A master’s in data analytics helps hone expertise and gain useful experience. As more companies and organizations rely on data to make decisions and refine business operations, data analysts are increasingly becoming valuable in the workforce.
According to a Harvard Business Review article, decision-makers can be broken down into distinctive groups. Unquestioning empiricists trust analysis over judgment, and visceral decision-makers go exclusively with their gut.
On the other hand, informed skeptics are the employees best equipped to make good decisions—effectively balance judgment and analysis, possess strong analytical skills, and listen to the opinions of others but are willing to dissent. They’re the kind of workers every company should try to cultivate.
Earning a master’s degree in data analytics gives you an edge by helping you become that data-savvy employee every organization is looking for. It makes you well equipped to enhance your decision-making skills when you already have expertise in your domain and a huge amount of data in hand.
Why a Master’s in Data Analytics?
Data analytics generally refers to searching for meaning, trends, or signals in massive data sets. These data sets can come from all kinds of places. And data can range from super refined and super specific (like measuring a patient’s heart rate in a hospital) to broad data, such as world population numbers.
What’s important here is to know that data is becoming more granular and sophisticated being collected on more and more activities. But all of this data is useless unless someone can make sense of it. And that’s where a data analyst comes in.
A postgraduate degree in analytics provides you with advanced skill sets, hands-on experience, and the technical skillset to analyze data using the latest tools and techniques. These practical skills and experience help you effectively communicate data-driven insights and decisions.
A question that most commonly arises is why you need to choose a master’s in data analytics when other traditional degrees in statistics and computer science are available. In other words, what makes a master’s in data analytics different than more traditional fields and degrees?
One of the key differences between a statistician’s analysis and data analysis by an individual with an analytics degree would be the approach to work on the data. A statistician would generally use hypotheses and rigorous mathematical theorems to observe data samples and reach an inference only to understand a particular aspect of a sample.
A degree in computer science could be taken by anyone interested in computer hardware, software, machine learning, AI, cybersecurity, database architecture, and so on. This is a lot to unpack if you are interested in only working with data to use it to your benefit.
Someone who has a degree in analytics follows the approach of sifting through enormous amounts of data using tools such as Python or Hadoop to clean, inspect, and model the data to present it to non-technical users.
Universities offer both on-campus and online courses. In the last four to five years, many top universities have started offering online and executive programs in data analytics.
Data-driven programs or courses have many forks and categories, and people often seem to be confused between the offerings. There are programs related to masters in information, data science, business analytics, health informatics, data analytics, bioinformatics, sports management specializing in sports analytics, and many more. The confusion is more evident when the decision pertains to enrolling for data science, information, or data analytics.
Data science bends more towards computer science and mathematics. It is more likely to be offered by engineering programs at universities. This includes parsing the data, machine learning, predictive analysis, and visualization. Information science focuses more on library science, cognitive science, and human-computer interaction.
On the other hand, data analytics leans towards performing statistical analysis on data sets to find answers to business problems with some intersection with the data science degree. Programs such as health informatics/analytics or business analytics narrow it down to domain expertise while still teaching the core of data analytics.
In the last decade, there has been an emergence of new departments at the universities for information and data-related degrees. Below is the list of universities that offer a degree in master’s of data analytics. The list is not exhaustive but gives a general idea of admission requirements and course curriculum.
Data Analytics Master’s Curriculum
Depending on the university, a master’s in data analytics program requires 30 to 50 credits to graduate. Before admission, they need the students to have completed a bachelor’s degree with some prerequisites like a fundamental understanding of statistics or mathematics and programming.
Universities might waive some prerequisites or may require you to complete them once admitted. Suppose a degree is offered from a University in the United States, Australia, or some parts of Europe. In that case, the universities might have requirements to complete the TOEFL or IELTS exam to check for your understanding of reading, writing, and speaking skills in English for international candidates.
Some well-known universities emphasize the importance of the GRE or GMAT during the admission process.
However, most universities had waived this requirement for Fall 2021 due to the COVID-19 pandemic and may continue to do so in the future.
Courses in the master’s program for data analytics prepare students to demonstrate experience in data collection, processing, analysis, retrieval, mining, visualization, and prediction. They help you learn the methods from information retrieval, statistical data analysis, data mining, machine learning, and other big data-related fields. Students work on capstone projects that provide hands-on experience dealing with industry-scale data sets and solving real-world problems. It also exposes them to the evolving trends in working with data in research and industry.
Some universities might also include a requirement for internship credits to get you industry experience before graduation. Students also are given an option to select elective courses to tailor their experience according to their interests if they offer a program in collaboration with other departments such as computer science, statistics, business, sports, or health in select universities.
Career Pathways
Students graduating with a data analytics master’s degree would be able to pursue a career pathway:
| Roles | Description |
|---|---|
| Data analyst | Their responsibilities include analyzing the organization’s data to find value and opportunities. Data analysts can be found in every industry, and job titles vary. Some roles will have industry-specific names like health data analyst, business analyst, market research analyst, security analyst, sports analyst, and similar titles. Learn more about a career as a data analyst here. |
| Data scientist | Data scientists have more technical knowledge in comparison with data analysts. They are the ones who can form questions and develop informed perspectives using algorithmic predictions. Learn more about a career in data science here. |
| Data engineer | Data engineers often focus on optimizing the infrastructure surrounding different data analytics processes. They work closely with data scientists and data analysts to capture big data used in analytics and prediction models. |
| Data architect | A data architect builds the infrastructure surrounding data analytics in an organization. They collaborate with data scientists and other engineers to work with the organization’s data infrastructure needs. |
| Machine learning engineer | A machine learning software engineer requires engineers to know about machine learning. This role’s responsibility includes scaling and deploying machine learning models in production. |
| Consultant | An analytical consultant may be someone who is not an in-house data scientist or data analyst. Their role often does not involve implementation, and they may work with many organizations simultaneously. |
| Leadership roles | Leadership roles such as chief data officer in the health sector, sports administration, managerial roles in supply chain management need analytical skills to make informed decisions and expertise in their domain and administration. Learn more about data analytics MBA programs here. |
Data Analytics Master’s FAQs
This is a common question for someone looking for an upward career transition or a change in their career track. If you come from a background where mathematics or statistics were not the foundation of your course curriculum, you can look for universities that can provide you with these fundamental courses. You can also reach out to the program’s admissions department about the possibility of fulfilling the requirements before the start of the degree program.
The answer to this question depends on factors such as the amount of overlap between your computer science degree and other data-related courses, and their relevance to concepts such as big data. If your bachelor’s degree was more theory-based, a master’s degree in analytics would be a fast-track option to help jumpstart a career in data-related fields.
This depends on the industry that you are trying to transition into. Fields like finance or legal firms, for example, typically require a degree and certifications that showcase your knowledge. However, there are other industries and career paths that do not require the same kinds of traditional experience.
A master’s degree is beneficial if there is a knowledge gap when it comes to data-related roles. If you have been in the data industry long enough to gain technical expertise and have acquired soft skills, then this degree is not required. Graduate certifications can help enhance your knowledge of trends and evolving techniques.
A good program develops your technical skills and helps develop the soft skills needed in this cross-functional and highly collaborative field. The program should be flexible enough to create a curriculum that allows you to tailor it to your needs while maintaining the core analytics foundation. The ultimate goal of a graduate program should be to help you build a solid professional network and a distinctive portfolio of projects to demonstrate your skills.