Introduction
Hello, I'm Belise Bwiza, an entry-level Research Analyst with a focus on research and data analysis. I am passionate about leveraging data to drive meaningful business outcomes. With a bachelor's degree in Quantitative Economics from Smith College, diverse internships and full-time roles in consulting, tech, aviation, and international development organizations, I have developed strong expertise in conducting quantitative analyses using tools such as Stata, R, Tableau, Python, and Microsoft Excel.
My professional experience includes research roles in the aviation sector, where I have analyzed aviation markets and trends. Additionally, I have contributed to international development initiatives across multiple countries, conducting research and data analysis to support impactful outcomes.
Let’s dive into my career journey and discover how you can build a career working with data, even if you’re not a data science major.
Data Science Fellow
career options
The world of data offers a very wide range of career avenues. Nearly all sectors and industries rely on data to make informed business decisions. No matter your field of study, whether it’s a data science major, a related discipline, or something entirely different, you can still develop and apply data skills to tackle data-related tasks in your career. As a data scientist, analyst, or data enthusiast, these skills can unlock opportunities across a wide range of industries. Over time, your career journey may lead you to cultivate specialized expertise in a particular domain. Here is a non-exhaustive list of possible career paths:
Data Science Fellow
skills
What are the main hard skills you use on a daily basis in your current job?
Data scientists gather, clean, preprocess, and transform raw data to prepare it for analysis. This includes addressing missing values, managing outliers, and resolving formatting issues. I developed these skills during an introductory econometrics course in college and enhanced them through free Excel tutorials, as well as several free courses on Coursera and edX. I started collecting data in my first internship in a behavioral economics firm in Nairobi as well as my first full time job.
Data scientists employ statistical techniques and machine learning algorithms to analyze data and identify trends, patterns, and correlations. They develop models to make predictions and generate actionable insights. I started analyzing data in a couple of courses in college as well as my two full-time roles.
Data scientists create visualizations, such as charts and graphs, to effectively communicate insights to non-technical stakeholders. I first learned the basics in college and applied them during my first full-time job and have continued to develop my skills in visualization techniques and tools ever since.
Proficiency in statistics is essential for hypothesis testing, model evaluation, and understanding data distributions. I gained this foundation through statistical courses in college, which also helped me become comfortable working with graphs.
Depending on the industry, data scientists may need domain-specific expertise to understand the nuances of the business and data. I majored in economics in college, focusing on international development before transitioning to aviation. Having expertise in a particular domain not only deepens understanding but also brings greater meaning and purpose to the work.
What are the main soft skills you use on a daily basis in your current job?
Dealing with data requires a lot of precision and care, so being attentive to small and big errors is key to maintain data integrity.
Being able to communicate your data either verbally, graphically, or in written form is needed in presenting the importance, relevance, and impact of your findings with relevant stakeholders.
Data analysts and scientists frequently face technical challenges, such as determining the most relevant data points to analyze for a given problem and interpreting the results to inform decisions. Strong problem-solving skills are critical in navigating these challenges.
The data landscape is constantly evolving, with new tools, technologies, and methodologies emerging. Being open to learning and adapting quickly is crucial for staying relevant.
Data professionals often work in cross-functional teams, requiring strong interpersonal skills to collaborate with colleagues from diverse backgrounds like marketing, finance, or engineering.
Belise
’s personal path
Tell us about your personal journey in
Data Science Fellow
:
Even though my career path is still in its early stages, my path has been far from linear. I’ve explored internships and full-time roles across various fields and companies. My journey in data science has been shaped by exploring diverse industries, including tech companies, management consulting, economic consulting, aviation, and international development organizations. In each of these roles, I utilized data and research, experiencing how they can be applied in various contexts. Allowing myself to explore these different fields not only broadened my perspective but also helped me discover the areas I’m most passionate about.
Through these experiences, I learned that I particularly enjoy aviation-related roles, which surprised me, as I initially believed international development would be my long-term focus. Despite this shift, data and research have remained a constant in every role I’ve held, serving as the foundation for my growth and the development of my skills. By not limiting myself to a single field, I was able to gain deeper insights into my interests and uncover new career paths that excite me.
As an international student with a limited to no network in the US, I relied heavily on my school’s career center and professors as resources to identify internship and job opportunities. I also leveraged LinkedIn to connect with professionals for informational interviews and career advice. The application tool that proved most effective for me as an international student was Handshake. Its filters made it easy to find relevant opportunities, and because it’s tailored to students, most employers on the platform are prepared to work with candidates with limited work experience. Additionally, Handshake’s employer responsiveness scores encouraged timely communication from companies, which significantly improved my experience.
What would you tell your younger you regarding building your current career?
If I could advise my younger self, I would encourage her to explore as many learning opportunities as she can, in whatever field, and to be fearless in her pursuit of knowledge and opportunities. I would remind her that learning is a long and continuous journey, and that belief in self and hard work will always propel her forward. I would also encourage her to seek out mentors early on, because there are incredible people out there who are not only willing but genuinely excited to help young, hardworking individuals. Finally, I’d remind her to celebrate small victories, cherish the process, and trust that every experience, no matter how small, contributes to the person she is becoming.
Final thoughts & tips
Whether your future career will be in a data field or not, you’ll inevitably encounter data along the way. Having even a basic understanding of data can be a valuable tool and taking a class or two could open doors you may not have anticipated. Your education is an investment in yourself and your future, and it’s something that no one can take away from you.
The journey won’t always be easy, and there may be moments when it feels overwhelming. But remember, every step you take brings you closer to your goals. Stay curious, ask for help when you need it, and keep moving forward, even on tough days. Completing your studies is a powerful achievement that will set you up for opportunities you might not even imagine yet.
Above all, trust in your ability to succeed and know that the effort you put in now will pay off in ways that matter most to you. You’ve already come so far, so keep going, and don’t give up because you’re capable of amazing things!
Resources to dig in more
Data Analytics Certificate & Training - Grow with Google
This program provides comprehensive training in data analytics, covering essential tools and skills like SQL, Tableau, and R. It's designed for beginners and offers a certification upon completion.
Analytics Academy
Google's Analytics Academy offers free courses on Google Analytics and other tools, teaching how to measure and analyze data for effective decision-making.
Free Tableau Tutorials
A collection of free online tutorials and guides to learn Tableau, a popular data visualization tool. Great for beginners and those looking to enhance their skills.
Educational SQL Resources - SQL Server
A wide range of tutorials, guides, and learning paths provided by Microsoft to help you master SQL and its applications in database management and analysis.
How to Become a Data Analyst (with or Without a Degree)
This guide outlines the steps to become a data analyst, including key skills, tools, and resources. It provides insights for both degree holders and those looking to learn independently.
Why All Employees Need Data Skills In 2019 (And Beyond)
This Forbes article highlights the importance of data skills in the modern workplace and explains why developing these competencies is essential for career growth.
