My Journey into Data Science: VMware’s Pragya Mishra

Everyone strives for great things when they dive headfirst into the industry. But some scammers keep a low profile, quietly leading the way in the tech space.

India Analytics Magazine We engaged with one of those women in tech, Pragya Mishra, Business Analyst (Data Science and Analytics), VMware, who provided insight into an ideal inclusive workplace.

From running business operations to mentoring students down the same path, Pragya shares her learnings on how to thrive as a data scientist. Pragya, Senior Fellow of Women in Analytics at VMware, has a bachelor’s degree in engineering.

OBJECTIVE: Narrate a typical day in the life of a business analyst.

Pragia: Organizations use tons of data. Business functions like treasury, legal, and internal audit are constantly trying to break down their massive business problems into little bits and pieces. Being part of the digital transformation main office, we lead initiatives, working in a sprint manner. A meeting with senior management is held every 15 days, to plan activities that revolve around generating impact. We serve multiple business functions with a primary focus on major data oddities. And there are always checklists to take care of smaller tasks and goals.

OBJECTIVE: What are the roles and responsibilities that come with being a part of the main data office at VMware?

Pragia: As a business analyst, my responsibilities include connecting IT and business teams, understanding business issues and their impact, analyzing various processes involved, and gathering detailed requirements. I also indulge in data and business solutions, in-depth data pre-processing and analysis to deliver data-driven recommendations using advanced machine learning and analytics. In addition, I also communicate the ideas and findings to upper management and deliver effective business products and solutions.

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PURPOSE: Highlight some of your contributions to the company and to the data science community, in particular.

Pragia: Success for me is sharing my learnings. One can never learn everything at once, it is an endless process. That’s why I chose to contribute to the data science community by starting the ‘Wabi Sabi Initiative’ – a non-profit mentoring program inspired by the Japanese philosophy of embracing imperfections. Its objective is to help new students in the field of analysis and data, allowing them to develop the necessary skill sets.
I also work as a subject matter expert and video content creator at upGrad, one of the leading edtech startups in India, creating written content on data science and business analytics. When you are advising or creating content, you are constantly learning together with them. It is a bidirectional process that does not stop.
Apart from that, I actively participate in Kaggle competitions, which helps me to update my knowledge in the data science industry.

OBJECTIVE: Could you elaborate on your educational qualifications and previous work experience?

Pragia: I have a degree in electronics and communication. I was always trying to figure things out and narrate things, call it data storytelling. So when I came across a big data analytics company called Mu Sigma, I was completely fascinated by how they solve business problems for Fortune 500 companies. I got into the role of a decision scientist with them.

OBJECTIVE: How do you approach a data science problem and make sure the work goes smoothly as planned?

Pragia: One of the main obstacles is the chaotic problems that data scientists are trying to solve. It’s never easy. One must be aware of the problem by constantly learning in the domain of their peers and leaders. To solve a problem smoothly, one must have the correct data sources to clean and pre-process and mitigate any risks. Certain questions may arise, such as, do we have the correct data? And when we do, we need to clean it enough, since one is likely to come across tons of dirty data on a daily basis.

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OBJECTIVE: How do you plan to grow in the data science ecosystem in the next five years?

Pragia: I envision myself evolving with the data science and product innovation community, collaborating with many experienced data people, budding data scientists, and analysts through online and offline partnerships. It would continue to be part of initiatives with great social and economic impact. Definitely great times ahead!

OBJECTIVE: As part of Women in Analytics at VMware, tell us about your company’s goals for a diverse and inclusive environment.

Pragia: By inclusion, we mean bringing together the power of different people from different backgrounds in the company. We become more aware of the challenges they faced, bringing different stories to the table. As a member of the Women in Analytics core team at VMware, I want to see an equitable environment that breaks down all barriers of gender inequality.

OBJECTIVE: How can organizations address gender inequality in the workplace? What steps can be taken to address such challenges?

The only way to fight unconscious bias at work is to hire more diverse people. When we discuss hiring different genders and races, we are united in understanding unrecognized patterns at work. We talk to them, acknowledging the challenges they face. This is something I have learned from my leaders. Such conversations will help organizations address these issues.

1) Favorite ML/AI algorithm
Machine learning models change so quickly and frequently in the industry that I won’t have a favorite model. But depending on the classification, I would say that the ensemble models are the ones I always look for. It’s because of the massive data loads they can handle.

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2) Favorite book on data analysis
My recommendation for anyone just starting out is Deborah J. Rumsey’s ‘Statistics for Dummies’ and Dinesh Kumar’s ‘Business Analytics’. However, Kevin Huo and Nick Singh’s ‘Ace the Data Science Interview’ tops my list.

3) Favorite Podcast on AI and Problem Solving
My favorite is Lex Fridman on Spotify – he’s an AI researcher at MIT and his podcast mainly relates AI to problem solving, space travel, neuroscience, human-robot interaction, etc. He is one of the best out there.

4) What would you be, if not a data scientist?
For the love of storytelling, I would primarily be a consultant who is, again, telling another data story in some other part of the world (or a screenwriter!).

5) Your advice for women who want to follow this path?
Constantly improve your technical skills. Period. Regardless of what genre you fall into, I think one thing that will always keep you in the game is if you consistently master your problem-solving skills. Second, always speak up and take a stand for yourself, because if you don’t stand up for yourself, no one else will.