This week, we are speaking with Smera Palanivel, who is working as an SDE Intern with Amazon and is a second-year student at the Ohio State University studying Computer Science and Engineering and Psychology. Using the newly launched Datapane features such as tabs, HTML components, and Big Number, Smera creates amazing dashboards to showcase her visualization skills and share data stories with the community.
Let’s get to know more about Smera!
Q: Tell us about yourself!
Smera: I’m a second-year student at the Ohio State University studying Computer Science and Engineering and Psychology. I’m involved in the Big Data and Analytics Association on campus and I am also a teaching assistant for a computer science course. When I’m not studying or learning about data, I love hiking, going on runs, and exploring Columbus. My friend and I recently started an Instagram food blog and love finding good local eats!
Q: How do you like to contribute to the data community?
Smera: I’m just starting to explore the wide world of data visualization! I’m still learning but I try to surround myself with people passionate about data and learn as much as I can, both inside and outside of school.
At Ohio State University, I’m the Corporate Relations Director at the Big Data and Analytics Association, which allows me to work with companies that interact with data at a large scale and create interactive data visualizations. At Big Data and Analytics Association, we have a heavy focus on education and are currently doing a workshop series in Python and R focusing on data manipulation and visualization. We’re passionate about contributing to the student data community and getting people excited about data!
I’m always exploring new tools and libraries and trying to find interesting datasets to work with. I’m currently working on creating an interactive dashboard that CSE professors at Ohio State University can use to visualize grade data from past semesters. I came across Datapane when looking for a versatile tool to easily create and share these dashboards. I hope to contribute to the community by sharing some of the best practices and tools that I come across.
Q: Why do you prefer to visualize data using Python instead of a proprietary tool, like Tableau?
Smera: I feel that Python gives you so much more flexibility when creating a data dashboard. I like how you have control over the whole process, from the data loading/cleaning to data analysis to the final visualization. Python also has a bunch of open source libraries that can help you do anything, from making a simple histogram to running an ML algorithm on the data.
Q: What tips and resources would you have for someone looking to learn about Python data storytelling?
Smera: I think the best way to learn any new skill is to fully immerse yourself and build something that’s simple and interests you. When I first got started with data visualization, I used to spend most of my time reading through tutorials and articles, but I realized most of the learning came from when I started writing code myself.
When working on storytelling with data, it is important to have a mental picture of what you want to convey before you set out to make your visualization. Starting with the big picture and drilling down to the details is a good way to help the storyline flow well. Keep in mind who your audience is and curate your dashboard to best fit their needs.
Another one of my favorite resources is Kaggle!
Kaggle has some great datasets for users at all skill levels. I think finding a dataset that interests you will make the whole process a lot more enjoyable and help you make a final product you’re proud of!
Here are some of my favorite fun datasets!
Legos: https://www.kaggle.com/rtatman/lego-database
Groundhog Day: https://www.kaggle.com/groundhogclub/groundhog-day Global Warming: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
COVID Vaccinations: https://www.kaggle.com/gpreda/covid-world-vaccination-progress
Q: What do you think are the best ways to get noticed as someone telling stories with data?
Smera: Surround yourself with others in the data visualization field through online learning communities. This helps you find a network of people who are also building their skills and allows you to learn from people of all skill levels. Data science applies to almost any field out there and this is a great way to meet people from many different backgrounds. You never know what cool connections you could make!
Create something and do not hesitate to put it out to the community.
Q: What tips would you have for someone just starting out?
Smera: Have fun with it! I’ve found that working on personal projects that are interesting to me are typically more successful than projects that I can’t connect to. Making visualizations about something you’re passionate about and documenting it not only helps you build your skillset but also allows you to share your interests with others in a fun and interactive way.
Most importantly, keep an open mind and strive to learn constantly!
Everyone starts as a beginner and the best way to become an expert at something is to fully immerse yourself, ask lots of questions and actively challenge yourself.
Q: What are your favorite libraries and resources for creating visualizations and data stories?
Smera: My favorite combination of libraries when doing data viz is Pandas and Plotly. Pandas make it easy to load in large amounts of data and analyze the data in different ways and Plotly Express makes it simple to create interactive graphs with only a few lines of code.
I love Datapane because of its interactive layout and how detailed the documentation is. I especially love that it automatically updates the online dashboard when your code runs, which makes it easy to make quick changes and customize your dashboard. It’s also so easy to share Datapane dashboards or embed them into articles.