thesis on business analytics

Business Analytics Dissertation: what to expect and how to make the most of it

• Tuesday, June 18, 2019

Juan Felipe Alvarez

• minute read

My academic year has been filled with numerous activities. Ever since fresher’s week started in September, there have been things going on (both academic and non-academic) and I can assure you that you will have a very intense and gratifying year as an MSc student.

Now that it's June and I have already finished with classes and exams, it is time to focus on the dissertation. I am writing this blog in order to let you know what to expect and my top tips for enjoying the experience.

Let’s start with the basics: what is a dissertation and what are the business school’s expectations? The objective of the dissertation is to provide students with an opportunity to produce an original piece of work and specialize in a particular topic of interest. The typical word count would be between 15,000 and 18,000 words, depending on the topic and needs to be developed between June and September.

The MSc in Business Analytics has a very pragmatical approach to dissertations; they do this, so you can try and solve a real-world problem with real-world data, which I think is exciting and will teach you applied problem-solving skills. You can either propose your own topic or choose to participate in one of the several ongoing research projects that the University has. If you have a very strong interest in one field and have the means to obtain the data, you can propose your own topic before February. However, most students decide to take part in the topics that are proposed by the University, like I did.

By the end of January, we received a list containing more than 60 different projects that were very diverse in terms of research field and industry. Many of them were in collaboration with companies that had proprietary datasets and were interested in analysing them thoroughly, while others dealt with publicly available datasets. In regards of the type of analysis there was plenty to choose from: text mining and natural language processing, machine learning modelling, simulation, operations research, forecasting, optimisation and decision analysis, just to mention a few. One of the ones that really caught my eye was to build a machine learning model to predict the best strategy for goalkeepers during penalty kicks.

We had to select 7 topics then rank them and the programme director would try to assign you the topic that was highest in your priority. My suggestion that will help you make an informed choice is to read very well the project description and understand what it is about. Don’t choose the dissertation topic only because the company has a well-known name. For me, it is more important to choose a type of analysis that you really like and feel comfortable doing. If you already know the supervisor, you could drop him an email asking for more information about the project and about the required skills.

You will be assigned a topic and a supervisor by the beginning of March and if you selected a topic with a company, some meetings might start taking place during the following months. It is very important to talk early to your supervisor and my tip would be to take the initiative and contact them with plenty of time. Your supervisor will help you to structure the research question, build the appropriate methodology and assess the quality of your work. Make sure you establish clear communication from the beginning in order to make your life easier. Another tip I could give you is to be ordered with your meetings and keep a minute for each one, so you can keep track of the tasks you are supposed to be doing.

A little bit about my topic: In case you are interested, I will be working with Europe’s Largest digital healthcare providers and I will be creating a model to optimise their medicine buying strategy. I have already met the company’s representatives and I hope to be able to frame my research question in the following weeks.

In order to close, I would like to add that the dissertation might look a little bit intimidating at the beginning. I just wanted to let you know that as soon as you begin to divide the whole deliverable into smaller sub-parts, you will feel that you are making quick progress. I hope that this article would be useful to level your expectations about this topic and I wish you the best when the time comes for you to start with your dissertation.

I'm Juan Felipe Alvarez, an MSc Business Analytics student from the class of 2019. Follow my blogs for an insight into life as a student at Alliance MBS.

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Master of Business Analytics: Everything You Need to Know

Thomas Nugent - BusinessBecause

Thomas Nugent is the Deputy Editor for BusinessBecause.

Today’s most in-demand jobs revolve around technology, data science, and business analytics. 

By 2025, the World Economic Forum (WEF) predicts that 97 million new jobs will emerge. Jobs with growing demand will include data analysts, AI and machine learning specialists, big data specialists, and software and applications developers. 

When you’re making a career plan , you want to identify a target area that combines your passion and interests with the highest chance of making an impact. 

If you’re a tech enthusiast with management ambitions, a Master of Business Analytics will give you exactly that.

Who is a Master of Business Analytics for?

