Will Your Data Science Skills Take You Where You Want to Go?
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The field of data science is exploding, and the variety of roles for up-and-coming data scientists has expanded right along with it. Known in the 1990s as data mining, the term actually dates to the early 1970s, when it was first introduced as an alternative to computer science. These days, the process of finding patterns among intersecting data sets involves a host of related technology and techniques, including machine learning.
My own career in data science has led me to a wide range of opportunities in the field — and also in my life. And every one of them resulted from my exposure to people and communities that helped me diversify the skills I use to analyze and visualize complex datasets. Since college, when I first discovered data science, I’ve been able to participate actively in the Kaggle community, which allows users to engage and compete with other data scientists.
I was also invited to become a Z The intersection of storytelling and data science is actually quite personal for me. I’m super passionate about images, for example, and on Kaggle, I was initially focused on storytelling through exploratory data analysis and data visualization on any sort of data. I got my start working with tabular audio files, images, NLP, and sentiment analysis before moving into more proper data science modeling. But computer vision has always been a north star for me.
As an undergrad, I came to data science When Everything Clicked: Transferring Skills to Grow My Career
During those early days of my career in data science, my first dataset iterations were a bit clumsy. I soon realized how many micro technical issues can creep into your tabular data, images, text, and audio files.
Once it came together for me through visualization, data science began to feel a little bit like magic. That’s the beauty of it. It’s not just about numbers. You can map early detection of skin diseases, for example, or you can apply it to autonomous driving. You can go on YouTube and create recommender systems, or you can take a bunch of audio files, like bird songs, and classify them and quantify the type of species that you’ve heard. The same goes for finance, medicine, entertainment, publishing, manufacturing, logistics…you name it.
This is why it’s extremely important to diversify early in your career. You want to be comfortable with any possible problem you might encounter and be able to assess any and all types of data. It also helps to be curious: you should feel comfortable researching new fields whenever an opportunity to apply your data science tools presents itself. It’s often best to learn a little bit about everything.
Tools of the Trade: Fully Loaded and Lightning Fast
Having an industry-best workstation to create your data sets and visualizations will always save you time and frustration along the way. My Z
Andrata Olteanu, a data scientist at Endava, is a Kaggle Notebooks Grandmaster, a Dev Expert at Weights Biases, and a Data Science Global Ambassador at Z by HP. She has a bachelor’s in statistics and a master’s in data science and analytics. She likes to combine the visual side of data with the technical side to create fun, educative and insightful notebooks and expand the idea of data science for all.