Master of Science in Business Analytics

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A Master of Science in Business Analytics (MSBA) is an interdisciplinary STEM graduate professional degree that blends concepts from data science, computer science, statistics, business intelligence, and information theory geared towards commercial applications. Students generally come from a variety of backgrounds including computer science, engineering, mathematics, economics, and business. University programs mandate coding proficiency in at least one language. The languages most commonly used include R, Python, SAS, and SQL. Applicants generally have technical proficiency before starting the program. Analytics concentrations in MBA programs are less technical and focus on developing working knowledge of statistical applications rather than proficiency. [1]

Science, Technology, Engineering and Mathematics (STEM), previously Science, Math, Engineering and Technology (SMET), is a term used to group together these academic disciplines. This term is typically used when addressing education policy and curriculum choices in schools to improve competitiveness in science and technology development. It has implications for workforce development, national security concerns and immigration policy. The science in STEM typically refers to two out of the three major branches of science: natural sciences, including biology, physics, and chemistry, and formal sciences, of which mathematics is an example, along with logic and statistics; the third major branch of science, social sciences, including psychology, sociology, and political science, are categorized separately from the other two branches of science, and are instead grouped together with humanities and arts to form another counterpart acronym named HASS - Humanities, Arts, and Social Sciences. In the United States education system, in elementary, middle, and high schools, the term science refers primarily to the natural sciences, with mathematics being a standalone subject, and the social sciences are combined with the humanities under the umbrella term social studies.

A master's degree is an academic degree awarded by universities or colleges upon completion of a course of study demonstrating mastery or a high-order overview of a specific field of study or area of professional practice. A master's degree normally requires previous study at the bachelor's level, either as a separate degree or as part of an integrated course. Within the area studied, master's graduates are expected to possess advanced knowledge of a specialized body of theoretical and applied topics; high order skills in analysis, critical evaluation, or professional application; and the ability to solve complex problems and think rigorously and independently.

A professional degree, formerly known in the US as a first professional degree, is a degree that prepares someone to work in a particular profession, often, but not always, meeting the academic requirements for licensure or accreditation. Professional degrees may be either graduate or undergraduate entry, depending on the profession concerned and the country, and may be classified as bachelor's, master's or doctoral degrees. For a variety of reasons, professional degrees may bear the name of a different level of qualification from their classification in qualifications frameworks, e.g. some UK professional degrees are named bachelor's but are at master's level, while some Australian and Canadian professional degrees have the name "doctor" but are classified as master's or bachelor's degrees.

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Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. [1] Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Business analytics can be used to leverage prescriptive analytics towards automation.

Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.

Data facts represented for handling

Data is a set of values of subjects with respect to qualitative or quantitative variables.

Statistics Study of the collection, analysis, interpretation, and presentation of data

Statistics is the discipline that concerns the collection, organization, displaying, analysis, interpretation and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics.

Origin

The MSBA was a response to the increasing need of complex data analysis beyond traditional use of spreadsheets such as Microsoft Excel. Since 2001, the increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources) has created a vacuum for talent. [2] Harvard Business Review noted: “Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization,” the article says. “While those are important breakthroughs, at least as important are the people with the skill set (and the mind-set) to put them to good use. On this front, demand has raced ahead of supply. Indeed, the shortage of data scientists is becoming a serious constraint in some sectors.” [3]

Volume Quantity of three-dimensional space

Volume is the quantity of three-dimensional space enclosed by a closed surface, for example, the space that a substance or shape occupies or contains. Volume is often quantified numerically using the SI derived unit, the cubic metre. The volume of a container is generally understood to be the capacity of the container; i. e., the amount of fluid that the container could hold, rather than the amount of space the container itself displaces. Three dimensional mathematical shapes are also assigned volumes. Volumes of some simple shapes, such as regular, straight-edged, and circular shapes can be easily calculated using arithmetic formulas. Volumes of complicated shapes can be calculated with integral calculus if a formula exists for the shape's boundary. One-dimensional figures and two-dimensional shapes are assigned zero volume in the three-dimensional space.

Velocity rate of change of the position of an object as a function of time, and the direction of that change

The velocity of an object is the rate of change of its position with respect to a frame of reference, and is a function of time. Velocity is equivalent to a specification of an object's speed and direction of motion. Velocity is a fundamental concept in kinematics, the branch of classical mechanics that describes the motion of bodies.

