Master’s in accounting with a concentration in data analytics online is a part of a new trend in the educational world where a growing number of universities are offering this type of concentration.
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A master’s in data analytics prepares you for a career crunching and interpreting big data sets. However, that’s not all it does; it can also pave your way in the much broader discipline of data science. The two fields are more alike than you may think.
Accountants use data analytics to help businesses uncover valuable insights within their financials, identify process improvements that can increase efficiency, and better manage risk. “Accountants will be increasingly expected to add value to the business decision making within their organizations and for their clients,” comments Associate Professor Wendell Gilland, who teaches Data Analytics for Accountants at UNC Kenan-Flagler Business School. “A strong facility with data analytics gives them the tool-set to help strengthen their partnership with business leaders.”
Here are a few examples:
Auditors, both those working internally and externally, can shift from a sample-based model to employ continuous monitoring where much larger data sets are analyzed and verified. The result: less margin of error resulting in more precise recommendations.
Tax accountants use data science to quickly analyze complex taxation questions related to investment scenarios. In turn, investment decisions can be expedited, which allows companies to respond faster to opportunities to beat their competition — and the market — to the punch.
Accountants who assist, or act as, investment advisors use big data to find behavioral patterns in consumers and the market. These patterns can help businesses build analytic models that, in turn, help them identify investment opportunities and generate higher profit margins.
There’s a lot of confusion these days about what data analytics is and isn’t, and how it differs from data science. On the website insideBIGDATA, business technology consultant Rick Delgado writes that data analysis involves “combing through data to find nuggets of greatness that can be used to help reach an organization’s goals… It tends to be slightly more business and strategy focused” than data science.
On the other hand, Adam Hunt, chief data scientist at RiskIQ, told CIO Magazine that people use data science, not analytics, to come to “conclusions that drive your data forward. If you’re not solving a problem with data… that’s just analysis. If you’re actually going to use the outcome to explain something, you’re going from analysis to science. Data science has more to do with the actual problem-solving than looking at, examining, and plotting data.”
Who’s right? Paradoxically, they both are. That’s because the gulf between data analytics and data science seems to be narrowing; as it does, definitions of each shift. Some colleges and universities, like CUNY City College, have actually transformed their data analytics programs into data science programs. Others offer one or the other, while a few offer both a Master of Science in Data Analytics and a Master of Science in Data Science (which are typically administered by a business school and an engineering school, respectively).
Outcomes suggest a degree in data analytics can lead to careers in both business analytics and data science. About 30 percent of students who graduate from North Carolina State University at Raleigh Institute for Advanced Analytics with a Master of Science in Analytics, for example, become data scientists, while only 20 percent become analysts.
Some people will tell you that a master’s in data analytics isn’t worth it in 2020 because most companies are looking for data scientists, not analysts. That guidance may be out-of-date; increasingly, there’s no hard line between the two. Today’s graduate-level analytics programs are often focused on “advanced analytics” and have a lot more in common with data science programs than business intelligence programs. Machine learning? Predictive modeling? You’ll learn about both in the top data analytics master’s programs.
In this article about whether a master’s in data analytics is worth it, we answer the following questions:
- What is a master’s in data analytics?
- What can I do with a data analytics master’s degree?
- How much do people with this degree typically earn?
- Is this the same degree as a master’s in data science?
- How long does it typically take to earn this degree?
- Are experienced data analytics professionals in demand?
- What kind of return on investment can I expect if I earn this degree?
What is Accounting Analytics?
Definition of Accounting Analytics
Accounting analytics is the application of data analytics and big data technologies to the field of accounting. In addition to helping accountants manage typical tasks, accounting analytics enables financial professionals to answer business questions, shape corporate strategy, forecast financial trends, thwart fraud, and more!
The Current Role of Analytics in Accounting
Budgeting, planning, data management, auditing—accountants are used to dealing with data. In fact, most professionals have already mastered two types of analytics:
Descriptive Analytics: By summarizing and interpreting raw data, accountants find answers to what has happened. For example, analysts frequently use sums, averages, and percent changes to calculate sales results, inventory stock, cost per customer, average dollars spent, year-over-year change in sales, etc. These data are used to generate reports (e.g. operations, finance, and sales).