A Master of Science in Business Analytics gives you the skills to apply modern data science and machine learning capabilities at work to solve real-world business problems. 

The degree is for working professionals or recent graduates who want to launch, or accelerate, their career in a business analytics role. That could be in sectors like digital, retail, healthcare, finance, logistics, or supply chain. 

A Master of Data Analytics is more focused on data science and less on the business side, although the terms are often used interchangeably.

Ultimately, business analysts use the data they analyze to make practical, data-driven decisions and to implement change, whereas data analysts focus mostly on the uncovering of data trends and insights.

Types of Masters of Business Analytics

The traditional MS in Business Analytics degree is an in-person, campus degree that usually lasts between 12 and 14 months. 

Students either go straight into their degree from undergraduate level or take time out of the workforce for the duration of the program.

You can also study Masters in Business Analytics online and, in the United States, many Masters in Business Analytics programs are STEM-designated. 

Master of Business Analytics STEM degree

A STEM-designated degree is a US academic program recognized for its specific, close focus on science, technology, engineering, or math (STEM). 

International students pursuing STEM-designated masters degrees can apply for a 24-month STEM-extension to their 12-month Optional Practice Training (OPT) period, which allows them stay in the US on a student visa after graduating. That means students on STEM-designated masters can work in the US for up to three years after graduation, without an H1-B visa .

Studying a STEM-designated Master of Business Analytics is the perfect way to acquire a top technical skillset that will allow you to accelerate your career. According to the US Department of Labor’s Bureau of Labor Statistics, the STEM field has the fastest growing and highest-paying jobs this century.

Online Master of Business Analytics

Online Master of Business Analytics programs allow you to study for your degree part-time, over several years. The online version of the degree covers the same content as the full-time, on campus version. 

Course content is delivered through each school’s online learning platform, and lectures, seminars, and group project work are all carried out virtually. 

Some of the  best online Master of Business Analytics degrees  allow you up to three years to complete your education. There’s the added bonus that you’ll be able to earn while you learn, and apply the skills you’re acquiring in real time to your job. 

MSc Business Analytics: What you’ll learn

Masters of Business Analytics dive into the world of big data, artificial intelligence, and data analytics. 

On MIT Sloan’s Master of Business Analytics (MBAn) , students study modules like Analytics Edge and Optimization Methods. They also take an action learning seminar on analytics, machine learning, and the digital economy. 

There’s an Ethics & Data Privacy module, and electives offer the choice to study Analytics of Finance, Product Management, and Applied Machine Learning. 

Students also work on real-life data science problems with industry practitioners in the degree’s capstone project. 

The seven-month project sees students work in teams of two around the US and abroad. After working on campus at MIT on the projects, students then travel to their host organization in the summer for a full-time summer internship.

Students study a similar syllabus on Carnegie Mellon’s Tepper School of Business Online Master of Science in Business Analytics. 

They take classes in machine learning, programming in R and Python, and corporate communication. They also study traditional business modules in marketing, operations, accounting, finance, and people analytics. 

Master of Business Analytics salary & jobs prospects

What can you do with a Master in Business Analytics? The types of jobs you can get with an MS in Business Analytics include:

• Data Scientist
• Analytics Associate
• Data Science Consultant
• Operations Analyst
• Product Analyst
• Business Consultant
• Quantitative Analyst

Companies that hire Master of Business Analytics grads include: 

• Boston Consulting Group

There are high employment numbers for business analytics degree grads. One hundred percent of job-seeking grads from MIT’s MBAn landed jobs within six months of graduating. One hundred percent of accepted offers were in data science functions. At Imperial College Business School in London, 94 percent of MSc Business Analytics grads were employed three months after their degree. 

The skills you gain from the degree mean you can enter a wide array of industries. Consulting teams at McKinsey, Bain, and BCG (MBB) now consist of not just management experts, but also leaders in data analysis and big data. The ability to work in technically diverse teams is one of the key things MBB consulting firms look for in their business school hires.

