Data science is not just about algorithms and code. In many ways, we need a deep understanding of the context and the industry. We must know what data sources to use, what approaches to take, and how all of these data points will be appropriately applied.
To be efficient, communication plays a crucial role. From gathering useful information (data) to setting a clear process for accessing what is needed, and wrangling all the information correctly.
That’s right: data can feel intimidating, overwhelming, and complex. But… at the end of the day, you must remember why you do all this. Your job is to bring clarity and precision to millions of data points that might otherwise remain unused. Time, simplicity, and wise choices on the right tools and libraries are essential. Otherwise, you risk wasting time on tools or languages that only slow you down.
Iteration and continuous learning along the way are invaluable. Handling missing values the right way is just as important to avoid misleading results. The same goes for your visualisations and dashboards.
Yes, your data visualisations need to tell a story. They must serve the given task, meet the requirements, and put everything together in a way that transforms raw data into a meaningful and beautiful story.
Research, refine, learn, and evolve your work. You will see that every day as a data scientist is unique, just like the data you handle. Data science is an ongoing journey. Each project demands fresh thinking. In the end, every day as a data scientist is rewardingly different, and that’s exactly what makes it worth it!