Diagnostic Analytics: Accountants also deploy data analytics and data mining to discover why something happened. They create variance reports to show differences between budgeted amounts and actual income or expenses. They conduct financial audits to pinpoint fraud or errors. They employ tools and software to look for patterns and problems in large data sets.
These skills are great, but they’re not enough. As automation takes over day-to-day tasks, accountants are increasingly being asked to act as data scientists—to incorporate non-financial data into their analyses, predict financial performance, and advise their company on actions to take.
What is a master’s in data analytics?
Data analytics master’s degree programs prepare students with strong STEM and/or business backgrounds to step into specialist roles in analytics, business intelligence, and even data science. Core classes in master’s in data analytics programs focus on topics related to collecting, organizing, and analyzing information using a variety of techniques. Coursework typically touches on:
- Advanced data analysis
- Applied statistics
- Data management
- Data visualization
- Modeling techniques
- Statistical analysis
- Systems architecture
- Working with large data sets
Many programs also cover more technical topics like:
- Artificial intelligence
- Data mining
- Data structures and algorithms
- Information systems
- Machine learning
- Predictive modeling
- Software engineering
- Visual analytics
Most graduate programs in data analytics also require students to complete a capstone course, thesis, research project, or internship. These experiential learning opportunities enable students to pit their evolving skill set against real-world challenges to prepare them for their future analytics careers.
Graduate schools have different naming conventions when it comes to master’s programs in analytics and approach these programs differently, too. You might earn a Master of Science in Analytics or a Master of Data Analytics degree. Some programs pair business intelligence or applied statistics and analytics while others pair analytics and data science or visualization. There are technically advanced analytics master’s programs and programs that devote more credit hours to teaching students about the types of business challenges that analytics can solve.
What you need to know is that nearly all of them will, to some degree, teach you how to:
- Use tools like Tableau, Python, Hive, Impala, PySpark, Excel, SQL, and Hadoop
- Find meaning in raw data and manage unstructured data
- Uncover useful patterns in that data
Online Master’s in Applied Data ScienceSyracuse University’s online Master’s in Applied Data Science can be completed in as few as 18 months.
* No GRE Scores RequiredLearn More
Southern Methodist University
Online Master of Science in Data ScienceSMU prepares you to manage and analyze large amounts of data and drive strategic change in organizations.
* GRE waivers available.Learn More
University of Denver
Online MS in Data Science programEarn your MS in Data Science online in as few as 18 months. Bridge courses are available.
* GRE waivers are available.Learn More
University of California, Berkeley
Online Master of Information and Data Science (MIDS)Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.
* No GRE Scores RequiredLearn More
University of Dayton
Master of Business AnalyticsGain in-demand analytics skills with an online master’s in business analytics. Complete in as few as 12 months.
* GMAT waivers are available to professionals with at least three years of business experience.Learn More
Online Master of Science in AnalyticsMake informed decisions using data analysis in 12 months with a Master’s in Business Analytics online from American University.
* No GMAT/GRE required to apply.Learn More
Master’s in Business AnalyticsEarn your MS in Business Analytics online from Pepperdine University.
* GMAT waivers are available for qualified applicants.Learn More
Online Master of Science in Business AnalyticsLooking to become a data-savvy leader? Earn your M.S. in Business Analytics online.
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Four types of data analytics
To get a better handle on big data, it’s important to understand four key types of data analytics.
1. Descriptive analytics = “What is happening?”
This is used most often and includes the categorization and classification of information. Accountants report on the flow of money through their organizations: revenue and expenses, inventory counts, sales tax collected. Accurate reporting is a hallmark of solid accounting practices. Compiling and verifying large amounts of data is important to this accurate reporting.
2. Diagnostic analytics = “Why did it happen?”
Diagnostics are used to monitor changes in data. Accountants regularly analyze variances and calculate historical performance. Because historical precedent is often an excellent indicator of future performance, these calculations are critical to build reasonable forecasts.
3. Predictive analytics = “What’s going to happen?”
Here, data is used to assess the likelihood of future outcomes. Accountants are instrumental in building forecasts and identifying patterns that shape those forecasts. When accountants act as trusted advisors and build forecasts, business leaders grow increasingly confident in following them.
4. Prescriptive analytics = “What should happen?”
Tangible actions — and critical business decisions — arise from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, raise an alert on poor choices. This insight is an example of the significant impact that accountants make in the business world.
