Anglia Ruskin University
Anglia Ruskin University

MSC Applied Data Science Conversion

Anglia Ruskin University, United Kingdom

Course Overview

The MSc in Applied Data Science Conversion is tailored for individuals with diverse academic backgrounds, including but not limited to business, social sciences, natural sciences, or engineering. The program is structured to provide a solid foundation in data science principles and techniques while building upon the analytical and quantitative skills of students from various disciplines.

Course Type
PG
Course Nature
Full Time
Course Duration
1 Year
Total Fee
£15000
Intake
Language Proficiency

  • DEGREE
  • IELTS

Documents Required
  • 10TH
  • 12TH
  • DEGREE
  • DEGREE PROVISSIONAL CERTIFICATE
  • Degree Consolidated Marksheet
  • Degree Individual Marksheet
  • PASSPORT
  • LOR 1
  • LOR 2
  • MOI
  • CV
  • SOP
  • EXPERIANCE CERTIFICATES
University
Anglia Ruskin University
University Details

ARU is ranked as one of the finest UK institutions for overseas students, offering 108 undergraduate degrees and about 120 graduate courses. To be admitted to undergraduate programmes in India, students must have 55% from CBSE or 60% from other boards, as well as an IELTS score of 6.0 or 6.5 for UG and PG, respectively.

Syllabus
  1. Foundational Data Science Concepts:

    • The program covers fundamental concepts in data science, including statistics, machine learning, data visualization, and data management. Students gain a comprehensive understanding of the entire data science workflow.
  2. Programming Skills:

    • As data science heavily relies on programming, the program typically includes coursework in programming languages such as Python or R. Students learn how to manipulate data, implement algorithms, and develop data-driven applications.
  3. Data Analysis and Interpretation:

    • Emphasis is placed on practical skills in data analysis and interpretation. Students learn to extract meaningful insights from large and complex datasets, using statistical methods and machine learning algorithms.
  4. Real-world Projects:

    • Many programs incorporate real-world projects or case studies, allowing students to apply their knowledge to practical scenarios. This hands-on experience is valuable in preparing students for the challenges they may face in professional data science roles.
  5. Data Ethics and Governance:

    • Given the importance of ethical considerations in data science, the program may include modules on data ethics, privacy, and governance. Students explore the responsible use of data and the legal and ethical implications of data-driven decision-making.
  6. Industry Collaboration:

    • Collaboration with industry partners, guest lectures from data science professionals, or internship opportunities may be included to provide students with exposure to real-world applications of data science and potential career pathways.
  7. Career Transition Support:

    • Programs often offer career support services, including resume building, interview preparation, and networking opportunities. This helps students transition smoothly into data science roles upon graduation.
  8. Diversity of Applications:

    • The curriculum may cover a diverse range of applications of data science, including business analytics, finance, healthcare, marketing, and more. This ensures that graduates are well-prepared for various industry sectors.

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