<i>Harvard Business Review</i> journal

Harvard Business Review (HBR) is a general management magazine published by Harvard Business Publishing, a wholly owned subsidiary of Harvard University. HBR is published six times a year and is headquartered in Brighton, Massachusetts.

See also

Big data Information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value

"Big data" is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional usual software to process within an acceptable time and value.

Machine learning Scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Data mining computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems; interdisciplinary subfield of computer science

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Related Research Articles

Computer science Study of the theoretical foundations of information and computation

Computer science is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate, store, and communicate digital information. A computer scientist studies the theory of computation and the practice of designing software systems.

Business intelligence (BI) comprise the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance.

Analytics discovery, interpretation, and communication of meaningful patterns in data

Analytics is the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.

SAS (software) statistical software

SAS is a statistical software suite developed by SAS Institute for advanced analytics, multivariate analysis, business intelligence, criminal investigation, data management, and predictive analytics.

A Business Intelligence Competency Center (BICC) is a cross-functional organizational team that has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of Business Intelligence (BI) across an organization.

The Tepper School of Business is the business school of Carnegie Mellon University. It is located in the university’s 140-acre (0.57 km2) campus in Pittsburgh, Pennsylvania, US.

Data science A field of study involving the use of computers to perform statistics

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".

Rayid Ghani was the Director of the Center for Data Science and Public Policy, Research Associate Professor in the Department of Computer Science, and a Senior Fellow at the Harris School of Public Policy at the University of Chicago. He was also the co-founder of Edgeflip, an analytics startup that grew out of the Obama 2012 Campaign, focused on social media products for non-profits, advocacy groups, and charities. Recently, it was announced that he will be leaving the University of Chicago and joining Carnegie Mellon University's School of Computer Science and Heinz College of Information Systems and Public Policy.

Morten Middelfart is a Danish-born, American entrepreneur, inventor, and technologist. He is best known for designing the TARGIT software for business intelligence and analytics. Middelfart is currently the founder/Chief Data Scientist of Lumina Analytics, CIO of Genomic Expression and founder of Social Quant. These organizations implement the data analytics techniques Middelfart has developed throughout his career. With seven U.S. patents for his inventions in business intelligence and analytics software, Middelfart holds the most patents of any Danish person working in software. Middelfart holds an MBA from Henley Management College, a PhD from Rushmore University, and a PhD from Aalborg University.

In the fields of information technology (IT) and systems management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. In 2014, Gartner predicted its use might increase revenue or reduce costs. By 2017, it predicted that 15% of enterprises will use IT operations analytics technologies.

Gregory Piatetsky-Shapiro data scientist and co-founder of KDD conferences and ACM SIGKDD association

Gregory I. Piatetsky-Shapiro is a data scientist and the co-founder of the KDD conferences, and co-founder and past chair of the Association for Computing Machinery SIGKDD group for Knowledge Discovery, Data Mining and Data Science. He is the founder and president of KDnuggets, a discussion and learning website for Business Analytics, Data Mining and Data Science.

Continuous analytics is a data science process that abandons ETLs and complex batch data pipelines in favor of cloud-native and microservices paradigms. Continuous data processing enables realtime interactions and immediate insights with fewer resources.

Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, and insight discovery. Although in a dystopian vision of AI applications, intelligent machines may take away jobs of humans and cause social and ethical issues, industry in general holds a more positive view of AI and sees this transformation of economy unstoppable and expects huge business opportunities in this process.

Guided analytics is sub-field at the interface of visual analytics and predictive analytics focused on the development of interactive visual interfaces for business intelligence applications. Such interactive applications serves the analyst to take important decisions by easily extracting information from the data.

References

  1. ↑ Allen, Nathan (2014-12-13). "Big Data Drawing Big Student Enrollments". Poets and Quants. Retrieved 2016-09-03.
  2. ↑ Baron, Ethan (2016-01-18). "Business Analytics Master's At 100 Top B-Schools". Poets and Quants. Retrieved 2016-09-03.
  3. ↑ "Data Scientist: The Sexiest Job of the 21st Century" . Retrieved 2016-09-03.
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