Student Perspectives: How to Become a Pro in the Field of Data Analytics

MS Statistics ‘24 student, Margie Juardo Espinoza, shared with the GCMC how she employed effective communications and strategy to grow as a data analyst and to expand her network in the field. 

What has been one of the most important things you’ve learned at your internship?  

One of the most important lessons I've learned throughout my internship is the value of communication. Clear and effective communication not only ensures that all team members are on the same page, but it also encourages a positive work environment. Specifically, as a data analyst, I've learned the importance of asking proper questions. The process entails not just acquiring and analyzing data but also comprehending the context, recognizing the problem or opportunity, and framing questions that lead to actionable insights. It is important to learn how to effectively communicate the goals and scope of a project to concerned parties and then convert those needs into data-driven solutions. 

This ability has improved both the caliber of my work and my ability to make contributions to decision-making processes. 

How have you approached networking at your internship? Share any one specific example that has been impactful for you. 

During my internship, I found that networking was crucial to establishing relationships inside the organization and learning about multiple aspects of data analysis and its uses. A specific example is when I took part in a cross-functional initiative that attempted to enhance data-visualization methods among different teams. I had the opportunity to work with peers from a variety of departments on this project, including product development, marketing, and finance. I actively tried to have meaningful interactions with them during our work, share insights from my perspective on data analysis, and gain knowledge from their expertise in their different specialties.  

One particularly significant event was during the marketing team’s meeting with a customer. I shared an experimental technique I acquired from a previous experience as we talked about methods for presenting data in a visually appealing way. I was surprised to hear that their analyst seemed fascinated by this approach, and they mentioned that my company report was, in some ways, better.  

This started a conversation, which resulted in insightful knowledge sharing. I volunteered to demonstrate the method and walk them through the process. In exchange, they provided my company with insights into industry trends and consumer behavior that improved my understanding of how my analytic abilities could support more general business goals.  

Through this collaboration, I was able to expand my network within the organization and gain new insights and methods for approaching data visualization, which improved my abilities as a data analyst. It proved the value of networking in promoting information exchange and interdisciplinary teamwork, which eventually benefited the project's success as well as my professional growth.  

What has been your biggest contribution to your team at your internship? Why?  

My biggest contribution has been optimizing the process of collecting and cleaning data. As a global company that has suppliers in multiple countries, we get information in multiple formats. That is why I developed templates and guidelines to standardize the documentation process and facilitate consistency across different data sets. These improvements save time for my team, allowing them to concentrate on more strategic work. This lowers the possibility of errors that could arise from managing data manually while simultaneously increasing efficiency. Furthermore, I am currently working on a project to improve data visualization methods so that team decision-makers can easily access and utilize complex information.  

After this experience, what would you like to learn next?  

After this experience, I'm excited to expand my understanding of data analysis fields, like machine learning and predictive analytics. To gain valuable insights and produce precise forecasts, I would like to investigate innovative modeling and data interpretation methods. I have been utilizing programs like Tableau and PowerBI thus far. Nevertheless, as effectively expressing insights is essential to promoting well-informed decision-making, I'm interested in honing my narrative and data visualization abilities. In addition, I'm eager to obtain practical experience with big data technologies and cloud computing platforms so that I can effectively manage and evaluate massive datasets. In general, I want to keep expanding on data management, and improve my analytic skills to address challenging situations in the industry.  

For students having a difficult time finding an internship, what is one piece of advice you would give them?  

I would advise students who are having trouble finding an internship to widen their search parameters. Have a look at startups, non-profits, smaller firms, and research institutions rather than only big organizations or well-known multinationals. Even though they don't openly post internship openings, these companies frequently have better access to internship possibilities and may even offer special opportunities. Furthermore, networking is beneficial. Speak with instructors, former students, close friends, or business associates to find out about possible internship opportunities or to get recommendations on where to look. Additionally, considering alternative options such as short-term projects can also be beneficial for gaining valuable experience and expanding your professional network.  

Besides that, students can improve their portfolios by developing their technical skills—that is, learning how to use fundamental data analysis tools and computer languages like Python, R, SQL, and ggplot2. Additionally, students can work with peers on real-world datasets, individually complete data analysis tasks, or work on extra assignments that demonstrate proficiency in their fields of interest, such as data collection, cleaning, and visualization. It is also important to highlight relevant skills, experiences, and projects that align with the specific requirements of each position you apply for. Show enthusiasm for the company and explain how you can contribute to their data analysis efforts. Finally, perseverance is the key to the job search process. Keep improving skills, building your portfolio, and networking with professionals in the field, and eventually you'll find the right opportunity.