In the often quoted article titled ‘Data Scientist: The Sexiest Job of the 21st Century’, published in Harvard Business Review in 2012, authors Thomas H. Davenport and DJ Patil highlighted the emerging importance of Data Science, and its Practitioners. “Data Scientist… is a high-ranking professional with the training and curiosity to make discoveries in the world of big data.” In the ensuing decade, Data Science has become a central part of every company’s decision making. A recent Fortune Business Insights report titled Big Data Analytics Market 2021-2028 said that global big data analytics market is estimated to grow at a CAGR of 13.2% during the 2021-2028 period to become worth USD 549.73 billion in 2028.
So, in simple terms, what is Data Science, what is the job of a Data Scientist and how does it relate to business management. Understanding this will help MBA aspirants and MBAs take the first steps to become professionals in this domain. In this exclusive MBAUniverse.com column, Dr. Satyam Mukherjee, Faculty at SME, Shiv Nadar University has succinctly explained this Data Science domain and its associated areas like Machine Learning. Dr. Mukherjeeis a subject matter expert with Phd from the IIT Madras and has taught at leading B-schools including IIM Udaipur. His research interests include network science, data analytics, open-source collaboration, and sports analytics.This column is not just useful for management students, but also faculty and academic leadership as they align their curriculum and pedagogy to digital business trends. Column by Shiv Nadar Faculty Dr. Mukherjee follows:
Data Science and Machine Learning in Business
Dr. Satyam Mukherjee
Associate Professor, Department of Decision Sciences, Operations Management, and Information Systems, School of Management and Entrepreneurship, Shiv Nadar University
The proliferation of Information Technology across different business processes has resulted in large volumes of data in the digital form being available for further analysis. Over a period, this data is becoming large across many industries. The burst of such complex business data has resulted in an astronomical demand for data science experts and machine learning professionals.
So, what is Data Science? How is it different from Machine Learning?
Data Science is the practice of the collection of data and drawing relevant conclusions. In business parlance, Data Science serves as a source of hidden insight.
Machine Learning involves the preparation, and training of various algorithms to meet the business goals set forth by data scientists. A Data Scientist brings up interesting and relevant questions, the answers of which are addressed by various Machine Learning algorithms. Data Science thus acts as a canopy housing Machine Learning tools and other related disciplines.
Data Science applications range from Anomaly Detection, Predictive Modelling, Sentiment Analysis, Sports Analytics, and Recommender Systems. Even the recent emergence of autonomous systems comes within the premise of data science. Self-driven cars operate on the well-known Bayes’ Theorem, often touted as the “Holy Grail of Data Science” by Data Scientists. Further, consider the Retail Industry in which Advertisers employ Data Science and tools of Machine Learning to statistically predict demand and sales. Tools of Machine Learning are also effective in detecting anomalies in transactions and customer activities. Sentiment Analysis using Machine Learning tools such as Natural Language Processing is commonly used in Twitter text analysis by Data Scientists.
So, how does one integrate Data Science and Machine Learning for a business problem? First, one must collect the required data, preferably large data for greater statistical power. Services such as Google Cloud AI are commonly utilized by beginners in Data Science for analyzing a big dataset. Seasoned Data Scientists who are fluent in coding, such as Python or Julia, possessing expertise in Machine Learning usually develop their codes and statistical models.
To summarize, given the inertia in various industries, integration of Data Science and Machine Learning tools will make business firms function differently soon.
About the Author
Dr. Satyam Mukherjee completed his doctoral degree from the Indian Institute of Technology, Madras. He also holds a bachelor's degree in Physics from Presidency College, University of Calcutta, and a master's degree in Physics from the University of Pune. His research interests include network science, data analytics, open-source collaboration, and sports analytics.
Prior to joining Shiv Nadar University, Delhi NCR, Dr. Mukherjee was a faculty member at the Indian Institute of Management Udaipur. He has previously worked with Kellogg School of Management and Northwestern Institute on Complex Systems (NICO), Northwestern University Evanston, USA as a Postdoctoral Fellow.
Dr. Mukherjee's works have appeared in many reputed journals including Science, PNAS, IISE Transactions, Science Advances, Nature Human Behavior, Nature Scientific Reports, Interface, and Physical Review E. His work has also been featured by The Economist, Times of India, The Hindu, MIT Technology Review, and The Wall Street Journal.
School of Management & Entrepreneurship (SME) at Delhi NCR based Shiv Nadar University (SNU) is developing a New Age MBA that is geared for the Digital Economy. Led by Dean Dr Bibek Banerjee, former IIM Ahmedabad Faculty and Director General of IMT Ghaziabad Group, SME at SNU has taken many bold steps – for instance 70% of its curriculum is focused on how business is done (or going to be done) in the new digital world. The school offers a comprehensive suite of degree programs in management including PhD and Executive PhD, Bachelor of Management Studies, MBA Executive and the 2-year MBA. Shiv Nadar MBA Admission 2022 is currently open. Apply Now
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