Exclusive Interview: Ajay Bhargava, TCS on the Ideal Analytics Curriculum at Graduate-level
We discuss the differences between analytics and Big Data, the evolution of expectations from data science, important qualities desired in data scientists, ideal curriculum for Analytics focused programs, advice and more.
He has frequently spoken at industry conferences, authored whitepapers, and has driven thought leadership in the Data industry. In addition, he has actively taught (Analytics, Database Design, and Data Mining etc.) at The University of Texas, Austin, and College of Engineering, Pune, and mentors high school entrepreneurs for global competitions.
Ajay holds an M.S. in Computer Science and M.S. in Aerospace Engineering from The University of Texas at Arlington. He obtained his B.Tech in Aeronautical Engineering from Indian Institute of Technology, Mumbai in 1984.
First part of interview.
Here is second part of my interview with him:
Anmol Rajpurohit: Q5. What is the importance of having a Data Governance model? What approach do you recommend for setting up Data Governance model?
Ajay Bhargava: Having a Data Governance program increases the odds of success in any data initiative. The program must be established at a strategic, tactical, and operational level. The program must encompass people, process, technology, and data dimensions of the program. The involvement of both IT and Business is crucial for a timely, high-quality, within budget delivery of initiatives.
The key is to plan and architect and design for the future, but implement and execute in the near term, starting with small initiatives. Key is to continuously deliver value to business, while seeding an analytics-driven culture in every aspects of business. A strong data governance program provides a rich career path for data science teams, (data, tools, and analysis) synergies across different lines of business, and allows IT to deliver on multiple, sometimes competing objectives by various business initiatives.
AR: Q6. What are the key differences between Analytics and Big Data? How can one assess if the solution requires Analytics or Big Data or both (or none)?
- Achieve business SLAs that are not possible to be met by traditional environment e.g. speeding up ETL in a data warehouse environment
- Introduce new capabilities, such as leveraging unstructured data for fraud detection, sentiment analysis, real-time triaging of tweets, and enterprise search across intranet and internet etc.
Most of these initiatives start as a proof of concept. Many a times, a big data platform is introduced (as a proof of technology), along with specific use cases (proof of value) to lay the foundation and enable value creation for the future.
AR: Q7. How do you think the expectations from Data Science have evolved over time? Where do you see them headed in the future?
AB: In the eighties and nineties, the focus was more on decision support systems to make better informed decisions from structured data in relational databases and mainframe systems. The emphasis was more on computing quicker and less on the analysis of data.
AR: Q8. What key qualities do you look for when interviewing for Data Science related positions on your team?
AB: To me, a person’s attitude, willingness to learn, and analytical techniques used to improve one’s learning are pretty important to look for in a candidate. In today’s world,
AR: Q9. Based on your experience of Analytics field from industry as well as academia perspective, what do you believe should be the focus areas of Analytics education in universities in order to prepare students for the real-life Analytics projects?
I believe that a 2-year graduate program that not only dives into data management, applied statistical and machine learning techniques, practicum with real world industry data, but also has some elective courses that are industry domain specific added to the mix, is required to prepare students for real world customer issues. A real symbiotic, partnership (to create a pipeline) between academia and industry is absolutely essential.
In addition, introducing concepts and career options, and spotting and grooming such talents as early as high school level, is a must for any company, industry, or for that matter, country to maintain a competitive edge.
AR: Q10. What is the best advice you have got in your career?
AB: I once read somewhere:
“Pursue and excel in what you love, and get someone else to pay you for it” :-)
AB: I recently read “Calculus – Early Transcendentals” by James Stewart. It explains calculus from multiple angles. Feel like taking calculus all over again from the eyes of this textbook.
When I am not working, I like to travel with my family and meet folks from different cultures, and when possible, watch some college football and international tennis.
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