The pandemic-induced demand for healthcare has seen the industry digitize and develop technologically. As companies compete for the gaps in the market this has left, they’ll be looking for employees with the latest data and analytical competencies.  

The same demand is seen in finance, as the big banks battle to adapt and compete with agile fintech firms looking to dominate the industry.

Master of Business Analytics salary

What salary will you earn after your Master of Business Analytics?

Graduate base salaries for business analytics grads vary, depending on the program. Some examples include:

• MIT Sloan’s Master of Business Analytics - US$114K
• IÉSEG School of Management’s MSc in Business Analytics & Consulting - US$68K 
• Warwick Business School 's MSc Business Analytics - US$43K

The strong salaries for business analytics degree grads reflects demand in the market, and the willingness of companies to pay highly skilled, tech-savvy grads who will be able to utilize their skillset to drive business growth in the decades to come.

Master of Business Analytics cost & ROI

The cost of Master of Business Analytics programs range from around US$20,000, up to the higher end of US$84,000 and above. Examples of program cost are:

• Columbia Business School - US$84K
• USC Marshall Business School  - US$70K
• IE Business School - US$45K
• Imperial College Business School  - US$45K
• AGSM @ UNSW - US$38K
• IÉSEG School of Management - US$20K
• University of Bath (Online) - US$20K

There are many ways to pay for business school . Students take out student loans, and some are sponsored by their companies. There are also many scholarship options available. 

The return you get from your investment is that you leave business school ready to launch or accelerate your career at the forefront of technology, where the opportunities and salaries are booming. 

The answer to whether a Master of Business Analytics degree is worth it  comes down to your own personal career goals. 

The degree is well suited to undergraduates fresh out of their degree who want to launch a career in data and analytics, and to professionals who want to accelerate their career in the space. 

A Master of Business Analytics will give you the comprehensive technical skillset you’ll need to tap into the huge opportunities and salary prospects in the analytics arena over the coming years.

Master of Business Analytics admission requirements

Some business analytics masters require you to have a bachelor’s degree in a related topic, such as management science, math, statistics, computer science, engineering, or business and management with an incorporated quantitative element. But that isn’t required for all Master of Business Analytics degrees.

You’ll typically start your an online application form and need to submit the following materials:

• Full degree transcript
• GMAT exam score
• Personal statement
• English language test results (if applicable)
• Letters of recommendation
• Written/video essays

The Ultimate Business School Application Checklist

Use our business school application checklist and timeline to nail the admissions process and access useful mba.com resources right when you need them. 

The admissions team then review your application and, if successful, you’ll be invited for a face-to-face interview.

Interested in a Master of Business Analytics or want to learn more about your degree options? Download our Graduate Business School Application Guide for Undergrads and find your best fit master’s program.

Thomas Nugent  is the Deputy Editor for  BusinessBecause , with responsibilities supporting the Editor with content and SEO strategy, alongside editorial team development.

Thomas is an experienced writer who has written articles covering business education, management, careers, and the future of work.

He launched and edits the BusinessBecause Insights series, which explores the latest research and trends from the business school classroom, drawing on the expertise of world-leading professors to inspire and inform current and future leaders. The Insights series has covered things like the sharing economy, working from home during COVID-19, the future of the office, NFTs, and ESG investing.

Thomas earned an MA in English from the University of Aberdeen before completing an MSc in Journalism at Robert Gordon University. He joined BusinessBecause in October 2017.

Business analytics research

Business analytics research requires a rigorous approach to model formulation and estimation as well as the skills to analyse the outputs of these models. Our Business Analytics scholars regularly publish in leading international journals. Particular fields of interest include:

• big data analytics 
• applied econometrics
• electricity markets
• financial econometrics and quantitative risk forecasting
• Bayesian methods
• forecasting, sensitivity analysis
• micro-econometrics, multivariate statistical methods
• panel data methods and models
• scheduling problems
• statistical machine learning
• stochastic non-life insurance and actuarial problems
• supply chains
• testing and modelling structural change
• time series and forecasting.

