group work
• Formal examinations

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Assessment Criteria:

• Each module will have specific assessment criteria aligned with its learning outcomes.
• Criteria typically assess students' understanding of key concepts, analytical skills, problem-solving abilities, and communication skills.

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Careers:

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Career Opportunities:

• Graduates of this program are equipped to pursue diverse careers in various sectors.
• Potential career paths include:
• Data scientist
• Data analyst
• Data engineer
• Machine learning engineer
• Business intelligence analyst
• Marketing analyst
• Research scientist
• Consultant

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Graduate Employability:

• Graduates are well-prepared to contribute to a range of organizations, including financial services firms, tech companies, retail businesses, marketing agencies, research institutions, and government agencies.

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Other:

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Additional Information:

• Students can opt for an optional work placement (up to 12 months) during the part-time pathway, extending the program length to 24 months.
• Placement opportunities may incur additional fees and depend on availability, visa requirements, and competitive application process.
• Students have the opportunity to participate in field trips to international destinations (cost-dependent).
• International experience opportunities are available (details vary per year).

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Tuition Fees and Payment Information:

• UK, Ireland , Channel Islands or Isle of Man: £11,200 | £4,000 (Work placement option additional fee)
• £20,050 | £4,000 (Work placement option additional fee) per year without EU support bursary
• International: £20,050 | £4,000 (Work placement option additional fee)