- Summary of the episode and podcast:
- FAQ: Key differences in the American and Indian education systems?
- FAQ: Practical differences in Data Science education between US and Indian institutions at the MS/MBA level?
- FAQ: Choosing between Data Science, Business Analytics, or Computer Science, and understanding their work differences?
- FAQ: What separates Business Analytics from Data Science and Data Analytics?
- FAQ: Can non-tech background individuals successfully transition to technology or AI careers through STEM-specialized MS/MBA programs?
- FAQ: Job prospects after US degree in Data Analytics, AI, or ML, and post-graduation job market strategy?
- FAQ: Why are employers increasingly seeking management graduates with data literacy skills?
- FAQ: Advice for students/young professionals on showcasing their Data Science skills to employers?
- FAQ: How to choose the right MS in Data Science program among the many available universities?
Summary of the episode and podcast:
The Study Abroad Podcast is for anyone and everyone who is either on their study abroad journey or just considering it. This series shall provide invaluable insights to help you make informed decisions about your academic future and stay informed about the latest trends in international education.
The first episode of the Study Abroad Podcast by Great Learning, hosted by Shweta Gupta of the international education team! explores the wide array of avenues for study abroad aspirants who want to pursue MS and MBA in the United States. In this episode, Shweta speaks to Dr. Abhinanda Sarkar, Director of Academics, at Great Learning. They touch upon the rising demand of STEM specialization at top universities, which are the most in-demand specializations when it comes to MS in USA or MBA in USA and a lot more.
FAQ: Key differences in the American and Indian education systems?
Shweta: Last year, about 82,000 visas were allocated for Indian students to study in the US. That’s one of the highest numbers in India, I believe. So, if I ask you, what are the key differentiators between these two education systems, that is, India and the USA?
Dr. Sarkar: Initially, I faced challenges in comprehending the full scope and practical applications of my work across various industries. However, I realized, reinforced by my teachers’ guidance, that my foundational knowledge in mathematics and statistics was robust. This strong base enabled me to self-educate and learn from professors and industry experts about practical applications, coding, and implementation.
To address the question precisely, the educational approach in India excels in establishing a solid understanding of fundamental concepts. In contrast, international exposure, particularly in the U.S., offers a broader, more interconnected perspective. This combination is ideal: it starts with a firm grasp of basic principles, followed by exposure to diverse applications and a widened worldview. This blend transforms the global landscape into a realm of limitless opportunities.
FAQ: Practical differences in Data Science education between US and Indian institutions at the MS/MBA level?
Shweta: It has been said that the practical part of education is better in US education versus in India. How true is that?
Dr. Sarkar: I believe that if we can address certain challenges in regions like India or Africa, for instance, we can essentially solve them for the world. This view challenges the outdated belief that innovation is solely confined to the U.S. or other developed nations. My global experiences, particularly in understanding the realities of India and the technological advancements in the U.S., have been valuable.
In my perspective, there are two primary approaches to integrating technology and business. The first is to focus on technological skills like data analysis and coding, and then explore their applications in business. The second approach is to identify entrepreneurial opportunities first and then learn the necessary skills to bring these ideas to fruition.
The rise of MBA specializations in STEM fields, such as business and data analytics, can be attributed to a few key trends. The first is the global increase in data generation across various industries, a phenomenon I refer to as ‘datafication’, which creates numerous opportunities for extracting value. The second trend is the significant advancement in computational power, as seen in technologies like ChatGPT, which allows for rapid processing and impressive results. These developments have opened opportunities not only in the technology sector but also for professionals from diverse backgrounds, enabling them to enhance their respective fields with new tools and insights.
FAQ: Choosing between Data Science, Business Analytics, or Computer Science, and understanding their work differences?
Shweta: When deciding between specialized fields like business analytics, data analytics, data science, AI, ML, and broader degrees like computer science, it’s crucial to consider the distinct career opportunities each path offers.
Dr. Sarkar: I believe that while strengthening fundamentals through a computer science degree is beneficial, it requires considerable time investment, a privilege I had. For those with time constraints, starting with fields like data science, artificial intelligence, and machine learning offers an effective shortcut.
I advocate this approach because, with the right teaching, case studies, and projects, the lack of certain foundational knowledge won’t be an impediment. Just as one doesn’t need to know how to build a car to drive it, we can focus on teaching individuals to excel in these advanced fields without overburdening them with every underlying detail. This method allows for efficient learning and application in the rapidly evolving tech landscape.
