We thank Shruti Jadon from Juniper Networks inc for taking part in the Data Science Interview Series. This interview with Shruti specifically targets the beginners into Data Science; the dos and the don’ts they need to keep in mind. Shruti also shares some insights from her experience that will help you make the right decisions in your Data Science journey. She shared several insights, including:
- Her story of starting in Data Science
- Her best book recommendations
- The skills required to be a good Data Scientist
- What keeps her motivated
Machine Learning modeling is a small part of a data scientist’s job. You need the skills of a software engineer, data engineer, and a researcher as well.
–Shruti Jadon
To shift into a different data domain, some efforts are required but to become a good data scientist, the core concepts generally remain the same.
-Shruti Jadon
To become a leader in this field, one must know how to design the whole infrastructure so that the ML model can be part of the system.
–Shruti Jadon
How did you first get into data science?
I loved working in the applied mathematics domain. So, while doing my bachelor’s, I took a course on data mining which introduced me to various mathematical models for data classification. I got fascinated by how we can reduce labour work with the help of coding. To further explore the data science field, I decided to pursue my career in the data science field and enrolled in the MS program at UMass Amherst.
What are the key skills that you use every day as a data scientist, and how did you develop them?
I think, above most, to become a data scientist, you must love working with raw data. Machine Learning modelling is a small part of a data scientist’s job; apart from it, you need the skills of a software engineer, data engineer, and researcher. About developing these skills, most of them we learn by reading books, papers, and doing challenging projects, whereas some of them you learn from experience.
What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
One of the main problems we face is the data scarcity problem. At various times, we get unlabelled data, biased data, or fewer data. The intuitive solution to go around is to hire someone to label them, but I wish it was that easy. Labelling sometimes requires domain experts, and in cases such as medical images, it’s very cost-intensive to hire folks for labelling. As data scientists, we need to analyze the data and see if we can adopt some other techniques such as new architecture, domain adaptation, etc., and deliver the best possible model.
How is Machine Learning transforming the Networking industry?
Machine Learning is helping many data centres optimize the cost by predicting traffic, memory utilization, and detecting anomalies. This, in turn, saves a lot of money in businesses and even helps big tech companies reduce their carbon footprint.
How important is the domain knowledge of the business/industry you’re in as a data scientist, and how did you acquire it?
Having domain knowledge can help understand the business requirement in a better way, but it’s not something that can’t be acquired on the way. Even I have worked as a data scientist in different industries such as manufacturing, medical, e-commerce, and now in networking. To shift into a different data domain, some efforts are required but to become a good data scientist, the core concepts generally remain the same.
3 words that best summarize how you learned ML and data science?
- Mathematical understanding,
- curiosity,
- and a lot of hard work.
Books: Which books have helped you the most in your journey and why?
To begin my journey in the Machine Learning field, I mostly referred to my coursework at UMass Amherst such as “An Introduction to Statistical Learning: With Applications in R“, “Deep Learning by Aaron“, and “Machine Learning by Kevin Murphy“. These books helped me clear out the conceptual doubts and gave me the ability to follow through with research papers.
After my Masters’s and four years of industry experience with some research papers on the side, I have also written a book on “Hands-on one-shot learning using python”. It won’t be wrong if I say, writing blogs and papers also helped me become a better data scientist as I had to explain those concepts.
Courses: What courses/programs did you take that significantly contributed to advancing your career in data science?
I have obtained my Masters’s from the University of Massachusetts, Amherst, in 2018. The coursework at UMass provided me with deep insight into the Machine learning field in general.
What is the biggest improvement that you introduced in the last 12 months that has considerably improved your workflow?
I started studying in detail about large scale machine learning systems. Having knowledge of Machine Learning based modelling is just the beginning; to become a leader in this field, one must know how to design the whole infrastructure so that the ML model can be part of the system.
What advice would you give to someone who wants to get into data science today?
Data science is a vast field; it ranges from data visualization to modelling. Even in modelling, there are multiple fields, and then there are various data domains. One can’t be an expert in all of it. I will suggest people who are about to enter the data science field read about everything but ensure that you are an expert in some of these fields and keep updating yourself by reading books, papers and attending conferences.
What inspires you about working in Data Science?
Data science is a new field, and various applications are yet to be explored. It’s dynamic, and every year, new research opens up new possibilities. This field is a work in progress, and just the thought of contributing to it while it’s building keeps me motivated.