Why accountants make excellent data scientists
Accountants have outstanding technical skills. Gilland notes, “Accountants are used to aggregating information to create a picture of an organization that summarizes the details contained in each transaction. Working with descriptive analytics, predictive analytics, and prescriptive analytics comes more easily to people who already possess excellent quantitative skills.”
Accountants are natural-born problem solvers. The jump from descriptive and diagnostic analytics to predictive and prescriptive analytics requires that one shift from an organizational mindset to an inquisitive mindset; a shift from stacking and sorting information to figuring out how to use that information to make key business decisions. Accountants are experts at making this jump.
Accountants see the larger context and business implications. The true value of data analysis comes not at the point when the data is compiled, but rather when decisions are made using insights derived from the data. To uncover these insights, a data scientist must first understand the business context. Not only do accountants understand this context, they live it.
How can you become more data savvy?
Build your skills. A Master of Accounting degree from the University of North Carolina will significantly expand your knowledge of data analytics. The topic headlines one of our key courses. And, perhaps more importantly, data analytics is infused into many classes across our curriculum so that you can acquire this critical training in context with many other key topics.
What can I do with a Data Analytics Master’s Degree?
Earning this master’s degree can take your career in a lot of different directions. Employers in nearly every sector, from engineering to education, use big data. The sectors that employ the most data analytics professionals include finance, insurance, information management, manufacturing, scientific services, and technology. There are also data analytics jobs in agriculture, energy, entertainment, and real estate sales. After earning a master’s in data analytics, you might hold any of the following job titles:
- Analytics architect
- Analytics manager
- Analytics specialist
- Analytics product manager
- Big data analyst
- Business intelligence analyst
- Business intelligence architect
- Data analyst
- Data engineer
- Data mining analyst
- Data scientist
- Marketing analytics manager
How much do people with this degree typically earn?
How much you can earn with a master’s in data analytics depends on many factors, from your job title to your location. Data analysts earn less than data scientists. West Coast analysts tend to earn more than those working on the East Coast or in the Midwest.
The average salary associated with an MS in Data Analytics is just over $77,000, according to PayScale. That figure includes early career data analysts earning $60,000, senior data analysts earning more than $80,000, and data scientists earning around $100,000.
What’s abundantly clear is that having a master’s degree can have a substantial positive impact on a data analyst’s earning potential and that there are plenty of affordable on-campus and online analytics programs at the graduate degree level. Based purely on the numbers, a master’s in data analytics is worth it. If you want to be totally sure that you get a big salary bump after earning this degree, choose a data science-focused program that includes core classes in data engineering, machine learning, and other high-tech topics.
Is this the same degree as a master’s in data science?
The frustrating answer is sometimes. The most significant difference between master’s in data analytics programs and master’s in data science programs tends to be the latter’s focus on predictive modeling and the development of custom algorithms. However, as discussed earlier, the distinction is growing hazier all the time. There are Master of Science in Analytics programs that are virtually indistinguishable from data science programs. Full-time and part-time students in the University of Chicago’s Graham School analytics program, for instance, take courses like Data Engineering Platforms, Data Mining Principles, Data Science for Consulting, and Machine Learning and Predictive Analytics.
Whether you’ll feel comfortable in a tech-focused program may depend on your background. Know that there are plenty of resources online that can get you up to speed in advanced programming techniques and machine learning basics.
How long does it typically take to earn this degree?
Most master’s in data analytics programs require students to complete 30 or so credit hours of coursework, and most full-time programs last two years. There are, however, some data analytics master’s degree programs that can be completed in just 12 months by students who take winter and summer classes.
Program length is less significant than program quality when calculating whether a master’s in data analytics is worth it. A bare-bones accelerated program will get you into the workforce faster. However, you may not graduate with the same depth of knowledge as someone in a longer, more involved part-time program that offers more hands-on learning opportunities. On the other hand, if you have a strong background in computer science, programming, or statistics and can take time off to immerse yourself in an intensive one-year data analytics master’s program, work can wait.
Are experienced data analytics professionals in demand?