FAQ: What separates Business Analytics from Data Science and Data Analytics?
Shweta:Business analytics focuses on using data to solve business problems and make informed decisions, while data science encompasses a broader range of data-related tasks, including data exploration, modeling, and algorithm development.
Dr. Sarkar: Starting with business analytics, I find it often revolves around spreadsheet-level work, yielding quick business results with limited data. As data volume increases, the focus shifts to understanding: How do you process it? How do you get it? How do you clean it? which involves more technological, especially computing skills.
Progressing to data science, the key addition is mathematical modeling, where the scientific aspect comes into play, allowing for concrete conclusions. Moving further towards AI, the development is about creating applications that mimic human behavior, like understanding and generating speech or images.
FAQ: Can non-tech background individuals successfully transition to technology or AI careers through STEM-specialized MS/MBA programs?
Shweta: I would like to request you to elaborate a little more because often we get quite a few students with the fear that I am very much interested in transitioning my career to technology or, like I mentioned, artificial intelligence, which is booming at this point of time. But then, how do I go about it?
Dr. Sarkar: To address data problems effectively, adopting the mindset of a data scientist is more crucial than being one. In my view, transitioning into roles like a data scientist or machine learning engineer does involve working with computers, which often intimidates people.
However, it’s not necessary to start with complex concepts like object-oriented programming to perform tasks like writing Python code for cancer prediction. While having such skills is advantageous, it’s not a prerequisite. For those who may not have these skills, there are accessible methods and tools that facilitate quicker learning and application in these fields.
FAQ: Job prospects after US degree in Data Analytics, AI, or ML, and post-graduation job market strategy?
Shweta: Would you be able to comment on the job opportunities post a degree in data analytics or artificial intelligence or machine learning globally?
Dr. Sarkar: What students want to do is participate in hackathons, do well in them, and therefore understand the domain well, but more importantly, get connected with the industry.
Especially in the U.S., it’s often more about the networks you build, the people you collaborate with, and the problems you solve, rather than just your degree. Creating a digital footprint through these programs, including hybrid ones, is crucial in this process.
So once a digital footprint is in place, and that could be through social media, that could be through hackathon presences, etc. Then what happens is that the industries you’re working with, the people you’ve contributed that footprint to, will reach out to you and say, we have this opportunity. Can you solve this problem? Or even, we have this job, do you want it?
FAQ: Why are employers increasingly seeking management graduates with data literacy skills?
Shweta: Data science jobs are in high demand because they provide valuable insights for solving business problems, and employers seek management graduates with data literacy for informed decision-making.
Dr. Sarkar: Core skills in data are always going to be in demand.
How do we make businesses sustainable? How do we forecast the weather? How do we do all these things? Irrespective of the particular application of data sitting in front of us, these skills will always be useful. If someone invests in it now, I do not think this will go out of fashion anytime soon.
FAQ: Advice for students/young professionals on showcasing their Data Science skills to employers?
Shweta: As an employer, I look for candidates with technical skills, problem-solving abilities, and effective communication. They should present a portfolio of projects and a commitment to learning and staying updated with evolving technologies.
Dr. Sarkar: From my perspective, entrepreneurs typically seek two qualities in potential hires. Firstly, they value a genuine interest in the problem and substantial knowledge of the specific industry, including its objectives and the technical and scientific culture within a STEM context. Secondly, companies often prefer candidates who demonstrate an end-to-end understanding of projects.
When faced with multiple resumes, my inclination is to favor individuals with some relevant experience. Communication skills are paramount in all these areas, and while MBA programs inherently focus on this, Master’s programs in other fields are increasingly emphasizing it too. Therefore, a candidate with such experience brings expertise and the aptitude to communicate effectively in the industry’s language.
FAQ: How to choose the right MS in Data Science program among the many available universities?
Shweta: With the proliferation of specialized programs in data science, AI, and ML across universities, choosing the right one can be daunting even when you’re certain about pursuing a master’s in data science.
Dr. Sarkar: In my experience as an academician, especially in this sector, I’ve found that curriculum can guide important decisions. The key aspect I look for is the emphasis on projects and project-based learning, which is active learning. I assess a curriculum based on the number of projects it includes and the extent of active learning it promotes. Our programs stand out in this regard, offering numerous hands-on projects, assignments, and capstone projects, with some programs featuring two capstone projects, one at the end of each year. I firmly believe that such a curriculum is instrumental in helping students achieve their career goals and aspirations.
