Overview
The demand for professionals who can manage, analyze, and apply data to improve business operations is higher than ever. Our MSBA provides students with fundamental skills, appropriate business knowledge, communication skills and industry EXPERIENCE to advance your career. Upon completing the program, students will be able to demonstrate a depth of knowledge of quantitative and analytical tools for decision making; apply data analytics in a business environment with confidence and competence; communicate effectively and persuasively.

For those students which wish to focus their study on a specific sector, the course has been tailored so that you can specialise in either Finance or Operations Management through the choice of Special Topics which you would undertake. The option to undertake the programme with an industrial placement offers you the opportunity to gain work experience in the UK or overseas. The option of completing an alternative Individual Research Report is for students interested in pursuing a PhD programme in the future.

MSBA is available in two study modes: this programme is available as four year Integrated Programme and two year Specialised Master Programme studied from anywhere in the world alongside your career.  The online learning platform used by students in the MSBA program is the Spikey X learning management system. It serves as a hub for faculty and students and is the portal through which students access both asynchronous and synchronous coursework. 

Asynchronous learning activities include viewing videos and slide presentations,  reading textbooks and articles,  taking quizzes. Students complete coursework on their own schedule, but must adhere to assignment deadlines.  Synchronous learning activities include weekly live sessions, collaborative exercises and group discussions.  Students are expected to review course material prior to the session and come prepared to engage fully in all activities. Additionally, faculty members and teaching assistants hold virtual office hours every week.

The programs culminate with a Capstone project course that is carried out in an actual business analytics context, e.g., analytical marketing, operations, finance or human resources analytics. Students are asked to manage a large data set, develop appropriate quantitative models and analytical insights, interact with the company, and deliver midterm and final presentations to company executives and faculty.
Learning Outcomes
Upon completing the program, students will be able to:

!. Demonstrate a depth of knowledge of quantitative and analytical tools for decision making.

2. Make appropriate judgements regarding managing, manipulating and analyzing large data sets.

3. Develop and/or efficiently apply computer software to implement analytical techniques.

4. Identify the potential and challenges of applying data analytics in a business environment.

5.Communicate effectively and persuasively.

Entry requirements
All applicants are considered on an individual basis and those without an honours degree may also be considered on the basis of work experience, professional qualifications and the relevance of the programme to their current professional role.

MSBA candidates must submit these materials:
1. Completed application form.
2. Current resume.
3. Two professional letters of recommendation.
4. Required essay.
5. Interview.

We prefer that both letters come from individuals who know the applicant in a professional capacity, but we will accept one academic recommendation combined with one professional recommendation.

After candidates submit their completed application, we invite all qualified applicants to interview. Interviews take place via Web conferencing software and last about 30 minute. The qualified applicants receive a link, enabling them to schedule this interview.

Course structure
Students entering the program will have varying degrees of exposure to probability and statistics and programming concepts. Therefore, the first two courses in the program are considered “level-setting” courses to ensure everyone has a common foundation of knowledge to build upon in the rest of the curriculum. To graduate, students must complete 108 units of MSBA courses.

Compulsory courses currently include:
Introduction to Probability and Statistics (15 credits)
Modern Data Management (15 credits)
Visualisation (15 credits)
Business Communication (15 credits)
Business Value Through Analytics (15 credits)
Special Topic A (15 units)
Special Topic B (15 units)
The Capstone (15 credits)

The Special Topics currently include:
Analytical Decision Making (30 credits)
Operations Management (30 credits)
Quantitative portfolios and asset management (30 credits)

Each course contains 36 contact hours and about 114 hours private study and, therefore, each student can not take more than 3 courses at the same time: 1 course requires about 10 hours of study per week. 
The main methods of assessments are in-course exam A (20%). in-course exam B(20%), the final exam (40%) and the coursework (20%). We require all students to answer correctly at least 60% of questions.

Introduction to Probability and Statistics
Broad Course Objectives
1. Learn the language and core concepts of probability theory.
2. Understand basic principles of statistical inference (both Bayesian and frequentist).
3. Build a starter statistical toolbox with appreciation for both the utility and limitations of these techniques.
4. Use software and simulation to do statistics (R).
5. Become an informed consumer of statistical information.
6. Prepare for further coursework or on-the-job study.
Specific Learning Objectives
Probability
Students completing the course will be able to:
1. Use basic counting techniques (multiplication rule, combinations, permutations) to compute probability and odds.
2. Use R to run basic simulations of probabilistic scenarios.
3. Compute conditional probabilities directly and using Bayes' theorem, and check for independence of events.
4. Set up and work with discrete random variables. In particular, understand the Bernoulli, binomial, geometric and Poisson distributions.
5. Work with continuous randam variables. In particular, know the properties of uniform, normal and exponential distributions.
6. Know what expectation and variance mean and be able to compute them.
7. Understand the law of large numbers and the central limit theorem.
8. Compute the covariance and correlation between jointly distributed variables.
9. Use available resources (the internet or books) to learn about and use other distributions as they arise.
Statistics
Students completing the course will be able to:
1. Create and interpret scatter plots and histograms.
2. Understand the difference between probability and likelihood functions, and find the maximum likelihood estimate for a model parameter.
3. Do Bayesian updating with discrete priors to compute posterior distributions and posterior odds.
4. Do Bayesian updating with continuous priors.
5. Construct estimates and predictions using the posterior distribution.
6. Find credible intervals for parameter estimates.
7. Use null hypothesis significance testing (NHST) to test the significance of results, and understand and compute the p-value for these tests.
8. Use specicific significance tests including, z-test t-test (one and two sample), chi-squared test.
9. Find confidence intervals for parameter estimates.
10. Use bootstrapping to estimate confidence intervals.
11. Compute and interpret simple linear regression between two variables.
12. Set up a least squares fit of data to a model.

Modern Data Management

Broad Course Objectives

1. Work on complex issues associated with big data analytics and business value creation.
2. Scrutinize different types of data for solving complex business problems and produce reports to support business planning.
3. Systematically, critically, and creatively present findings to both technical and non-technical managers and executives.
4. Use computer tools to solve complex practical problems of direct relevance to contemporary business operations and management.

Specific Learning Objectives

1. Display conceptual understanding of big data analytics.
- Critically evaluate and apply big data techniques using software.
- Develop a systematic understanding in order to build and apply skills in big data network analytics, text mining, and social media data mining.
- Demonstrate critical awareness of how managers and executives utilise big data analytics for business value creation by improving their operational, social, and financial performance and create opportunities for new business development.
- Demonstrate a systematic understanding of database management concepts and their connections with big data analytics.

Visualisation

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