Demand for data analysts and other data specialists is high—and growing, as humanity generates more and more data. “In the past, data was gathered by individuals,” explains Stephen Beyer, department chair in the School of Business and Information Technology at Purdue University (Main Campus). “Today, data is gathered by individuals and machines. While individuals sleep, machines never do… the constant collection of data, in almost all areas of life, requires people who can manage and make sense of this massive volume of data.” That data can be used to drive smarter decision making in marketing, manufacturing, medicine, and other fields, but only in the hands of those who understand what to do with it.
What kind of return on investment can I expect if I earn this degree?
Calculating the ROI can be tough for some degrees, but there’s not much ambiguity when it comes to this one. Earning a master’s in data analytics is worth it. According to data collected by North Carolina State University at Raleigh, it takes most master’s in data analytics graduates less than two years to recover the cost the school’s 10-month program plus lost earnings.
If you graduate from a school like Carnegie Mellon University, Massachusetts Institute of Technology (MIT), or Tufts University, you’ll pay more for your degree but chances are good you’ll command a higher salary after graduation, making it relatively easy to pay off your education.
You’ll also be in demand with this degree, and your salary will reflect that. Most experienced data analytics professionals earn somewhere between $82,750 and $138,000, which means earning a master’s in data analytics can lead to a stable and well-paid career. That’s doubly true if you’re open to the idea of working in data science. Data analyst salaries tend to increase over the first ten years and then max out. At that point, you can transition into data science or data engineering with this degree and some additional training.
Finally, this degree is a good investment simply because you may need it to land interviews—even for entry-level data analytics jobs. Master’s degrees are the new normal in data analytics. Twenty-five percent of hiring managers looking for data analysts “prefer or require candidates to have a graduate degree,” according to data scientist Randy Bartlett, who adds that having an advanced credential is increasingly essential if you want to stand out in this field. Need more proof that a master’s in data analytics is worth it? Burtch Works found that 89 percent of working data analysts with less than three years of experience have master’s degrees. That’s who you’ll be competing against for jobs.
Career outcomes for a Master of Science in Analytics
Today’s businesses rely on big data for everything from management to shipping. Professionals interested in taking command of that quantitative intelligence and establishing strategies for long-term growth may find a master’s in business analytics can be the start to building a career. There is demand for experts in data analysis from a wide variety of organizations interested in capitalizing on the strategic awareness made possible by gathering and processing large amounts of information.
If you’re considering pursuing an online master’s in data analytics, you should be aware of the huge range of possible career paths where you can take advantage of the skills and knowledge you’ll develop. New possibilities are opening up for individuals who are trained to use cutting-edge tools in collecting data, interpreting their findings and applying those insights to everyday problems. These are just a few of the avenues available for business-analytics master’s graduates.
Computer and information-systems manager
The tools that leaders and workers use on a daily basis affect every aspect of an organization’s operations. Managers in the field of computer and information systems take charge of technological resources to keep a business running smoothly and plan for future needs. Depending on the precise job title, there may be numerous variations in the duties involved:
- The chief information officer is responsible for organizing the company’s overall use of information technology to further its larger business goals, laying out strategies for growth over time
- A chief technology officer supervises both the use of current technology and the development of new systems and software to meet emerging business needs
- An IT director is in charge of the day-to-day operations of an IT department
- The IT security manager handles data and network security
Whatever their precise job description, computer and information-systems managers all have an integral role to play in setting the agenda for acquiring and applying technology, ensuring employees at every level of the organization have the tools and information they need to do their jobs effectively and efficiently. Their careers are rewarding ones, both because they can clearly see the results of their efforts and because of the average compensation. According to the Bureau of Labor Statistics, these professionals earned a median annual income of $135,800 in 2016.
Advertising and promotions manager
The worlds of big data and advertising are increasingly tied together. A decision-maker in charge of building a brand needs to approach the job with a creative outlook and an awareness of the best practices for deploying an array of programs and collateral materials. However, gathering and assessing quantitative information is just as necessary for finding the most effective means of spreading the word about a business.
To maximize their return on investment, advertising and promotions managers need relevant data and the knowledge to plan their campaigns accordingly. E-commerce analytics, customer surveys and other key sources of information drive choices like where to purchase ads and what audiences to target with online content. Organizations are getting to know their customers better by gaining perspective on their purchasing habits and preferences, so leaders in advertising and promotion must be equipped to put that intelligence to work.
