In today’s world, data is indispensable. It’s everywhere, shaping decisions in every industry. Without data, you’re just another person with an opinion. Every day, we see companies skyrocketing their revenues by making data-driven decisions. That’s why it’s crucial for businesses to rely on facts rather than gut feelings or biases. As it is often said, “Data is the new oil.”
My journey into data started at Iowa State University, where I initially majored in Computer Engineering. While I enjoyed coding, I realized it wasn’t something I wanted to do all the time. That led me to switch to Management Information Systems (MIS). It was a tough decision, as I thought I’d be leaving engineering behind. But as I progressed through my degree, I realized that data was where my passion truly lay. Fun fact: switching majors in the American education system can take just four hours!
At Iowa State, I was introduced to SQL, Power BI, and Tableau, which opened the door to the world of data. What I loved most was how working with data felt meaningful. Sure, spreadsheets can seem dull, but the insights you can pull from data visualizations in Excel, Power BI, or Tableau are anything but. I loved building pipelines and creating live data feeds. This exposure made me even more excited about data analytics and querying.
College semesters are short, and group projects often mean that not everything can be covered in four months. But what college did well was teaching the foundational concepts. For instance, I learned SQL in class and practiced at home, reinforcing my understanding by researching online. This hands-on learning approach helped me develop a strong grasp of data techniques.
After graduating, I was amazed by the scale of data in the professional world. While college had me working with thousands of rows, my first full-time job involved millions! I had to build on the concepts I learned in college and adapt to this larger scale. One tip I’d offer is to start small: if you’re troubleshooting a process, work with a sample of 10-50 rows. If you can fix the issue on a small scale, it will likely work on the full dataset. In my first few months, I was overwhelmed by large datasets, which intimidated me. But once I started taking it step by step, it became more manageable.
For anyone already in the data field and looking to build projects, here’s a simple roadmap: Learn SQL → Visit Kaggle → Pick a dataset → Clean and analyze it → Connect it to Power BI or Tableau to create insights. This roadmap teaches a number of different concepts like data collection, data modeling, data analytics, data transformation, etc. This process is a free and effective way to showcase your skills to potential employers. Working with real-world datasets will help you understand different complexities and improve your technical abilities. Once you land an internship or full-time job, your skills will grow exponentially.
In my 5+ years in the data industry, I’ve found that while I learned a lot on the job, the thirst for knowledge and self-motivation are what truly drive success. Everyone has their own learning style, but this approach worked for me. One resource that’s often underrated is YouTube—it’s packed with free content (ads aside) that can teach you the core concepts or ignite your interest in the field.