As interest in data grows, there might be some confusion over terminology. Here is a closer look at the difference between data science and data mining.
Meet Nada, one of our industry leading Instructors for the Data Science course at our Vancouver Campus. Apart from her data science background, Nada also has experience in consulting. Today she’s a data scientist at Microsoft and is constantly leading research and machine learning projects. Here are some of her insights into this trending topic, and what it’s like teaching at BrainStation.
BrainStation: What would you say is the biggest misconception others have about data science?
Nada: One of the biggest misconceptions about data science is that you need to have access to lots of data to drive insights. That’s not always the case. Though having a large volume of data is helpful, insights can be driven from data that is rich in variety or veracity. What is important is having the RIGHT data to be able to drive competitive edge. A key component to being a great data scientist is being able to figure out what critical pieces of data are necessary to answer the hypotheses at hand.
What aspects of your career in data science do you love the most?
There are many aspects of data science that I love. Mostly, I love solving problems and sharing insights with colleagues and others. Even the smallest insights have a big impact, and it is always a joy to see those insights help form informed business decisions and shape the products in question.
What key takeaways have you learned in your career trajectory?
My career trajectory has taught me that there is no one right way to solve a problem. Data Science is simply a means to an end, and data scientists come from an array of different backgrounds. We are tasked with identifying a problem and then solving it by whatever methods we determine are best. It is always fun to be in control of the narrative, but it is also an immense amount of responsibility. The path we take to connect the dots is completely ours.
In your experience as a data scientist, how relevant and applicable is the BrainStation approach to data skills training?
Brainstation’s data science course teaches individuals the fundamentals of programming in Python, to create algorithms using basic statistical methods, and to understand advanced data structures and models. Armed with those skills, students will have the skills and tools to collect, wrangle, and analyze data. All of these skills not only allow the students to effectively communicate insights, as data scientists, to a wide array of audiences, but also help organizations tackle many problems from different domains.
As an instructor at BrainStation, what do you want your students to leave with?
I would like my students to leave with the necessary tools to tackle any data science problem thrown at them. Ideally, the students would leave with an excitement towards data, the ability to ask the right questions to solve the problem at hand, and the flexibility to learn new skills to remain on top of their game.