General and operations manager
A general manager has a broad purview, keeping the regular activities of an organization moving forward. These responsibilities may include determining the utilization of personnel and materials, setting policies, and keeping employees on track in their daily output. An operations manager may have a narrower area of concentration, dealing with the production processes of the business, ensuring manufacturing and warehousing run in line with expectations.
In either position, a mastery of data analysis is a major asset. Quantitative reasoning informs smarter decision-making in supervising budgets, handling operational costs and making purchasing decisions. Keeping a company’s facilities functioning at their peak and scaling for future growth is the result of thoughtful planning based on carefully maintained data.
These professionals focus on finding ways to promote efficiency and increase profits for a business or agency, and their skills are in high demand. The Bureau of Labor Statistics projected positions for management analysts to grow by 14 percent from 2014 through 2024. While many are enlisted from outside a company to offer their recommendations, they may also work in-house, developing their plans over time.
The work of a management analyst centers on identifying and solving the problems that are holding an organization back from achieving its full potential. They accomplish this in part by considering data, such as an organization’s earnings and expenses. However, analysts also take into account qualitative factors, including input from workers and managers and the business culture as a whole.
Therefore, management analysts and consultants must be skilled at collecting and interpreting quantitative information while also remaining thoroughly engaged in the realities of how organizations run. Success in this field depends on being able to draw fresh insights from the numbers and communicate actionable strategies to stakeholders. Every business has its own challenges, and it takes a versatile expert to find the most direct way to capture opportunities for improvement.
Businesses of all kinds have come to embrace the value of collecting, monitoring and analyzing big data for seizing opportunities and solving problems. However, each organization has individualized needs for particular types of data and specific ways of making use of it. Data architecture is the field concerned with creating rules, procedures and models to determine what information IT systems gather and how it is processed.
Architects discover the best means of collecting, storing and managing big data. The mass of information passes through multiple layers based on the models established by the architect, yielding details that are of interest to various departments at different points in the business cycle. With the proper systems and applications in place, data gets to the right stakeholders in a timely, accessible manner.
In addition, a big-data architect performs duties like:
- Combining data from multiple silos to speed routine processes, predict problems or improve the company’s understanding of its customers
- Keeping data systems functioning efficiently
- Optimizing the system architecture to add data sources and meet analytics requests faster
- Adding new systems to established ones
The data architect collaborates with executives and IT professionals alike to build a system that’s precisely tailored to the needs of the company. As the demands evolve, the architecture shifts as well, making this expert a vital part of planning for the future of how a company collects and uses big data.
As consumers have grown accustomed to speedy service from e-commerce websites, businesses have in turn become increasingly reliant on logistics experts to stay competitive. These individuals play an indispensable role in the functioning of a business, considering every step necessary to take a product from the supplier to the purchaser and looking for any chance to streamline the process. This work involves close attention to the demands of customers and also a deep, quantitatively based understanding of how the supply chain functions.
Logisticians must have the analytical skills to coordinate and track the path taken by each shipment, improving the organization’s methods and execution over time. These duties require extensive attention to statistics, databases and spreadsheets. Advanced training in data analysis prepares logistics professionals to pick out problems in the supply chain and find effective solutions, improving the experiences of customers and the bottom line for the company.
These professionals draw on advanced quantitative methods to solve business problems. Collecting, structuring and visualizing data, they bring together a wide range of expertise in fields like statistics and software engineering to establish improved methods for handling data. They may be tasked with many different responsibilities based on the unique needs of the organization, such as:
- Developing efficient methods for data gathering and storage
- Creating algorithms and computer programs to catch meaningful patterns in the information
- Locating anomalies in data that could interfere with analysis and scrubbing them
- Reworking information into a format decision-makers can easily understand and apply to the business strategy
In heavily data-driven organizations, these experts may play a particularly crucial role. For a company that bases its strategic planning on using its huge stores of information for machine learning and predictive analytics, a data scientist can take a leading role in setting and executing the agenda. In these cases, it’s especially vital to couple advanced abilities in science and mathematics with a strong awareness of the real-world business applications for these principles.
Preparing for a career powered by data
Big data has become the cornerstone of how many businesses track the needs of their customers while planning for long-term expansion. Earning an online master’s degree in data analytics from Villanova University may be the first step in seizing this chance to take a leadership role. Visit the program page to find out how an education in applying big data to everyday business questions can prepare you to move forward in your career.