Master’s in Data Science: Everything You Need to Know
Organisations today heavily rely on data for informed decision-making, and this dependency is likely to increase. The US Bureau of Labor Statistics projects a 21 percent increase in data science-related jobs between 2020 and 2030. Across industries like healthcare, finance, and e-commerce, data scientists are sought after to analyse data, create predictive models, and drive precise and profitable outcomes. For instance, Amazon, a major online retailer, uses data scientists to develop predictive models and analyse customer trends. Similarly, UnitedHealth Group employs data scientists to enhance patient outcomes and reduce healthcare costs through predictive models.
What is a master’s in data science?
A master’s in data science is a graduate-level academic programme that equips students with advanced knowledge and skills in data analysis, machine learning, statistical modelling, and data-driven decision-making. This specialised degree programme combines aspects of computer science, statistics, and domain expertise to train individuals to extract valuable insights and knowledge from large and complex datasets. Students typically learn how to clean, pre-process and analyse data, develop predictive models, and apply advanced algorithms to solve real-world problems across various industries. The programme often includes hands-on projects and practical experience, enabling graduates to excel as data scientists, analysts, or researchers in today’s data-driven professional landscape.
What are the specialisations for a master’s in data science?
Master’s in data science programmes offer various specialisations that allow students to tailor their studies to match their interests and career aspirations within the broad field of data science. Some common specialisations within a master’s in data science include:
- Data mining and machine learning: Covers the techniques to extract meaningful insights and make predictions from large and complex data sets. Topics may include regression analysis, decision trees, clustering, and neural networks.
- Data visualisation: Focusses on tools and techniques used to create visual representations of data that others can easily interpret. Topics may include data visualisation software such as Tableau and techniques such as scatter plots, heat maps, and network graphs.
- Statistics and Probability: Delves into the fundamental principles of statistics and the theory of probability, covering subjects such as hypothesis testing, Bayesian analysis, and the study of various probability distributions.
- Database management: Explores the fundamentals of overseeing databases, encompassing concepts like data modelling, database design, and programming in SQL.
- Big data analytics: Covers the techniques for processing and analysing large and complex data sets, including distributed computing, parallel processing, and cloud computing.
- Ethics and privacy: A specialisation in ethical considerations surrounding data science and protecting personal information. Topics may include data privacy laws, data anonymisation techniques, and ethical considerations for working with sensitive data.
- Business analytics: Focuses on using data to make business decisions and covers market analysis, customer segmentation, and revenue optimisation.
- Healthcare analytics: Emphasises applying data science techniques to healthcare data for disease prediction, patient outcomes, and medical imaging analysis.
- Financial analytics: Covers the use of data to make informed financial decisions, including risk assessment, investment strategies, and fraud detection.
- Social network analysis: Explores relationships and patterns within social networks, often used for influence analysis, community detection, and recommendation systems.
- Environmental data science: Applies data science methods to environmental data for tasks like climate modelling, resource optimisation, and environmental impact assessment.
These are just a few examples of the many specialisations available for a data science master’s degree. Before choosing one, consider your interests, career goals, and the demand for the job.
Is a master’s in data science worth it?
Whether a master’s degree in data science is worth pursuing depends on various factors, including your career goals, current skills, and the specific programme you’re considering. Data science is a rapidly growing field. While online courses and resources work for personal projects and informal learning, a master’s in data science offers a better understanding of the subject and provides specialised knowledge and skills in high demand.
A master’s degree in data science can open prospects in many countries, particularly those with solid technology and business sectors. Countries like the US, Canada, the UK, Germany, Australia, and Singapore have robust job markets for data scientists. Numerous companies across various industries hire data scientists. Examples include tech giants like Google, Facebook, Amazon, and Microsoft, as well as financial institutions, healthcare organisations, retail companies, and consulting firms. Currently, there are as many as 780+ full-time job listings on Google for those with a master’s in data science.
Moreover, pursuing a master’s in data science abroad provides unique opportunities for gaining international experience, learning from renowned professors, and conducting cutting-edge research.
What is the average salary for those with a master’s in data science?
Salaries for those with a master’s degree in data science vary by industry, company, location, and experience level. For example, the starting salary of an environmental data scientist is approximately $5,934 a month, while a healthcare data scientist earns a monthly salary of $12,594.