We welcome approaches from potential PhD students with an interest in any of these areas.

Meet our academics and research students.

Head of Discipline

Associate Professor  Dmytro Matsypura

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Associate Professor Anastasios Panagiotelis (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor  Daniel Oron

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr  Nam Ho-Nguyen

Dr  Stephen Tierney

Dr  Chao Wang

Dr Wilson Chen

Dr  Bern Conlon

Dr Qin Fang

Dr  Simon Loria

Dr  Pablo Montero-Manso

Dr Bradley Rava

Dr  Marcel Scharth

Dr Firouzeh Taghikhah

Dr Alison Wong

Adjunct Senior Lecturer

Dr  Steven Sommer

Adjunct Lecturer

Research associates, postdoctoral research associate.

Dr  Tomas Ignacio Lagos

Honorary and emeritus staff

Emeritus professor.

Professor Eddie Anderson

Professor Robert Bartels

Honorary Professors

Professor Robert Kohn

Professor Ganna Pogrebna

Professor Michael Smith

Honorary Associates

John Goodhew

Hoda Davarzani

John Watkins

David Grafton

Yakov Zinder

Higher degree by research students

View our current  higher degree by research students . 

Research groups

Time series and forecasting research group, productivity, efficiency and measurement analytics (pema), research seminars.

The Discipline of Business Analytics holds a regular seminar series. Seminars are usually held on Fridays at 11am in Room 5070, Abercrombie Building (H70) . The seminar organiser is Bradley Rava .

Please email  [email protected]  if you wish to be included in the BA seminar series mailing list.

Below is an outline of our recent and upcoming activity. 

2018 seminars

Finding critical links for closeness centrality.

• Date: 10 Aug 2018 at 11am
• Venue: Rm 3010, Abercrombie Building (H70)
• Speaker: Professor Oleg Prokopyev, Department of Industrial Engineering, University of Pittsburg

Risk management with POE, VaR, CVaR and bPOE: Applications in finance

• Venue: Rm 4150, Abercrombie Building (H70)
• Speaker: Professor Stan Uryasev, Department of Industrial and Systems Engineering, University of Florida

My experience as EIC of OMEGA

• Date: 9 Aug 2018 at 11am
• Venue: Rm 2240, Abercrombie Building (H70)
• Speaker: Prof Benjamin Lev, LeBow College of Business, Drexel University

Heterogeneous component MEM models for forecasting trading volumes

• Date: 27 Jul 2018 at 11am
• Venue: Rm 3190, Abercrombie Building (H70)
• Speaker: Professor Giuseppe Storti, Department of Economics and Statistics, University of Salerno UNISA

Realised stochastic volatility models with generalised asymmetry and periodic long memory

• Date: 1 Jun 2018 at 11am
• Venue: Rm 2290, Abercrombie Building (H70)
• Speaker: Professor Manabu Asai, Faculty of Economics, Soka University

Improving hand hygiene process compliance through process monitoring in healthcare

• Date: 24 May 2018 at 11am
• Venue: Rm 1080, Abercrombie Building (H70)
• Speaker: Associate Professor Chung-Li Tseng, Operations Management, UNSW Business School

Exact IP-based approaches for the longest induced path problem

• Date: 18 May 2018 at 11am
• Speaker: Dr Dmytro Matsypura, Discipline of Business Analytics, The University of Sydney

Bayesian deep net GLM and GLMM

• Date: 11 May 2018 at 11am
• Speaker: Mr Nghia Nguyen, Discipline of Business Analytics, The University of Sydney

Computational intelligence-based predictive snalytics: Applications with multi-output support vector regression

• Date: 13 Apr 2018 at 11am
• Speaker: Professor Yukun Bao, School of Management, Huazhong University of Science and Technology (HUST)

Entrywise functions preserving positivity: Connections between analysis, algebra, combinatorics and statistics

• Date: 5 Apr 2018 at 3.30pm
• Venue: Rm 3120, Abercrombie Building (H70)
• Speaker: Associate Professor Apoorva Khare, Department of Mathematics, Indian Institute of Science

Large-scale multivariate modelling of financial asset returns and portfolio optimisation

• Date: 23 Feb 2018 at 11am
• Speaker: Professor Marc Paolella, Department of Banking and Finance, University of Zurich

Statistical inference on the Canadian middle class

• Date: 16 Feb 2018 at 11am
• Speaker: Professor Russell Davidson, Department of Economics, McGill University

2017 seminars

Heterogeneous structural breaks in panel data models.

