Higher Diploma in Data Science and Analytics

Course overview

Qualification Bachelor's Degree
Study mode Full-time, Part-time, Online
Duration 16 months
Intakes January, March, May, July, September, November
Tuition (Local students) $ 9,073
Tuition (Foreign students) $ 11,305


The Higher Diploma in Data Science and Analytics has been developed by the London School of Business and Finance, School of  Advanced Technology and Digital Media to provide a qualification for students who are seeking to work in the data science, business analytics industry, or in occupations where big data management will be of utility.

Throughout the course, students are fully supported, and their development is checked frequently by progress assessments. Student performance and satisfaction are monitored to ensure that the course meets students’ academic and personal development needs, and industry contacts ensure that the programme is relevant and suitable for the demands of a career in the industry.

The main aim of the Higher Diploma in Data Science and Analytics is to give students knowledge and practical insight into the workings of the data science and business analytics industry. Students will get an insight into how big data is managed and useful information extracted to better make informed decisions. Holders of the Higher Diploma will be able to demonstrate detailed knowledge, practical applied experience, and critical understanding of the major concepts in big data management.

The aim of the Higher Diploma in Data Science and Analytics is to:

  • Develop students’ competence and practical skills in big data management.
  • Provide the foundation for future pathways and continuing professional development.
  • Provide the relevant knowledge and understanding of big data management as it relates to the wider business context.
  • Provide the technical, communication, and practical work skills that will enable graduates to follow a career in all areas of data science and analytics and a wide range of careers in IT, Retail, business, finance, marketing, logistics, and administration.





$ 9,073
Local students
$ 11,305
Foreign students

Estimated cost as reported by the Institution.


$ 64
Local students
$ 240
Foreign students

Student Visa

$ 66
Foreign students

Every effort has been made to ensure that information contained in this website is correct. Changes to any aspects of the programmes may be made from time to time due to unforeseeable circumstances beyond our control and the Institution and EasyUni reserve the right to make amendments to any information contained in this website without prior notice. The Institution and EasyUni accept no liability for any loss or damage arising from any use or misuse of or reliance on any information contained in this website.

Entry Requirements

a. Minimum Academic Entry Requirement

Local students shall possess one of the following:

  • Two passes in GCE ‘A’ Level Examinations
  • International Baccalaureate (24 points)
  • Local Polytechnic Diploma in any field

International Students shall possess one of the following:

  • Completion of Year 12 High School Qualification or equivalent qualification from respective home countries
  • Completed International Baccalaureate (24 points)
  • Equivalent Local Polytechnic Diploma in any field in respective home countries

b. Minimum English Language Entry Requirement

Both local and international students MUST fulfil the minimum English language entry requirement of one of the following (except Mandarin programmes):

  • Achieved grade C6 or better in the English language in GCE O level.
  • Passed in the English Language in Year 10 High School qualification or equivalent.
  • IELTS 5.5/TOEFL 550.
  • Completed LSBF Preparatory Course in English Upper Intermediate Level.

Students with non-standard entry requirements will be assessed on a case-by-case basis subject to the approval of the Academic Board.

c. Minimum Age

18 years or above


Level 1:

  • Statistical Analytics
  • Data Analytics
  • Machine Learning
  • Cloud Computing
  • App Development
  • Internet of Things
  • Project Management
  • Data Visualisation

Level 2:

  • Database Management Systems
  • Python Data Science Essentials
  • Cloud Artificial Intelligence (AI)
  • Deep Learning with R programming
  • Hadoop Big Data Analytics
  • Web Analytics
  • Data Science Visualization (Tableau)
    • +Practicum/Research Project

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