Python, Gravitational Waves, and Data Science: An Interview with Dr. Abhishek Parida

The newest program to be added at iCLA, Data Science has been an essential addition to the liberal arts curriculum. Read our Faculty Spotlight Interview with Dr. Abhishek Parida.

iCLA's Data Science Professor, Dr. Abhishek Parida

Dr. Parida’s Data Science courses range from introductory courses specially designed for liberal arts students to in-depth courses for students who are prospective Data Engineers. With the ever-increasing influx of data generated by users around the world, demand for Data Scientists is rising in industries, organizations, and government entities. Being able to understand, analyze, visualize, and summarize data can pave the way for a meaningful and lucrative career for students.

iCLA's Data Science Lab

Data Science Course List:
・Introduction to Computer Science
・Introduction to Python Programming
・Coding Bootcamp: Applied Probability and Statistics
・Coding Bootcamp: Python
・Machine Learning
・Mathematics for Data Science

We asked Dr. Parida about his specialization and advice for students studying Data Science:

Q: What sparked your passion for Data Science?

I have a Bachelor’s degree in Computer Science, after which I got a job as a software test engineer. Then after working for about a year, I wanted to study General Relativity. So slowly, I moved into Physics. First, I did a Masters in Physics and then pursued a Ph.D. So during my Ph.D., I worked with Gravitational Wave data, which is convoluted. It’s not simple like sales data or housing data. And it was during this time that I cared for and valued data in general. Since I had a background in Programming, my supervisors advised me to go into Gravitational Wave Data Science. And that’s how I could channel into Data Science. Also, I think that this way, I can showcase my skills which are programming and working with data.

Q: Please tell me what your area of specialization is.

I worked mainly on Gravitational Wave data. Gravitational Waves are exciting predictions of Einstein’s General Relativity and are produced by the coalescence of Astrophysical bodies. We have stochastic gravitational waves of Astrophysical origin due to many unresolved sources and stochastic gravitational waves of Cosmological origin. Stochastic is a fancy, technical term for random. My work was to separate these two components from the LIGO (Laser Interferometer Gravitational-Wave Observatory) data. I worked with LIGO collaborators on pipelines for signal processing, parameter estimation, and Sky map making. We use Python and Matlab as our primary languages.

LIGO Observatory
Laser Interferometer Gravitational-Wave Observatory (LIGO) Hanford Observatory

Q: What do you find most enjoyable about your field of study?

If you’re talking about programming, it teaches you how to think, not what to think, which is very important. Using programming, you can practically solve any task. For example, the conditionals – how to use Booleans and make a conditional. The if-else blocks – are used to create a decision or rules. Loops – if you want to repeat something. Once you know, you can use them to simulate your own experiment, which is otherwise difficult to do. What you need for writing a code is a good laptop and internet connection; that’s it. It is the sheer joy of making things; coding is all about making things that are useful for other people.

Q: What is your message to students who might be intimidated to start studying Data Science?

It takes a considerable amount of time to understand the processes involved in the Data Science domain. But if one is determined, it is their responsibility to work on those hurdles. Having said that, you don’t have to know everything from the beginning. You evolve as you do many projects. One should first focus on the programming language(s), e.g., Python. There are many in-built modules in Python that one has to simply import and start using it. If you talk about Machine Learning algorithms, say linear regression, support vector machine, or a decision tree, you don’t have to write your own code from scratch. You need to understand how they work, and you need to apply them. So, it is intimidating to understand the concept, but when it comes to implementation, it is not that challenging. Python and R are two programming languages that are open source and are extensively used in many Data Science projects. But, my personal preference is Python because it has a big ecosystem. Apart from leveraging various statistical packages, one can also use Python to build a website or a desktop app or a game, and many more. Python has many applications, not only Machine or Data Science.

Q: Do you recommend students have a foundation before they take your courses or can they begin from zero?

It is always good, at least if you know why you want to pursue Data Science. But definitely, we begin from zeroth level with fundamental courses like Python in the curriculum that we have made.

Q: And for those students who love Data Science, what is your tip for pursuing a deeper understanding of the field?

I would suggest doing as many projects as you can. The traditional approach is something like you complete an entire book on statistics, and then you work on projects. While you’re working on a project, it is likely that you will forget some concepts and may have to refer to them again. But we need to change these tactics as things are quickly paced. One can start working on some projects when they have a fair amount of knowledge. Suppose you are studying Python; if you are done with the basics, you can start thinking about how to build a website or a dashboard. When you’re doing a project, you’ll face hurdles; that way, you’ll refine your study more. The more projects you have under your belt, the better. For Data Science, do some exploratory data analysis, and do as many projects as you can. You will get stuck, no doubt, and that is how you learn. This feedback learning is what is required.

Q: Why is it important for students to learn about Data Science?

Organizations need Data Science, but there’s a huge gap. There is a sudden growth in data because of good technology. Data is exponentially increasing. The data collected in the last five or six years alone is bigger (in volume) than the data of the entire humanity in the last hundred or thousand years. It has already created a shortage of people, so one can contribute or work if they have the necessary skills. It is an exciting opportunity to be in Data Science.

To explore more of Dr. Parida’s research, click here.

Dr. Abhishek Parida’s Faculty Profile