Role of Business Analytics in Higher Education Curriculum
Dr. Krishnadas Nanath, Faculty and Data Scientist, Middlesex University Dubai
Soon after the proclamation of Data Science & Analytics becoming one of the most sought professions of the 21st century in 2012, analytics became a lucrative field luring several professionals in this domain. With the drive of technological change (more computational power, easy storage, powerful tools) and the growth of piling colossal data in organizations waiting to be mined, analytics pervaded in the curriculum of several higher education programs. However, applications of Business Analytics (BA) is permeating all industry verticals (finance, marketing, HR Analytics, IT and others) today and hence making it vital to include Data Science and BA as core of several higher education programs than just optional modules.
According to an article published recently by a renowned magazine, annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will boost up by more than 30 percent in 2020. There are estimates for analytics capturing one-third of the Global IT market in near future. Glassdoor listed Data Science as the highest paying job in the year 2016, keeping the demand for analytics professional very lucid. While the demand is evident, the fact that these job positions remain open for a larger timeframe compared to other jobs demonstrates a supply shortage. Higher education institutions should play a critical role here to close this demand-supply dichotomy.
"Graduates in near future would be expected to have some knowledge in analytics to make their job roles more efficient and optimized"
Business Analytics in education should no longer be considered as a specialization or additional domain. Graduates in near future would be expected to have some knowledge in analytics to make their job roles more efficient and optimized. This vision of Data Science literacy can be accomplished only if analytics becomes the core of educational curriculum in colleges. Teaching, research and practice of analytics from multiple perspectives can be brought in only if more use cases in various domains are demonstrated to potential graduates. Analytics is slowly encompassing several elements of advanced technologies and there are several pointers to look at:
a) Google Analytics (marketing)
b) ERP and their visualizations like SAP, SAP Hana, Microsoft Dynamics (HR, Supply Chain, and Finance)
c) Advanced Machine Learning, AI, Deep Learning with Python, SAS
d) Visualization Tools like Tableau, Sisense, Power BI
e) Robust packages in R and Python, modules in SAS.
To fulfill a gap this wide, several professionals will need to be analytics ready and business/engineering programs will play a critical role. A good data analytics professional can be weighed across in three domains- Storytelling, Business Analytics and Data Engineering. Storytelling covers the business acumen perspective, making it important for a data scientist to have good domain knowledge. Many organizations are capturing plenty of information from several touch-points available. All this information makes sense only if they add business value with the help of use cases like predictions and classifications. A good storyteller would help mine this data and deliver meaningful knowledge that will add great business sense to organizations. Storytelling domain is particularly relevant for business graduates and hence makes a strong case of Data science education in business schools.
The second domain is the core analytics part that requires the knowledge of statistical methods and tools that will help achieve the business goal. Interesting aspect is that this domain is related to the storytelling aspect, as more knowledge of methods will help develop strong business use case of data mining making a professional a better storyteller. Therefore, students aspiring to become excellent analytics professionals must have a mathematical mindset to understand the execution and assumptions of algorithms involved. Third domain is the one where technical expertise is required for making use of advanced computing techniques (like big data and distributed storage) to deliver analytics execution with sheer efficiency. Technical and Computer Science related degrees should incorporate this domain to complete the third pillar of analytics.
A data science enthusiast after graduation could expect roles ranging from generating use cases for a given dataset to working on products that deliver data science reports. While some data science graduates can expect roles that require great statistical knowledge, others can also expect roles that demand IT and coding acumen. They could expect to work on tools like R, Python, SAS, Tableau, and others. Their daily jobs would be around three key areas- Data Extraction, Data Transformation (including algorithms) and Data Loading (Visualization and reports). Higher educational institutions should now consider including analytics as core component of the curriculum for not only preparing Data Science professionals but also encouraging students to think analytically in any job role. There is no question of analytics being a fad or hype; it's here to stay and become an integral part of all business domains.
According to an article published recently by a renowned magazine, annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will boost up by more than 30 percent in 2020. There are estimates for analytics capturing one-third of the Global IT market in near future. Glassdoor listed Data Science as the highest paying job in the year 2016, keeping the demand for analytics professional very lucid. While the demand is evident, the fact that these job positions remain open for a larger timeframe compared to other jobs demonstrates a supply shortage. Higher education institutions should play a critical role here to close this demand-supply dichotomy.
"Graduates in near future would be expected to have some knowledge in analytics to make their job roles more efficient and optimized"
Business Analytics in education should no longer be considered as a specialization or additional domain. Graduates in near future would be expected to have some knowledge in analytics to make their job roles more efficient and optimized. This vision of Data Science literacy can be accomplished only if analytics becomes the core of educational curriculum in colleges. Teaching, research and practice of analytics from multiple perspectives can be brought in only if more use cases in various domains are demonstrated to potential graduates. Analytics is slowly encompassing several elements of advanced technologies and there are several pointers to look at:
a) Google Analytics (marketing)
b) ERP and their visualizations like SAP, SAP Hana, Microsoft Dynamics (HR, Supply Chain, and Finance)
c) Advanced Machine Learning, AI, Deep Learning with Python, SAS
d) Visualization Tools like Tableau, Sisense, Power BI
e) Robust packages in R and Python, modules in SAS.
To fulfill a gap this wide, several professionals will need to be analytics ready and business/engineering programs will play a critical role. A good data analytics professional can be weighed across in three domains- Storytelling, Business Analytics and Data Engineering. Storytelling covers the business acumen perspective, making it important for a data scientist to have good domain knowledge. Many organizations are capturing plenty of information from several touch-points available. All this information makes sense only if they add business value with the help of use cases like predictions and classifications. A good storyteller would help mine this data and deliver meaningful knowledge that will add great business sense to organizations. Storytelling domain is particularly relevant for business graduates and hence makes a strong case of Data science education in business schools.
The second domain is the core analytics part that requires the knowledge of statistical methods and tools that will help achieve the business goal. Interesting aspect is that this domain is related to the storytelling aspect, as more knowledge of methods will help develop strong business use case of data mining making a professional a better storyteller. Therefore, students aspiring to become excellent analytics professionals must have a mathematical mindset to understand the execution and assumptions of algorithms involved. Third domain is the one where technical expertise is required for making use of advanced computing techniques (like big data and distributed storage) to deliver analytics execution with sheer efficiency. Technical and Computer Science related degrees should incorporate this domain to complete the third pillar of analytics.
A data science enthusiast after graduation could expect roles ranging from generating use cases for a given dataset to working on products that deliver data science reports. While some data science graduates can expect roles that require great statistical knowledge, others can also expect roles that demand IT and coding acumen. They could expect to work on tools like R, Python, SAS, Tableau, and others. Their daily jobs would be around three key areas- Data Extraction, Data Transformation (including algorithms) and Data Loading (Visualization and reports). Higher educational institutions should now consider including analytics as core component of the curriculum for not only preparing Data Science professionals but also encouraging students to think analytically in any job role. There is no question of analytics being a fad or hype; it's here to stay and become an integral part of all business domains.