According to Glassdoor, the salary for a data scientist can range from $80,000 to $150,000 per year, with an average of $117,000 per year. Overall, pursuing a master’s in data science is a wise investment for those interested in this field and who have the potential to develop the skills and knowledge necessary to succeed.
Read our blog on how a master’s degree can help you get a better job.
What are the top jobs for those with a master’s in data science degree?
In the rapidly evolving landscape of data-driven industries, a master’s in data science unlocks a realm of top-tier job opportunities. Graduates are poised to excel in diverse and impactful roles, including:
- Data Scientist: This role requires you to analyse complex datasets to extract valuable insights, develop predictive models, and aid decision-making processes across various industries.
- Machine learning engineer: As a machine learning engineer, you will implement and optimise machine learning algorithms to create AI-powered applications and systems, such as recommendation engines or fraud detection tools.
- Business intelligence analyst: In this role, you will use data to generate actionable insights, create reports, and support strategic planning for companies to improve performance and competitiveness.
- Data engineer: You will build and manage the infrastructure required to collect, store, and process large volumes of data, ensuring data accessibility and quality for analysis.
- Quantitative analyst (Quant): As a quant, you will apply advanced statistical and mathematical techniques to financial data, assisting investment firms in making informed trading and risk management decisions.
- Research scientist: In this role, you will leverage data to conduct experiments, develop new algorithms, and contribute to advancements in healthcare, genetics, and social sciences.
- Data analytics manager: You will lead teams utilising data to drive business strategies, overseeing data-related projects, and communicating insights to stakeholders.
What are the application requirements for a master’s in data science?
The prerequisites for a master’s in data science can vary depending on the university and programme. However, most programmes require applicants to have a bachelor’s degree in a related field. For instance, to apply for a master’s in data science at Fordham University, you must have an undergraduate degree in a quantitatively focused field, like computer science, engineering, math, or business. You should be proficient in discrete math, probability and statistics, descriptive statistics, and basic probability concepts. The university expects you to know basic Python programming. You can acquire the latter by completing a course called CISC 5380 Programming with Python. If you do not wish to complete this course, you must take a placement exam, which covers the fundamentals of the Python language. However, the typical application requirements for a master’s in data science include the following:
1) Transcripts for a master’s in data science
Typically, most programmes require official transcripts from all institutions you attended for your undergraduate and graduate studies. These transcripts should showcase the courses you took, your grades, and your degrees.
According to the Education Testing Service (ETS), the typical undergraduate GPA for candidates admitted to computer science master’s programmes in the US ranges between 3.14 and 3.68 on a scale of 4.0. In the UK, the norm is often a minimum of a 2:1 or first-class honours degree in a related discipline, such as computer science, mathematics, statistics, or engineering. Meanwhile, Canadian universities frequently adopt a 4.0 grading scale, with 4.0 equating to an A (90-100 percent) and 2.0 equating to a C (60-69 percent).
Indian universities, on the other hand, usually stipulate a minimum admission requirement of 60 percent or higher for master’s in data science programmes, roughly equivalent to a 6.0 GPA on a 10-point scale. Nevertheless, certain institutions might impose a higher GPA benchmark or specific computer science, mathematics, or statistics prerequisites.
The precise undergraduate subjects mandated for a master’s in data science depend on the specific university and programme. However, a solid grounding in computer science, mathematics, and statistics is often a prerequisite. Subjects that give applicants an extra edge include proficiency in programming languages such as Python, R, and SQL and coursework in calculus, linear algebra, probability, and statistics.
2) GMAT/GRE for a master’s in data science
The GMAT/GRE is not mandatory for all master’s in data science programmes in the US, UK, Canada, and India. However, some universities and programmes recommend taking the GMAT/GRE as part of the application process, especially if you don’t have a background in computer science or a related field. Others may waive the requirement if you have relevant work experience or have completed a master’s or PhD in a related field.
In the US, some universities offer waivers for the GMAT/GRE requirement for applicants with high GPAs, strong letters of recommendation, or other qualifications demonstrating academic and professional potential. In the UK and Canada, some universities may also offer test waivers to students who meet specific criteria, such as having a certain level of professional experience, a high undergraduate GPA, or a relevant professional qualification.
In India, some universities offer waivers to those who have demonstrated academic excellence in undergraduate studies, hold a relevant professional qualification, or have a certain level of work experience.