• Date: 1 Sep 2017 at 11am
• Venue: Rm 1050, Abercrombie Building (H70)
• Speaker: Dr Wendun Wang, Erasmus School of Economics, Erasmus University

Externalities, optimisation and regulation in queues

• Date: 25 Aug 2017 at 11am
• Speaker: Dr Nadja Klein, Melbourne Business School, University of Melbourne

A partial identification subnetwork approach to discrete games in large networks: An application to quantifying peer effects

• Date: 11 Aug 2017 at 11am
• Speaker: Professor Tong Li, Department of Economics, Vanderbilt University

An introduction to knowledge management and some common entry points

• Date: 4 Aug 2017 at 11am
• Venue: Rm 2090, Abercrombie Building (H70)
• Speaker: Prof Eric Tsui, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University

Two applications of serial inventory systems

• Date: 21 Jul 2017 at 11:00am
• Venue: Rm 5070, Abercrombie Building (H70)
• Speaker: Associate Professor Ying Rong, Operations Management, Shanghai Jiao Tong University

Methods of matrix factorisation

• Date: 2 Jun 2017 at 11am
• Speaker: Professor Wray Buntine, Master of Data Science, Monash University

Optimisation and equilibrium problems in engineering

• Date: 26 May 2017 at 11am
• Speaker: Prof Steven Gabriel, Department of Mechanical Engineering, University of Maryland

Exact subsampling MCMC

• Date: 12 May 2017 at 11am
• Speaker: Dr Matias Quiroz, UNSW Business School, University of New South Wales

Effects of taxes and safety net pensions on life-cycle labor supply, savings and human capital: The case of Australia

• Date: 21 Apr 2017 at 11am
• Speaker: Dr Fedor Iskhakov, College of Business and Economics, Australian National University

Trial-offer markets with social influence: The impact of different ranking policies

• Date: 18 Apr 2017 at 11am
• Venue: Rm 5040, Abercrombie Building (H70)
• Speaker: Dr Gerardo Berbeglia, Melbourne Business School, University of Melbourne

Conditionally optimal weights and forward-looking approaches to combining forecasts

• Date: 7 Apr 2017 at 11am
• Speaker: Dr Andrey Vasnev, Discipline of Business Analytics, The University of Sydney

A flexible generalised hyberbolic option pricing model and its special cases

• Date: 31 Mar 2017 at 11am
• Speaker: Dr Simon Kwok, School of Economics, The University of Sydney

Scheduling with variable processing times: Complexity results and approximation algorithms

• Date: 24 Mar 2017 at 11:00am
• Speaker: Associate Professor Daniel Oron, Discipline of Business Analytics, The University of Sydney

Modelling insurance losses using contaminated generalised beta type-2 distribution

• Date: 17 Mar 2017 at 11am
• Speaker: Dr Boris Choy, Discipline of Business Analytics, The University of Sydney

How (not) to get what you ask for: Survey mode effects on self-reported substance use

• Date: 24 Feb 2017 at 11am
• Speaker: Dr Bin Peng, School of Mathematics, University of Technology Sydney

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What Can You Do With an MBA with a Specialization in Business Analytics?

August 30, 2021

Purdue Online

Business analytics is the science of studying, analyzing and transforming data into meaningful conclusions that drive business strategy. Business analytics professionals help companies discover more about customers, identify the need for certain products and services, and learn how to market companies in more effective ways. Using business analytics, a company can improve operations and increase revenue.

