BSc (Hons) Data Science and Business Analytics
Programme Overview
- Degree: Bachelor of Science (Honours) in Data Science and Business Analytics (Awarded by University of London)
- Programme duration: 3 years (Full-time)
- Location: BUV Campus, Ecopark, Hanoi
- Timetable: Student’s timetable will be sent to BUV student’s emails prior to the commencement date of each semester
Degree awarded by one of the most prestigious universities in the UK
Benefits of LSE’s academic expertise and the backing of the University of London
Diverse career opportunities with equipped skills in data analysis, machine learning, and business acumen
Extra entry option available through 1-year International Foundation Programme (IFP)
- Quality of learning: The academic rigour and relevance of the BSc Data Science and Business Analytics degree is augmented with expertise from the London School of Economics (LSE) which is ranked 4th amongst UK universities.
- Employability and career progression: Graduates with a BSc in Data Science and Business Analytics are highly employable in today’s data-driven job market. Their skills in data analysis, machine learning, and business acumen make them sought after by companies looking to leverage data for strategic decision-making. The demand for such professionals continues to grow across industries. Alternatively, an undergraduate degree from the LSE provides a perfect platform for further studies, whether an MSc or PhD, at the LSE itself or at leading universities worldwide.
- Developed by expert academics: The London School of Economics (LSE) stands as a beacon of academic excellence, renowned globally for its rigorous intellectual environment and groundbreaking research. Established in 1895, LSE has consistently upheld a commitment to excellence in social sciences. Its faculty comprises leading scholars and experts, fostering a vibrant academic community. LSE’s emphasis on critical thinking and innovative research methodologies has produced influential studies that shape public policy and discourse. The institution’s diverse student body adds a rich dimension to academic discussions. LSE’s commitment to cutting-edge research, world-class faculty, and a tradition of producing influential graduates solidify its position as a premier institution for academic excellence.
- Stimulating content: The BSc in Data Science and Business Analytics offers a dynamic curriculum, merging statistical analysis and programming skills with business acumen. Students delve into data mining, machine learning, and predictive modeling, gaining expertise in transforming raw data into actionable insights. Courses cover database management, data visualization, and decision support systems, preparing graduates to extract meaningful information from vast datasets. Additionally, business-focused modules enhance strategic thinking, fostering a holistic understanding of how data drives informed decision-making in various industries. Practical projects and real-world applications further sharpen analytical and problem-solving capabilities.
- A mark of excellence: The University of London was established in 1836 as a pioneering institution that brought together several existing colleges. It was the first university in the United Kingdom to admit students regardless of their religious beliefs or political affiliations. Its international programmes commenced in 1857. Notable alumni include Mahatma Gandhi, Nelson Mandella and seven Nobel laureates. The university has evolved over the years, expanding its constituent colleges (of which the LSE if one) and offering a diverse range of academic programmes. Today, it remains a prestigious and globally recognised centre for higher education and research.
This programme is subject to validation.
Course Structure 
Year 1
Year 1 Introduction to Economics
Mathematical Methods
Business and management in a global context
Statistics 1
Statistics 2
Year 2 
Year 3
This module will introduce you to the fundamentals of economic analysis and reasoning. It is the module upon which subsequent, more specialised economics courses are based.
In the last decade the demand for programming skills related to managing and visualising data has grown remarkably. Python, R and SQL feature consistently in the top skills listed in data science and data analyst jobs. Knowing how to write efficient software code to handle and visualise data is an essential skill for any modern data scientist.
This module studies the management of information systems in organisations. It identifies the main managerial factors affecting the design and implementation of ICTs in organisations.
Programme Outcomes
Learning outcomes
The programme aims to:
• develop competence in students in the application of statistical and machine learning techniques at a high level.
• provide students with a sound knowledge of the principles underlying standard applications of probability and statistics.
• provide practical skills of applied data science and business analytics to inform commercial and policy decisions.
• develop competence in students in the application of statistical and machine learning techniques at a high level.
• provide students with a sound knowledge of the principles underlying standard applications of probability and statistics.
• provide practical skills of applied data science and business analytics to inform commercial and policy decisions.
Knowledge and understanding
A student will be able to:
• formulate and build statistical and algorithmic models in a logical manner.
• understand, formulate and use quantitative models arising in social science, business and other contexts.
• understand probability theory and the concept of uncertainty.
• demonstrate a knowledge and understanding of prediction, classification, regression and clustering.