The GRE is usually favoured over the GMAT for admission to a master’s in data science programme. The GRE assesses readiness for graduate studies, including skills like analytical writing, verbal reasoning, and quantitative reasoning, which are crucial for data science. A few data science programmes, such as the University of North Carolina Charolette’s MS in Data Science and Business Analytics and the Rutgers School of Arts and Sciences’ MS Program in Data Science may consider GMAT scores.
It’s important to note that these scores are just averages, and many programmes may have higher or lower score requirements. Applicants should research the specific requirements of the programmes they are interested in to determine the average GRE or GMAT scores needed for admission.
Read our blog on what admissions officers seek beyond the GMAT/GRE scores.
3) IELTS/TOEFL for a master’s in data science
IELTS and TOEFL are accepted by most universities and master’s in data science programmes as proof of English language proficiency. However, checking with the specific programme is essential to determine which test is preferred or accepted. Generally, TOEFL is more commonly used in the US, while IELTS is preferred in the UK and other countries.
Most universities require a minimum score of 6.5 or 7.0 on the IELTS or an 80-100 in TOEFL for admission to graduate programmes.
Read our blog on why English language proficiency tests are important.
4) Work experience for a master’s in data science
5) Resuméfor a master’s in data science
Your Resumé for admission to a master’s in data science programme should highlight your academic achievements, relevant coursework, work experience, skills, and other relevant information demonstrating your suitability. It’s important to note that there is no specific format for a master’s in data science application resume. However, you should ensure your Resuméis professional, well-organised, and easy to read.
Watch our webinar on how to write an effective Resuméfor a master’s programme.
6) Letters of recommendation (LORs) for a master’s in data science
Most universities typically require two to three LORs as part of the application process, but this number can vary. It’s crucial to pick referees who are familiar with your qualities and can specifically address your academic or professional competence, especially in data science.
Ideally, you should approach people who have supervised your work, instructed you in pertinent courses, or collaborated with you on data science-related projects. But do not assume that you only need to request that your recommender write your LOR. Like every other application component, LORs require planning.
Along with your request, provide relevant details about your background and accomplishments in data science. Include your resume, transcripts, or an overview of your academic and professional feats within the discipline, and leave nothing to chance.
An impactful LOR should highlight your skills, experiences, and accomplishments in data science. It should emphasise your academic or professional triumphs, diligent work ethic, and other qualities that position you as an ideal candidate. Ultimately, the composition of your LORs should adhere to the programme’s specific requisites.
Watch our webinar to discover LOR strategies for a master’s in data science.
7) Statement of purpose (SOP) for a master's in data science
An SOP for a master’s in data science is a personalised essay that outlines your motivations, aspirations, and qualifications for pursuing the programme. It provides insight into your academic and professional background, highlights your specific interests within data science, and explains how the programme aligns with your goals. The SOP offers an opportunity to showcase your unique perspective and articulate why you are an ideal candidate. Read our blog for tips on writing an impactful SOP.
The number of essays essential for securing admission to a master’s in data science programme can differ. Typically, programmes require at least one essay, which is usually the SOP. Some universities may ask for additional writing samples, such as a personal statement, a research proposal, or a diversity statement.
Adherence to the provided essay prompts, clear and concise writing, effective communication, and thoughtful expression are crucial. It’s essential to review the SOP prompts, as they can vary.
Watch our webinar on crafting a killer SOP for a master’s in data science programme.
8) Interviews for master’s in data science
While not mandatory for all master’s in data science programmes, some institutions may request an interview as part of the application process. Typically conducted after the initial application review, these interviews are conducted in-person or remotely, either online or via phone. During the interview, expect questions about your academic and professional history, your motivation for selecting a master’s in data science, and your career aspirations. Technical inquiries about data science concepts also arise, necessitating confident responses. Approaching the interview with self-assurance and eagerness is crucial.
Furthermore, be prepared to pose thoughtful questions of your own. Doing so will give you deeper insights into the programme and showcase your enthusiasm and dedication to pursuing a master’s in data science. Here are 10 tips to ace your interview.
There are different interview formats. Some common ones include:
- One-on-one interviews: This format involves a personalised conversation between an applicant and an admissions officer. The discussion may delve into your background, qualifications, and reasons for pursuing a master’s in data science.