Data analytics is increasingly important in business today. According to a 2020 report by ReportLinker , the global big data and business analytics market is projected to grow from $192.24 billion in 2019 to $446.42 billion by 2025. Via channels like e-commerce and digital marketing, businesses have access to more data than ever before.

If you enjoy gathering information and analyzing data to create meaningful change, a career in business analytics may be for you. An MBA with a specialization in business analytics teaches students advanced business principles with an in-depth focus on data analytics.

Mohit Tawarmalani , Krannert School of Management at Purdue University business analytics professor, explains how an online MBA focused on business analytics can equip you with the tools to make sound business decisions. If you’ve wondered why you should earn an MBA with a specialization in business analytics, this guide explains potential career paths you can take.

Why Choose Business Analytics?

Business analytics is one of the most in-demand career areas across diverse business landscapes today. The U.S. Bureau of Labor Statistics (BLS) named data scientist and mathematical science positions among the 20 fastest-growing occupations through 2029, with a 31% job growth rate. 

With an MBA with a specialization in business analytics, a graduate may be able to pursue leadership positions in business. An MBA is an advanced degree that teaches students management and business planning skills, in addition to advanced data skills that a business analytics degree would offer.

“An MBA is often a way to get into more managerial positions and into places where a variety of different skills are required to operate  the marketing, financial aspects and other enterprises,” says Tawarmalani. 

In business analytics programs, students learn how to use data analytics tools, create data analysis reports and make data-driven decisions. They also learn how to apply analytics best practices to business areas like sales, marketing and finance.

What’s Driving Growth in the Analytics Field?

The amount of data that’s being created in the world is rising exponentially. According to a 2021 report by Statista , the volume of data/information worldwide is expected to grow to 181 zettabytes in 2025, up from 64.2 zettabytes in 2020 and 2 zettabytes in 2010.

(How big is a zettabyte? According to Cisco , a zettabyte is 2 to the 70th power bytes, also expressed as 10^21 [1,000,000,000,000,000,000,000 bytes] or 1 sextillion bytes. One zettabyte equals approximately 1,000 exabytes, 1 billion terabytes or 1 trillion gigabytes.)

“Major companies such as Amazon, Tencent, Alibaba and Google all use data to inform their business decisions,” Tawarmalani says. “They are all acquiring data, building new platforms and engaging with companies on the backend to provide customers with the services they seek. For any company that wants to play in this field, it is critical that they have managers who are analytics-savvy. The demand has been by growing leaps and bounds.”

Companies of all sizes can use data to optimize their operations. From free website analytics tools like Google Analytics to more robust data-gathering and analysis systems, there’s a variety of data tools and systems businesses can use to scale their growth.

What Skills Are Needed?

Those who work in business analytics should have a natural affinity for processing data and putting it into actionable insights for the decision-makers in a company. According to the BLS , important qualities for a position such as an operations research analyst are:

• Analytical skills
• Critical thinking skills
• Problem-solving skills

Since data is based on numbers, it’s also important to have a math-driven mindset.

“Business analytics requires the ability to deal with numbers, the ability to deal with software and the ability to deal with communicating these ideas or managing change as it occurs in an organization,” Tawarmalani says.

Business skills like communication, writing and interpersonal skills are also important for analytics professionals. They have to collaborate with and communicate their findings to key stakeholders within a company.

What Careers Are Possible With an MBA With a Specialization in Business Analytics?

A variety of jobs are available for graduates with this type of MBA. According to Tawarmalani, “These positions almost always include the term ‘analyst,’ like analytics manager or credit analyst or operations research analyst. The job descriptions are changing more toward the growing need for data-friendly and analytics-enabled positions.”

An MBA with a specialization in business analytics prepares students for job titles like the following listed by Krannert School of Management at Purdue University :

• Advanced analytics consultant
• Associate manager – customer analytics
• Business analyst
• Business intelligence analyst
• Data analyst
• Compliance analyst
• Data scientist
• Marketing analyst
• People analyst
• Research analyst
• Revenue management analyst
• Senior product strategy analyst

Grow a Business Career With an MBA With a Specialization in Business Analytics

An MBA degree prepares graduates to advance their business careers using leadership skills, expert knowledge and business strategy. An MBA degree program focused on business analytics helps students master data analytics skills to help businesses optimize their operations.

