Author Spotlights

Author Spotlight

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Rashi Desai.

Rashi is a Senior Data Analyst in a healthcare company based out of Chicago. She started her career in data science and analytics two years ago with a Master’s in Management Information Systems and an internship with PepsiCo Global E-commerce while in grad school. She loves to travel and to read and write about the latest in technology.

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What aspects of data science interest you the most these days?

The role of data science and analytics in everyday life is ever-evolving, and I love working on projects that touch human lives; projects where we get to understand people and create a data science model that caters to the needs, demands, and pain points of a human.

Predictive modeling and data visualization excite me the most! With predictive modeling, the projects allow me to go back in time, away from a (sometimes) messy present, to interpret a clear, more meaningful future. We’re talking about time travel here! Only a data professional can do that.

Being a data analyst, storytelling with data is one of my strongest assets. I love exploring the implicit insights from data and communicating crisp insights to business leadership – the translation of data to business is exhilarating.

Can you tell us about the path that led you to this kind of work?

I was introduced to data science and analytics in 2017 during my undergrad—I was pursuing Electronics Engineering, working to file two patents, and was extremely passionate for the field. However, the power and exponential use of data kept me hooked. So I went on to pursue a Master’s in Management Information Systems where I honed in on the skills, tools and technologies to pursue a career in data science and analytics. The school projects served as a great starting point to the diverse beginner projects one could work on – regression, clustering, exploratory analysis, data visualization, etc.

While the classes at grad school prepped me up with technical skills, my year-long internship with PepsiCo exposed me to the real-world – business processes and data-driven decision-making. The internship opened up an ocean of opportunities for me to learn, explore, and experiment across a multitude of projects like social media analytics, predictive modeling and forecasting, correlation explanation, and an emphasis on data cleaning. With that, I continued to write articles on Medium that encouraged me to voraciously read about projects and get inspired.

Did any part of this journey turn out to be more difficult than you’d expected?

As a beginner in data science and analytics, there is never one right way of learning and landing an entry-level job. You learn, experiment, fail, repeat. There’s always a learning curve trying new concepts. One challenge I would like to talk about, however, is not realizing the value data projects can carry. In the beginning, I would aimlessly work on 12 different projects with no plans on how to communicate them. Every project can narrate a business story if you ideate your project and create your own problem statement in that direction.

A couple of years into your career, are there any skills or areas in data science that you find more useful than others?

A strong grasp on SQL and Tableau can go a long way!

As a data professional, data querying is the first step in the life cycle. Over the past year of working as a data analyst, I have written countless SQL queries and chosen between inner join vs left join, reading Entity Relationship Diagrams, creating temporary tables, and more such elemental SQL operations. However, as more businesses migrate from on-premise systems to the cloud, I am taking time to learn more about Cloud SQL and the platforms that can enhance my capabilities as an analyst. At the end of the day, my goal is to fetch information from databases quickly and save effort in time and money. As data analysts, a strong base in SQL is a power move and can make our lives easy at work!

As an analyst, my job is also to narrate a plot to the business – an impactful story supported by data to improve products and processes and communicate insights in a palatable format. Tableau as a tool has garnered a lot of popularity amongst executives as they love looking at interactive dashboards for effective decision-making. I continue to hone my Tableau skills every day.

There has been a lot of buzz about low-code/no-code platforms for quick and efficient data modeling (random forests, forecasting models, regression, etc.), and I am super pumped to try on some of the new and upcoming tools.

Why did you start writing for a wider audience—and what factors contributed the most to your success?

I wrote my first blog post on Medium in July 2017 as I was exploring user-experience design. With no idea that data science and analytics would be my career someday, I continued to write about UXD. It was only in June 2019 that I wrote my first post about data as I was starting graduate school. The transition was very intimidating at first. For six months, I was just struggling to find my niche.

Then, in December 2019, I had planned a lot of content to write over the winter break with my learnings from the first semester at school, and I started with Top 10 Python Libraries for Data Science. That blog post blew up! It had ~250K views in just a week and I had a moment of truth. That’s exactly when I knew what my target audience could be. Since then, I have been catering to an audience with beginner-intermediate knowledge about data science and data analytics.

Over the past two years, I have realized that as more and more people enter the data world, they want to read about how to get started, what projects to work on, tools and skills for a data professional, learnings from the business, and more beginner-friendly content. In a world of jargon and clickbait, people want to read genuine content. My most-read and -interacted articles are about real-world experiences, where I talk about my day as a data analyst, creating impact at work, how I got my job, and my experiences and learnings. I am inclined towards a data language that both an 18-year-old and an 80-year old can understand with the same ease, and that seems to work the best so far!

What advice would you offer to people who are considering a data science career? What questions should they ask themselves?

Does data excite you to solve real-world problems?

Is data science or analytics motivating you enough to take on challenges?

I’ve met a lot of people who consider data science and analytics just because some publication said "data science is the sexiest job of the century" in 2012, and they are not happy with their career arch today. In my journey to become an impactful data professional, I’ve found three statements to be an excellent pivot:

• Identify what you love doing in your career, and more importantly, what you do not.
• It is okay to feel overwhelmed by the depth data science and analytics has to offer. Start small with the basics, and build your way up to complex projects at your own pace. Read what people are working on. That can inspire you, set expectations, and introduce you to the latest and greatest in the data community.
• Take time to create your value proposition as a data person and work to be the subject matter expert for a niche. Be the pacesetter of goals for people to turn to you for knowledge, advice, or to get stuff done.

Also, a data professional without business acumen is like a sword without a handle. The ability to translate business problems into data and connect it back to business impact is compelling and much appreciated in today’s world.

If all of these still don’t connect with you, there are plenty of other roles in data beyond data scientists and analysts! There’s a lot in store for a technology enthusiast today.

Where do you see data science going next, and what are your own goals within the field’s future?

The careers of the future include robust data science, acquisition, and analytics across any and every industry – healthcare, finance, sports, retail and ecommerce, streaming, aviation, dating, marketing, weather, education, government, travel, and everything else under the sun. If you find data and its landscape fascinating, now is the time to take up a project or make that career transition (and try not to leave it incomplete).

In 2022 and beyond, I am excited to see human-centered data analytics, composable analytics, and the emergence of small data and data marketplaces – a lot of exciting things are brewing in the data landscape.

In my role as a Senior Data Analyst for a healthcare company, I envision promoting the centralization of data sources, automation of everyday data jobs, a stronger reliance on cloud, and increased use of predictive analytics to bolster performance. I am more than excited to be a fly on the wall with the tremendous opportunities for advancement in data science and analytics in the future.

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To learn more about Rashi’s work and to explore her latest articles, follow her on Medium and on Twitter. For a quick introduction to Rashi’s TDS articles, here are a few highlights from our archives:

• How to Start Your First Data Science Project When You’re a Beginner (June 2022, 6 minutes)
• Why are Data Science Jobs Hard to Get When They’re the Most In-Demand? (October 2021, 5 minutes)
• What to Study Before a Data Science Interview (May 2021, 14 minutes)
• 5 Things I Actually Work on as a Data Analyst in 2022 (May 2022, 5 minutes)
• A Step-by-Step Guide to Becoming a Data Product Manager (September 2020, 6 minutes)

Feeling inspired to share some of your own writing with a wide audience? We’d love to hear from you.

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This Q&A was lightly edited for length and clarity.

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