- Group interviews: In this scenario, several candidates are interviewed simultaneously by one or more admissions officers or faculty members. A group interview often emphasises evaluating teamwork dynamics, communication skills, and interpersonal abilities.
- Alumni interviews: Certain institutions might arrange interviews with data science graduates. Alumni interviews emphasise the applicant’s alignment with the programme and potential integration into the network.
What are the best countries to study a master’s in data science?
Country | Duration | Approx fee (annual) | Post-Study work |
Master’s in Data Science – India | 2 years | INR 1 to INR 5 Lakhs | – You may pursue a PhD in data science – Seek employment in finance, healthcare, technology, or consulting. – Start a data science consulting firm. |
Master’s in Data Science – Canada | 1-2 years | CAD 20,000 to CAD 40,000 | The Post Graduation Work Permit (PGWP) allows international students to work in Canada for up to 3 years after graduation. |
Masters in Data Science – USA | 1-2 years | USD 30,000 to USD 60,000 | While most international students can work for 12 months after graduating, STEM graduates are eligible for a three years stay under the Optional Practical Training (OPT) option. |
Masters in Data Science – UK | 1-2 years | GBP 15,000 to GBP 30,000 | The UK’s Graduate Route Visa enables all international students to work for two years after graduation. |
What are the top universities for a master’s in data science?
Indian Institute of Technology (IIT) Bangalore – India – Master’s in Data Science – Executive Post-Graduate Programme
| Acceptance rate | Application Requirements | Class Size | Specialisations |
| 2.5% | – Online form – Transcripts – GRE/ GMAT/ CAT/IIT-B Selection Test scores – SPP – Resume – 2 LORs – International applicants need TOEFL/IELTS – Work experience is not required but is beneficial | 30-40 | – Machine Learning, – Data Mining – Big Data Analytics
Students can also specialise in data science for healthcare, finance, or engineering. |
Indian Institute of Management (IIM) – India – Master’s in Data Science and AI
Acceptance rate | Application Requirements | Class Size | Specialisations |
5% | – Online form – Transcripts – GMAT/CAT score – SOP – Resume – 2 LORs – International applicants need TOEFL/IELTS – Work experience is not required but is beneficial | 50-60 | – Machine Learning, – Deep Learning – Natural Language Processing – Computer Vision Students can also specialise in data science for healthcare, finance, or marketing. |
New York University (NYU) – US – Master’s in Data Science
Acceptance rate | Application Requirements | Class Size | Specialisations |
15% | – Online form – Transcripts – GRE/GMAT scores (Optional) – TOEFL/IELTS – 3 LORs – Resumé – Statement of Academic Purpose – Work experience is not required | 50-70 | – Data Science Theory and Methods – Natural Language Processing – Computational Social Science – Machine Learning – Computer Vision Students can also specialise in data science for healthcare, finance, or social impact. |
University of Chicago – US – Master’s in Data Science
Acceptance rate | Application Requirements | Class Size | Specialisations |
15% | – Transcripts – GRE/GMAT scores (Optional) – TOEFL/IELTS – 2 LORs – Resumé – SOP – Work experience is not required | 60 | – Data Science – Computational Analytics – Decision Analytics. Students can also specialise in data science for healthcare, finance, or marketing. |
Texas A&M University – US – Master’s in Data Science
Acceptance rate | Application Requirements | Class Size | Specialisations |
35% | – Transcripts – GRE/GMAT scores (Optional) – TOEFL/IELTS – 3LORs – Resumé – Optional Material – SOP – Work experience is not required | 30-40 | – Statistical Modelling – Machine Learning – Data Visualisation – Big Data Analytics. Students can also specialise in data science for healthcare, energy, or finance. |
Columbia University – US – Master’s in Data Science
Acceptance rate | Application Requirements | Class Size | Specialisations |
16% | – Online Application – Personal Statement – Transcripts – 3 LORs – Resumé – GRE scores – Work experience is not required | 120-150 | – Data Science – Machine Learning – Computational Biology Students can also specialise in data science for healthcare, energy, or social science. |
City University London – UK – Master’s Data Science
Acceptance rate | Application Requirements | Class Size | Specialisations |
5.7% | – Online Application – Personal Statement -Transcripts – Resume – Work experience – LORs may be requested later to finalise admission | 50-100 | Along with the six core modules, students can specialise in any two of the following elective modules: – Advanced Databases – Information Retrieval – Data Visualization – Digital Signal Processing – Audio Programming – Deep Reinforcement Learning – Computer Vision – Semantic Web Technologies – Knowledge Graphs – Natural Language Processing |
The University of British Columbia- Canada- UBC Master of Data Science
Acceptance rate | Application Requirements | Class Size | Specialisations |
15.54% | -Online Application -Transcript – One-page Letter of Intent – Resume/CV – 3 LoRs | 60-90 (depending on campus) | – Data Visualization – Supervised Learning – Communication & Augumentation – Unsupervised & Semi-supervised Learning – Advanced Predictive Modelling – Advanced Machine Learning – Language Data Processing Techniques |
FAQs about master’s in data science
1) Are a master’s in data science and a master’s in business analytics the same?