The Krannert MBA with a specialization in business analytics is offered online, so students can study from wherever they are in the world. The program prepares graduates to grow their careers in business analytics and achieve their professional goals. Find out more information today.

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Business Analytics Essay

Nowadays business analytics has established itself as an efficient tool for improving a management performance. A smart approach towards a company’s data interpretation allows to find out the possible drawbacks, predict potential problems, monitor the collective achievements and, most importantly, to perform a more reasonable and grounded decision-making process.

It is evident today that one has to be capable of processing large amounts of information in order to be a successful competitor. Therefore, companies contribute significantly to the modern technologies and professional personnel that constitute a favorable analytical environment (Pearlson & Saunders 2013).

The motives for developing analytical competence can be various. Some firms perform a thorough data analysis in order to work out a unique strategy; others try to renew the already existing concept. Despite the fact that the necessity of applying analytical analysis seems to be unquestioned, statistics has shown that one starts developing this competence when the company experiences some difficulties.

As a rule, managers employ the analysis of big data in order to determine the cause of the current challenge. One should point out that this kind of approach cannot be called farsighted. The companies with a more rational managing policy contribute to business analytics not to solve the problem but to avoid it (Charles & Gherman 2013).

Reasonable management suggests using the data analysis in the interest of making the right decision and predicting its outcomes. The intensive business competition can, likewise, prompt one to engage analytical tools in furtherance of improving the company’s analytical performance. Thus, a thorough analysis of the collected data lets one monitor the clients’ satisfaction rate, as well as follow the rivaling companies’ activity.

Whereas the analysis of big data is an undoubtedly efficient tool, it is also a relatively new concept. Therefore, companies are likely to face a series of challenges before they learn to perform a successful processing of the collected information. The primary problem one is apt to experience is the shortage of high-qualified specialists.

As far as universities do not yet have relevant faculties, identifying a skilled data scientist seems to be rather problematic. Hence, it is up to the managers to work out a proper approach for attracting consistent professionals and evaluating their competence.

Moreover, while the problem of processing high volumes of data is easily solved with the help of modern technologies, its quality analysis seems to be a more challenging task. The company’s principal concern is, thence, to determine the precise aims of the performed analysis. It is an accurate targeting that results in formulating valuable conclusions instead of receiving mere statistics (Charles & Gherman 2013).

In my organization, the employment of big data analysis would have a significant impact on the company’s progress. First of all, as long as modern technologies allow storing the unlimited volumes of information, a high-quality data analysis would spare the company’s time and effort in carrying out the same procedures.

The wise analytical policy would also prevent the firm from taking the wrong measures once they have proved their inefficiency. Finally, sufficient contribution to the development of the analytical competence would turn the organization into a successful competitor perfectly aware of its targeted market and the rival’s performance.

In conclusion, one should point out the importance of integrating business analytics into the corporate policy. It should be realized that the analysis of big data has its specificity and peculiarities that are to be considered.

Finally, it is crucial that the managers do not overuse technologies while examining the input. However smart the mechanism is, the accredited analysis still requires human’s strategic vision and criticism (Pearlson & Saunders 2013).

Reference list

Charles, V & Gherman, T 2013, ‘Achieving Competitive Advantage Through Big Data.Strategic Implications ‘, Middle-East Journal of Scientific Research, vol. 16, no. 8, pp. 1069-1074.

Pearlson, KE & Saunders, CS 2013, Strategic Management of Information Systems , John Wiley & Sons Incorporated, New Jersey.

• Chicago (A-D)
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IvyPanda. (2019, June 24). Business Analytics. https://ivypanda.com/essays/business-analytics/

"Business Analytics." IvyPanda , 24 June 2019, ivypanda.com/essays/business-analytics/.

IvyPanda . (2019) 'Business Analytics'. 24 June.

