| |9 July 2020HIGHERReviewGraduates in near future would be expected to have some knowledge in analytics to make their job roles more efficient and optimizedbe 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, SASd) 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.
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