While a master’s in data science and a master’s in business analytics seem similar, with overlapping content, there is a difference.
A master’s in data science primarily focuses on the intricate technical facets of data analysis, encompassing in-depth exploration of machine learning algorithms, statistical analysis, data visualisation, and database management. The program intends to give students a robust grounding in computer science, programming, and mathematics, coupled with hands-on exposure to managing extensive data sets.
On the other hand, a master’s in business analytics focuses on using data analysis to overcome business challenges. It gives students a solid foundation in statistical analysis, data visualisation, predictive modelling and various business concepts. Practical engagement with these methodologies in tackling real-world business problems is a defining feature of this programme.
Before applying for these programmes, students must understand them and identify which resonates best with them.
2) Can I pursue a master’s in data science after BBA?
Yes. You can apply for a master’s in computer science with a Bachelor of Business Administration (BBA) degree. However, certain data science programmes may require maths, statistics, and computer science coursework. Researching programme requirements is crucial to assess eligibility.
Some programmes provide foundational courses or bridge programmes to fill gaps in prerequisites. Relevant experience in areas like business analytics or programming enhances your chances of admission. Though less common, a BBA background is feasible for a Master’s in Data Science with suitable preparation and background knowledge.
3) Is a master’s in data science suitable for working professionals?
Absolutely! A master’s in data science is well-suited for working professionals seeking career advancement in the data science domain. Numerous programmes accommodate professionals, offering flexible schedules like part-time or online coursework. Part-time options are advantageous for those juggling full-time jobs and other commitments. Online programmes provide additional flexibility, catering to those unable to attend in-person classes. Some programmes even feature evening or weekend classes to suit working schedules.
Moreover, these master’s programmes often integrate practical projects, allowing professionals to apply their learning to real-world scenarios, enhancing their on-the-job capabilities. Pursuing a master’s in data science is an excellent route for working professionals aiming to excel in the dynamic realm of data science by acquiring essential skills and knowledge.
4) Is a master’s in data science suitable for non-programmers?
Typically, a master’s in data science assumes a certain level of programming proficiency. While it may cover programming basics, prior experience is beneficial. Some master’s in data science programmes provide foundational courses or bridge programmes to help non-programmers catch up.
Individuals without programming experience may find it challenging to grasp concepts. While not always mandatory, a programming background offers a valuable edge. Non-programmers might consider additional courses or self-study to acquire the necessary skills for success.
5) What’s the duration of a master’s in data science?
The length of a master’s in data science programme varies. Generally, full-time on-campus programmes take one to two years, while part-time or online options might extend beyond two years. Some programs offer flexibility in completion time. Research specific programmes for accurate duration and consider accelerated or self-paced options.
6) Is a master’s in data science hard?
A master’s in data science programme can present challenges, especially for newcomers to the field or those lacking a computer science and statistics background. The curriculum typically demands proficiency in maths, statistics, programming, and handling extensive data sets. Success requires diligence, time management, and a readiness to embrace new challenges. Extensive extracurricular practice is crucial to applying learned skills. While demanding, a master’s in data science offers gratifying prospects and ample career openings for those committed to overcoming its challenges.
Why consider The Red Pen Postgraduate Admissions Team?
- The Red Pen Postgraduate Admissions Team is dedicated to helping you identify and apply to international master’s and PhD programmes that align with your academic and career goals.
- We are members of the IECA (Independent Educational Consultants Association), which gives us access to the latest updates on university trends.
- We work with applicants with diverse interests in finance, technology, general management, advertising, marketing, entrepreneurship, computer science, data science, analytics, sustainability and social impact, among others.