Answer: Business Intelligence, Data Analytics, and Data Science programs address three related but overlapping specializations within the larger field of analytics. These program specializations are distinguished by differences in their curricular focus. Data Analytics programs are grounded in the foundational elements of analytics, including advanced mathematics and statistics, and data mining. Data Science programs delve into the more technical aspects of computer science, computer programming, and computer engineering. Business Intelligence programs are defined by their focus on the IT systems that process analytics data.
Analytics is an interdisciplinary field that incorporates training in four main areas: advanced mathematics and statistics; computer science and programming; database technologies; and enterprise decision management. Broadly speaking, analytics involves learning how to use the big data processing capabilities of modern IT systems to store, sort, and analyze relevant data. The goal is to provide scientifically based, quantitative solutions to a range of complex problems.
The field of analytics is broken down into three primary types of degree programs: Data Analytics, Data Science, and Business Intelligence. While it is useful to sort programs into these categories, there is considerable overlap between the three different program types. There is also no standardized naming convention or curricula standards for degree programs in analytics. The best way to determine each program’s focus is to carefully evaluate its curriculum, including required and elective courses. In general, Data Analytics programs are the broadest and most diverse of the three analytics degree programs, with courses in applied mathematics, statistics, computer science, and IT systems. While Data Science programs cover these areas as well, they typically require more advanced coursework in computer science, programming, and engineering. Business Intelligence programs are the most IT focused of the analytics program specializations, with coursework in data warehousing, database management, and dashboarding technologies.
Data Analytics programs encompass the foundational curriculum in analytics, the curriculum in which other analytics specializations are grounded. These programs have a broader scope than other analytics specializations. Students in Data Analytics programs learn to use advanced mathematics and computer programming to test hypotheses, identify trends, and answer questions quantitatively. This involves utilizing spreadsheets and databases to aggregate and sort unstructured data, and using statistical modeling techniques, probability matrixes, and algorithms to analyze different kinds of data. It also includes learning data visualization techniques and other methods for presenting analytics results to others who may or may not have a technical background.
In addition to foundational coursework in applied mathematics and statistical modeling, Data Analytics students learn about data mining, a process by which relevant data is identified, extracted, sorted, cleansed, interpreted, and prepared for presentation. Data Analytics programs may be housed within schools or departments of mathematics, statistics, computer science, or information technology. A typical Data Analytics curriculum includes coursework in the following areas: data mining; advanced quantitative methods; predictive analytics and forecasting models; and big data visualization.
Some schools offer specializations within their Data Analytics programs. These specializations typically focus on the use of analytics in specific fields, like healthcare and government policy. One of the more common Data Analytics specializations is Business Analytics. Business Analytics programs are tailored to fit the specific demands of the business world. They target the use of descriptive, predictive, and prescriptive modeling in areas like risk assessment and mitigation, performance reporting, efficiency optimization, supply-chain management, executive decision-making, and other business-related concerns. These programs may be somewhat less technical in nature than pure Data Analytics programs, and they are often housed within schools of business or schools of professional studies. Typical Business Analytics subject areas include the following: marketing and consumer behavior analytics; web and social media analytics; financial accounting analytics; and organizational communication skills.
For a more information, see our FAQ: Data Analytics versus Business Analytics Programs.
It is important to emphasize that Data Analytics programs can include coursework in a wide range of potential analytics applications, including bioinformatics, data journalism, demographics modeling, and sports management. Many programs offer electives in one or more of these and other areas, including business and marketing analytics.
Data Science programs are somewhat more technical than Data Analytics programs, with more coursework dedicated to computer science, computer programming, and computer engineering. Data Science students learn to employ many of the same tools used in Data Analytics, including statistical modeling, advanced mathematics, algorithmic programming, and big data systems. Data Science curricula also address many of the same chief concerns as Data Analytics curricula, including the various processes for collecting, sorting, and interpreting empirical data in order to identify meaningful trends, correlations, and causations. However, Data Science programs delve into experimental analytics applications that go beyond what is typical in a Data Analytics program. This includes learning how to work with the largest, most complex datasets, and writing computer code that can handle the unique challenges of structuring and analyzing these datasets.
As a result, Data Science programs generally put a stronger emphasis on computer science. This includes learning to code in languages like R, Python, SAS, and Hadoop. While Data Science programs do include coursework in data mining, data modeling, and data presentation techniques, they also incorporate advanced coursework in the following areas: applied machine learning; artificial intelligence; cloud computing; regression and time series analysis; and software engineering. As with other specializations in analytics, Data Science programs may have different points of emphasis and concentrations.
A Business Intelligence (BI) curriculum is characterized by a clearly defined focus on the IT systems that are used to facilitate the flow of analytics information within an organization. These include advanced databases, data warehousing technologies, and executive dashboard platforms, which are tools that provide non-technical members of an organization access to metrics generated by data analysts.
BI programs typically include foundational coursework in data mining, predictive modeling, and analytics programming. However, these programs generally feature less coursework in advanced mathematics and computer programming compared to Data Analytics and Data Science programs. Instead, BI programs emphasize a deep understanding of business technologies, their architecture and design, and how these technologies are integrated with other IT systems. BI programs may be housed in schools of engineering, computer science, or information technology, and typically include coursework in the following areas: database design and management; big data systems; prescriptive analytics; decision support and dashboarding; and IT security and management.
Choosing the right analytics program can be challenging. Analytics programs exist on a continuum, with Data Analytics in the middle, and Data Science and Business Intelligence on each side. Data Analytics programs are typically the broadest with Data Science and Business Intelligence programs being more specialized. In general, Data Science programs required more computer science related coursework, while Business Intelligence programs require more IT related coursework.
Some programs have set requirements that more clearly define them as Data Analytics, Data Science, or Business Intelligence programs. However, these programs may also offer electives that allow students to combine elements of the three different programs. For example, Data Analytics programs may offer electives in areas like machine learning (Data Science), strategic marketing (Business Analytics), and/or data warehousing (Business Intelligence).
Prospective applicants should closely examine a program’s core requirements and elective options to determine how closely it aligns with their specific needs, interests, and career goals. OnlineEducation.com evaluates online master’s in analytics programs and categorizes them as Master’s in Data Analytics, Master’s in Data Science, and Master’s in Business Intelligence based on their curricula.