With all my experiences with Data Science, I believe there are three crucial traits every Data Scientist should have: collaboration, resilience, and creativity.
Math and Computer Science were my favorite academic subjects in high school, and I became quickly interested in Data Science. Taking the initiative, I spent a lot of my free time digging through material online, learning programming skills and Machine Learning concepts through sites like EdX, Coursera, or Udacity. Learning skills isn’t enough, however. Applicable experience through projects and internships/work is the key to the growth of a Data Scientist.
After learning many of the technical skills, I developed my own projects on Kaggle. Kaggle has a strong community of Data Scientists with the same passions and curiosity as me, as well as tons of datasets and competitions to choose from. I participated in many different competitions and worked on my own projects with the public datasets. Currently, I am focusing on image recognition projects. I was also able to meet and network with many other Data Scientists from all over the world and even collaborate on projects. Collaboration really gets the work done, fast with better code and results. Feel free to check out my Kaggle and some of the projects I’ve worked on, or even work with me on some projects! (kaggle.com/samsonqian)
Data Science is not creating algorithm black boxes and feeding data through them expecting to get fruitful results. It requires creativity, as every problem in the world requires different approaches with different Data Science techniques, and there is no magic key that works every time. In the current time, data has the power to solve problems that seemed to be impossible before. To do so, however, creative data-driven approaches must be made to solve different tasks at hand.
I’m interested in how Data Science can help companies make Data-Driven business decisions, as opposed to intuition. The company I worked for, TribioScience, was collecting a lot of data about customer transactions but never touched it. Being a small company, I had to take care of cleaning and processing the data before analysis. This took rigorous work and was incredibly frustrating at times. Resilience and dedication are very important because every Data Scientist will run into a wall at some point. Learning to power through it makes a great Data Scientist. I was faced with many more problems through statistical analysis and ML modeling, but collaboration, my creativity, and my will to not give up allowed me to find solutions to all of them. The company was able to make smarter decisions and raised their profits in a short time.
It is useful to find a domain to apply Data Science to, whether that be Economics or Biology, or something else. There are many ways to develop your skills and become involved in the community. Currently, I am an officer in UIS (Undergraduate Investment Society), UES (Undergraduate Economics Society), and DS3 (Data Science Student Society) and have made many connections with people who share the same interests and goals as me. Networking with others and getting actively involved in the community offer invaluable experiences and resources, so I encourage anyone with an interest in Data Science and professional development to come to events hosted by us!
I am truly proud to be a part of UCSD’s Data Science program, and can’t wait to see how the field continues to develop. Please reach out to me at firstname.lastname@example.org if you want to ask me about anything! Or connect with me on LinkedIn 🙂 linkedin.com/in/samsonq/