• show a good knowledge and understanding in advanced areas of statistics.
• learn and use a variety of statistical software packages and the programming language R and Python to conduct data analysis.
• apply advanced quantitative techniques to the analysis of large and complex data.
• formulate and build statistical and algorithmic models in a logical manner.
• understand, formulate and use quantitative models arising in social science, business and other contexts.
• understand probability theory and the concept of uncertainty.
• demonstrate a knowledge and understanding of prediction, classification, regression and clustering.
• show a good knowledge and understanding in advanced areas of statistics.
• learn and use a variety of statistical software packages and the programming language R and Python to conduct data analysis.
• apply advanced quantitative techniques to the analysis of large and complex data.
Intellectual and cognitive skills
A student will be able to:
• locate, extract, analyse and draw reasoned conclusions from multiple sources (including electronic sources) of appropriate literature and relevant data and acknowledge and reference these sources appropriately.
• critically analyse data and text using quantitative and qualitative techniques.
• assess the relative merits of quantitative methodologies and use them appropriately for the problem in hand.
• locate, extract, analyse and draw reasoned conclusions from multiple sources (including electronic sources) of appropriate literature and relevant data and acknowledge and reference these sources appropriately.
• critically analyse data and text using quantitative and qualitative techniques.
• assess the relative merits of quantitative methodologies and use them appropriately for the problem in hand.
Transferable skills
A student will be able to:
• communicate effectively in a written context;
• use information technology (spreadsheets, word-processing and online databases) appropriately.
• organise information, and to assimilate and evaluate competing arguments and methods.
• manage their own learning, including working effectively to deadlines.
• be open minded and have a capacity to handle ideas and scrutinise information in critical, evaluative and analytical ways.
• communicate effectively in a written context;
• use information technology (spreadsheets, word-processing and online databases) appropriately.
• organise information, and to assimilate and evaluate competing arguments and methods.
• manage their own learning, including working effectively to deadlines.
• be open minded and have a capacity to handle ideas and scrutinise information in critical, evaluative and analytical ways.
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Employability
Graduates from the BSc Data Science and Business Analytics programme can work as many different roles:
- Data Scientist
- Business Analyst
- Data Engineer
- Machine Learning Engineer
- Financial Analyst
- Market Research Analyst
- Operations Analyst
- Risk Analyst
- Supply Chain Analyst
- Business Intelligence Consultant
Admission Requirements
Academic Requirements
- Age 17+ by the registration deadline of your enrolment session.
- Academic Requirement: one of the following
- Vietnamese high school diploma and pass the IFP
- 3 UK GCE Advanced level passes in non overlapping subjects where grade EED or above is achieved
- 2 UK GCE A level passes at grade A – E in non overlapping subjects and 3 UK GCSE / GCE O level passes at grade A – C / 9 – 4 in non overlapping subjects
- International Baccalaureate (the Diploma) and Mathematics / Mathematical Studies passed at Higher Level grade 4 – 7 / Standard Level grade 5 – 7
- UK A-Level Mathematics: Grade A – E or equivalent.
English Requirements
One of the following proficiency tests within the last 3 years
- (IELTS) International English Language Testing System: 6 overall, with 5.5 in each sub-test (Academic or General or IELTS Indicator Online Test)
- Pearson Test of English, Academic: 54 overall, with 54 in Reading and Writing elements
- (TOEFL) iBT Test of English as a Foreign Language (including Special Home Edition test): 87 overall, with 21+ in Reading and Writing sub-tests and 19+ in Speaking and Listening sub-tests
- International General Certificate of Secondary Education (IGCSE): English as a Second Language (grade C)
- Cambridge Certificate of Advanced English.
Fees and financial aid
Fees
Tuition fee information for students enrolling from January 2025:
| Tuition Fee | |
| International Foundation Programme | VND 232,885,000 |
| Degree* | VND 1,251,545,000 |
| Total Tuition Fee | VND 1,484,430,000 |
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* Listed fee covers the full programme
Tuition Fee & Fee Policy
Scholarships and Financial aid
The British University Vietnam (BUV) scholarship fund was established in 2018 with the goal of giving students the opportunity to study UK undergraduate and graduate programmes in an international learning environment. With an aim to improve and increase accessibility to high-quality education, BUV has officially launched the fund valued at 150 billion VND in 2024 with hundreds of scholarships and financial aid made available.
You can visit our Scholarship page to find out more and check your eligibility.
