What do you get when you combine hands-on learning, team mentoring, and coding skills over long, hot summer days? One of the best internships ever to kick-start your career!
This summer, Selector AI welcomed five interns—all high school students with an interest in data science. Despite each one having minimal work experience and knowledge of coding, they were determined to dive in, learn, and do their best—all essential components for success.
This post introduces you to each of our interns, explains the projects they worked on, and shares their thoughts about the experience. You’re sure to see some of their work in a future release of Selector.
Aman: Device failure detector
Aman Jain spent his summer at Selector looking for a way to use temperature data to detect device failures. This project expanded into developing a time series analysis method to monitor and observe devices within a network.
Aman said the opportunity enabled him to become familiar with Prometheus, Python libraries—like PyCaret and Pandas—and Jupyter Notebook. He added, “The most important part of my internship was the guidance I received. … the work I did at Selector was really successful. But beyond that, I am really glad to have had this experience.”
Ishanvi: Keyword Search Utility
Ishanvi Kommula developed a keyword search utility for users to retrieve relevant queries, widgets, and dashboards within the Selector platform. Working with her mentor, she learned how to break down the project into manageable chunks. In the process, she gained hands-on skills with REST APIs, CSV and JSON files, VS Code, and Jupyter Notebooks.
Despite running into challenges with her code, she found a way to rework it so she could then focus on enhancing its ease of use. This part entailed converting the program to a user-defined class—executed periodically by the data hypervisor—so she could better understand the application structure.
About her experience, Ishanvi said, “I achieved my goal to contribute to the platform through facilitating ease-of-use and customer satisfaction. … I also acquired many skills in programming and problem solving that will help me as I further develop technical proficiency.”
Manya: Network metrics collection service
Manya Bachheti created a service to collect network metrics from Google Cloud Platform (GCP) so users can view them in Selector alongside on-premises network metrics. Having all network metrics in Selector will enable more meaningful dashboards. It will also support alert configuration within the platform and the delivery of notifications via Slack or Microsoft Teams.
Thanks to guidance from her mentor and team—along with hands-on learning about GCP, Google SDKs, and how to write a simple program in GoLang—Manya created her service. She brought it up almost instantly, referenced boilerplate code for her project, and visualized the data she collected for her service. After dumping the data into Selector’s time series database, the data was immediately visible in various forms, such as line plots, honeycombs, and timeline heat maps.
Reflecting on her experience, Manya said, “I learned how to collaborate with others and how to use pre-existing code instead of rewriting it on my own. I also learned to be confident in my work and to present it proudly in front of others.”
Sia: Alert report card
Sia Agarwal created an alert report card based on an objective scale to improve alert configurations. This report card will make it easier for users to identify and fix an issue, instead of sorting through misconfigured alerts, for example, that have a poor description.
The program evaluates the quality of alerts made in the Selector workspace based on their name, description, priority, and notification profiles. The resulting report card lists all the alerts and their grades, in addition to providing an action plan for the user to fix the configured alerts across the deployment. Because the alerts are ranked in descending order, users can address those with higher severity first.
Besides the fundamental programming skills Sia gained from this project, she said, “I learned the principle of putting the user first. A good service is strongest when the user has everything they need to have a successful experience.”
Pranav: Correlation and causation
When exploring the Selector deployments, Pranav Nimmagadda was drawn to the correlation graphs and recalled the statement “correlation does not mean causation.” He wanted to explore whether this concept was true, so he asked if root cause identification would be plausible.
Working with his mentor, he learned helpful data mining techniques to expand his understanding and discovered that “correlations with associations can imply causation.” In the process, he also became more comfortable with Python libraries—like Pandas and MLxtend—and Jupyter, which enabled him to test his ideas.
About his experience with Selector, Pranav said, “Everyone was so welcoming and made me feel at home. The daily morning huddles were tremendously helpful in this regard, as I learned about others’ projects and how they asked for help.”
Fundamental Career Lessons
After learning about our intern experiences, we were reminded of these fundamental lessons for everyone:
- It’s okay to go into a job not knowing everything. The best part is learning and growing from it.
- Don’t dive headfirst into a new project. Break it down into manageable chunks and ask for guidance if you need it.
- Never underestimate the power of passion and ambition. Stay focused and ask the right questions to help you reach your goal.
- Teamwork makes the dream work. Working collaboratively builds trust, confidence, and motivation across your team.
At Selector, we practice these lessons every day—even in our remote work environment. It’s what makes us and our interns so successful. Plus, it helps to work on a platform that’s so programmable and easy to work with.Do you know a student who’s interested in a data science career and wants to try it out first-hand? Encourage them to apply for an internship at Selector by emailing us at firstname.lastname@example.org.