Masters in Business Analytics

What to expect from a Masters in Business Analytics 

A master’s in business analytics will provide you with a foundation necessary for the development of both the specialist and the so-called ‘soft skills’ often required in business careers.

Using the latest analytics tools to analyze and construe data for future business improvements and decisions, you’ll acquire a deep insight and knowledge of analytical and predictive modeling skills, as well as communication, research skills and management and leadership. 

An MSc in business analytics will usually take a year or two to complete, and will combine both theoretical and practical elements of learning. You’ll be provided with the opportunity to apply various modeling techniques to help real-life business organizations improve their performance in a modern and fast-transforming world, and can expect a variety of teaching methods, including lectures and seminars, tutorials, presentations, project work – as well as a combination of assessment methods, such as formal examinations and coursework, and a dissertation at the end of your course. 

Entry requirements

Generally, to be accepted to study most master’s degrees in business and analytics, you’ll need to have completed an undergraduate degree – with a high degree classification – from a recognized university or the international equivalent in a numerical discipline such as mathematics, statistics, or computer science. In the UK, this means achieving an Upper Class or Second Class Honors undergraduate degree (equivalent to A or A-/B+ in the US), though some universities may make exceptions for applicants who are able to provide an impressive application, complete with a strong personal statement and evidence of work experience in the chosen field. Many well-recognized universities may require both notable academic qualifications and previous professional experience.   

Overall, the main focus of a master’s degree in business analytics is to equip aspiring business analysts with the right up-to-date tools of business analysis, and to provide them with the correct knowledge and skills needed for a role in this industry. There are, however, several different specializations for those wishing to gain a deeper insight into specific areas of business analytics. Among some of the available course modules within this area are:

Marketing analytics

The aim of marketing analytics is to introduce the role of data analytics in learning about – and marketing to – individual customers. It provides a focus on the essence of marketing-customer relations, and offers a better understanding of the customer lifecycle as well as profitability, in addition to presenting analytical and statistical modeling of customer data.

Mathematical foundations

Foundational mathematics are essentially the backbone of the new analytics behind the ever-changing areas of business. A course in mathematical foundations for business analytics will start off by rejuvenating students’ knowledge of key areas in mathematics, such as algebra, statistics and calculus, before progressing to economics with variations of statistical inference and modeling, and concluding with an introduction to the mathematical foundations for various approaches to machine learning.

Programming for business analytics

In learning about the various technological tools needed in business analytics, students in this course will also gain a deeper insight into the methods required to implement computational models and ideas for this field of study. Areas of focus in this course include computational thinking, experimental methodology, preparing datasets and practical means for training, validation and testing models.  The practical side of this specialization will allow business analysts to partake in lab sessions, where they’ll be introduced to programming in Python, Matlab, R and Strata, as well as a basic overview of applying algorithms.

Operations analytics

Operations analytics is an evolving area of business analytics that provides a greater insight into business processes, events and operations. It explores in detail the methods by which data analytics can really help improve and develop important areas of business, while building on the skills and knowledge needed to bring practical, cutting-edge business analytics projects to the table.

Once you’ve completed your MSc in Business Analytics, you should be more equipped for either further postgraduate studies in your field, or to kick-start your career in either of the specializations we’ve mentioned. Some of the major business careers available to graduates include:

Business analyst (data)

Business analysts who focus on data typically hold copious amounts of knowledge in relation to the technical modules associated with manipulating and managing data. Their career is centered around using data and information gathered by a data scientist, to draw conclusions about the overall performance of certain businesses and develop strategies to solve business-related problems and help make improvements.

To become a business analyst, graduates holding an undergraduate degree in a relevant discipline (business, accounting, information systems, human resources, etc) may begin by applying for entry-level jobs in the sector to help them gain a more practical understanding of their role.

Business analyst (consultant/manager)

Becoming a business analyst will require you to have a strong hold of technical tools, data gathering and processing, and groundwork methods of data analytics, as well as a good command of team management and leadership skills, strategic thinking, and strong communication skills. You’ll need to have completed a bachelor’s degree in subjects such as business, finance, marketing, management, or a similar field, and gain plenty of experience working at entry-level before pursuing a master’s degree to further optimize your chances of future employment.

In some parts of the world, such as in the US, certification for management consultancy – such as that of Certified Management Consultant (CMC) – may be offered to qualifying jobseekers in the field, usually through recognized associations and organizations, like the Institute of Management Consultants USA. Candidates for this certification are usually required to hold at least three years of work experience as a full-time consultant, a degree from a four-year college, and must complete comprehensive written and oral examinations. 

Data scientist (analysis)

A data scientist focused on analysis is responsible for creating complex data models and simulations to help improve their understanding of their business and customers, and in turn, generate appropriate decisions in relation to the business as a whole.

Since a career as a data scientist requires a high level of expertise and in-depth knowledge – alongside a range of other skills – becoming one usually involves a strong educational background, with aspiring data scientists expected to at least hold a master’s degree in an appropriate field of study – though a vast proportion of current data scientists also have PhDs.

Data scientist (technology)

Unlike a data scientist focused on analysis, one focused on technology leads a job that’s typically less absorbed in numbers and statistics, working largely with high-performance computing, machine learning, databases, coding, and parallel processing.

As is the case with data scientists in analysis, data scientists in technology must be well-educated, as they’re typically expected to have completed a postgraduate degree (either a master’s or a PhD) in the field of data science, mathematics, astrophysics, or any other related field of study. Both data scientists in analysis and technology must also demonstrate several technical and non-technical skills in order to succeed in this sector.   

Quantitative analyst/modeler

As a quantitative analyst or modeler, your job will be to work closely with grasping the various concepts of data models and produce a strategy to support business decisions. To secure a role as a quantitative analyst/modeler, you must have a very strong academic background in any relevant subject in fields such as computer science, science and engineering, mathematics, etc. Since you’ll be working with mathematical models, you’ll learn to generate concrete and impartial results that won’t be affected by the factors often associated with human reasoning.