Manaswita Tyagi
Featuring Manaswita Tyagi for our Saturday shoutout initiative. Currently an Associate Software Engineer Intern at Navis and previous Data analyst Intern at LG. Read on to hear her views on being a Pythonista and an NLP enthusiast!
What domain are you interested in and why?
As a freshmen student in college while exploring different domains I found myself really interested in Natural language processing. In late 2017 I remember watching a video on Facebook’s chatbots, Bob and Alice. They were part of an experiment to build negotiating machines. They made it to the headlines because of an unanticipated deficiency in programming logic. While bargaining, Alice made initially incomprehensible statements which lead to people believing that these bots have developed a language of their own.
The more I skimmed through this episode, the more I got my interest piqued. Then I read about MIT’s chatbot Eliza which raised public expectations that it would pass the Turing test. This is how one series of events led to another, and I eventually ended up doing my first project in LG, based on NLP and Data Analysis. I have always been a very talkative person and an active participant in various public speaking events in school, I decided to dive deeper into this field and explore more.
When did you first use Python and how was your experience?
I started traditionally, learning C than C++, and then Java. It was during my internship at LG where I was required to make a project in this particular programming language. Before this, I knew Python was a modern upcoming language and had read about it but never had I actually built something using it.
When I started learning it, I realized it was much easier to pick up as compared to other programming languages, because of its English-like syntax. Then since it’s an interpreted language we don’t get as many errors. Python has huge library support which makes things easier while building projects.
Tell us about that one kick-ass project that you made using python.
Generally, there is a sentiment attached to the first project you build, however as you build more and more projects you tend to become not only technically sound but you also start getting new ideas of projects. So The project that I am currently working on is my kick-ass project in python.
It is a team project that we are building as our final year project of applying the concepts of NLP and data science in HR management. Through this project, we intend to predict employee attrition and hence, help assess the input resources required to put in an employee. We tried various algorithms like SVM, CART, Decision trees, XGBoost, etc. Finally, the best accuracy was provided by XGBoost of 0.93. This project was really fun, as I learned how to deploy models using Heroku and showing the output of the ML model on the web interface.
How would you describe your experience on making your first paper publication?
I had completed one of my projects in NLP and was wondering how to actually begin with writing a research paper. I researched a lot on the internet reading on how other people went to write their research paper. So I searched for research papers on the same topic and found how people had previously worked on the same topic.
I went through about 10 research papers and after seeing how other people had written about their techniques and how they had explained their work, I started writing my paper. It took me around 4 months to complete this paper, maybe because this was my first paper and I was doing all the work myself it took so much time. The second time I wrote the paper it did not take this much time as the work was divided and I had previous experience.
Working on my first paper made me realize that it does take a good amount of patience, hard work, and dedication while working on a research paper.
Publication: ANALYSIS OF PRODUCT REVIEWS USING NATURAL LANGUAGE PROCESSING
As a student soon to graduate what do you aspire to do in tech? Whom do you look up too?
One of my friends told me that, “Excellence is a choice. One should excel not to cater to society but to cater themselves”.
Currently, I am a software engineer intern. I aspire to improve my expertise in the field and justify career success as a result of it. As I have been a curious and communicative person, I could see myself seeking out something along the lines of “AI/ML Engineer” to help bridge the gap between machine and human.
There are many women around me whom I admire. I have been surrounded by really strong-headed women in my life. One of those is Keerti Purswani (Youtuber/ SDE-2 at Intuit) I think it takes a lot of courage to understand all this tech-related stuff and then explain it in a very easy way. I remember while preparing for my placements whenever I used to get stuck up with some concept, I used to search for her videos. Then there is Marissa Mayer, she was one of the first women employees at Google and then went on to become the CEO of Yahoo, talking about her only gives me goosebumps, and seeing how much she has accomplished in her career is really inspiring.
From your experiences as an intern at LG & currently interning at Navis how different is industry from a college student’s perspective?
Right from our school life to college we are an individual contributor to ourselves. It’s always about our grades, our activities everything you do is for towards your growth. One thing that I have realized after entering the industry is you need to have soft skills. You will always be required to work in a team, so the “self-mindset” has to shift to “team-mindset”. When you work in a team, things don’t always go your way, you have to adjust and make things happen to work collaboratively.
The second thing is though for getting a job as a software engineer you need to be good at Data structures and algorithms, however, in industry things aren’t centered around this. Everything you studied in college might not be used however the concepts of Object-oriented programming are heavily used, abstraction being one of the most important concepts. Then you need to work with APIs and learn how to present an output using them.
What advice would you give to yourself when you started out in college?
When I started in college, I feel the learning path that I adopted was not a great way to start. I directly jumped into machine learning and then NLP. If there is any advice that I did like to give my younger self then would be to first get good at basics. First, become proficient with core CS subjects before specializing in any specific domain. By the time I had reached my junior year in college, I realized I was not that good at competitive coding which is the most important for grabbing a good placement. I had to work really hard then to polish my coding skills.
The second thing is to try to get involved in open source projects and contribute to them. That is a great way to learn how to collaboratively work on a technical project and enhance your skills.
What is your take on low participation of women in tech? How do you think this can be changed?
These days a lot of companies including the tech giants have various initiatives aimed at hiring women thus increasing their participation in tech. Owing to these initiatives by these companies and various communities, women’s participation in tech has increased a lot and it continues to do so every day, thus decreasing the existing gender gap in the industry. To keep this up, women out there should be made aware of the various opportunities available for them and encouraged to start in tech.
What are your thoughts on the statement “How can women have it all ?”
While there have been plenty of examples of women having it all like in the case of Marissa Mayer, Priyanka Chopra, etc. Women need to be made more aware in terms of career, opportunities, etc. They should learn to try to learn as much as possible in their area of interest and give their best. The most important thing for women to do is to believe in themselves and their capabilities.
Follow Manaswita on:-
Linkedin : Manaswita Tyagi
Medium: manaswitatyagi
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