IvyPanda . 2019. "Business Analytics." June 24, 2019. https://ivypanda.com/essays/business-analytics/.

1. IvyPanda . "Business Analytics." June 24, 2019. https://ivypanda.com/essays/business-analytics/.

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IvyPanda . "Business Analytics." June 24, 2019. https://ivypanda.com/essays/business-analytics/.

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Business Administration, B.S. – Business Analytics

Gain expertise in the data analytics that employers need to effectively use new technologies including big data, decision automation, machine learning, and artificial intelligence.

• 120 Credits »
• Semester Map »
• Tuition & Financial Aid »

Accelerated Master's

• B.S.B.A. to M.B.A. or M.S. in Risk Management »
• New York, NY »
• Long Island, NY »

What You’ll Learn

The Business Administration, B.S. program with a concentration in Business Analytics gives you the in-demand skills and tools you'll use to solve real-world business problems and to increase organizational productivity, efficiency, and revenue. You'll learn from a range of real-world case studies to appreciate how these skills and tools can be leveraged to achieve success in measuring key metrics, forming inferences and predictions from data, and making optimal and robust business decisions.

These courses in this concentration include a wide array of topics, such as data cleaning, data visualization, data mining, database management, web analytics, text mining, and social media analytics. In addition to learning Python programming languages, the students will also learn to work with business analytics tools such as SQL, Tableau, Google Analytics, and more.

What makes our program special?

• AACSB accreditation, earned by only five percent of business schools worldwide.
• A centralized location in New York City, home of significant demand for graduates in this field.
• A strong research-oriented business analytics faculty offering unique insights on analytics.
• New York Tech's strong network of over 100,000 alumni, including leaders in all industries.
• Progressive curriculum development ensures our program remains contemporary and competitive.
• Our Career Building Platform (CBP) extends learning beyond the classroom to give you the professional and personal skills you'll need to succeed.

Thinking about transferring to New York Tech ? Take advantage of our rolling admissions and generous transfer credit acceptance policy and begin taking upper-level business courses right away!

Grad School/Employers

• Bank of America
• Goldman Sachs
• JPMorgan Chase

Career Options

• Budget Analyst
• Business Analytics Manager
• Compensation and Job Analyst
• Database Administrator
• Information Security Analyst
• Logistician
• Management Analyst
• Market Research Analyst

“New York Tech gave me a strong understanding of accounting. Any CFO needs to know how to read financial statements.You must be able to balance the accounting and finance sides of the job.”

Bill Zerella (B.S. ’78)

Exceptional experiences. Outstanding outcomes.

In New York State for the economic value of a New York Tech M.B.A. (Georgetown University Center on Education and the Workforce, 2022)

In New York State for inclusion, diversity, and international student representation. (2022 Wall Street Journal/Times Higher Education College Rankings)

Average financial aid award for undergraduate students at our New York campuses.

$ 119 ,0 40

Mean annual wage for data scientists, May 2023. (Georgetown University Center on Education and the Workforce)

How You’ll Succeed

The demand for analytics graduates in the job market is expected to grow considerably in the near future. Across the globe, organizations are heavily investing in analytics to capture insight from a large amount of data generated daily. Employers now realize that they need to hire people who have technical expertise in data analytics and who understand the implications of the analysis, to help organizations make intelligent business decisions.

Upon completion of your B.S. degree, you'll be able to:

• Conduct research, collect data, and analyze it using appropriate tools.
• Develop appropriate models using analytics algorithms to solve business problems and provide insights.
• Integrate functional disciplines together to effect sound policymaking and business planning.
• Take on responsibilities in data analyses, visualization, future prediction, and decision-making.

For those interested in even greater career preparation, the B.S. with Accelerated M.S. or M.B.A. Options (4+1) program offers you the opportunity to earn your B.S. and either an M.B.A. or an M.S. in Risk Management in just five years.

The School of Management's Bachelor of Science programs are accredited by AACSB International (The Association to Advance Collegiate Schools of Business).

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