UCLA Extension

Digital Technology Instructor Interview: Vera Kalinichenko

Vera Kalinichenko is one of our new Data Science instructors and is a professional in the field. We are very excited to have her join our team!

UCLAx: Please tell us about yourself and how you got to where you currently are in your career?

Vera: I was born in a small town in the west of Ukraine. I participated in national mathematical competitions while I was in high school. I have graduated from VZMS (http://math-vzms.org/) which was an old USSR-style mathematics school for high school students. I was accepted into Kiev State University named after T. Shevchenko and spent two years there studying pure mathematics. In 1997 I came to the United States.

I went to UCLA for my undergraduate and graduate work. After UCLA, I worked for many years as a software engineer, building software for finance companies to trade bonds, options, and currencies. During 2012, when Big Data started to rise, I switched to the field of data science and have been doing data science since. Currently, I work as Principal Data Scientist at Atom Tickets, LLC. My personal goal is a constant search for knowledge since life is a puzzle.

 

UCLAx: Is there anything you are currently working on that you would like to share with us?

Vera: Currently, I am working on a customer movie list personalization for Atom Tickets and a variety of others projects; from basic model tuning to experimentation with neural nets and deep-learning libraries from TensorFlow. I love opening a probability graduate book or any mathematics textbook and reading a few pages for inspiration. I strongly believe that discrete mathematics, combinatorics positively influence your creativity and help build elegant and simple models.

 

UCLAx: Could you tell us about your course, “Introduction to Data Science,” and what students can expect to take away from it?

Vera: My course is about developing a strong foundation in data science, familiarizing my students with data exploratory analysis, illustrating to my students the practical overview of several commonly used models in a wide range of fields. Data Science applications range from retail to finance, the medical field to engineering. I would like to prepare my students for their professional career as a data scientist/data analyst.

 

UCLAx: What advice can you give to our students trying to break into the data science field?

Vera: Mostly my students will not build gradient descent models from scratch at their workplaces, since there are so many libraries already written and available for use, and there is no need to reinvent the wheel. However, I strongly believe that it is very important to understand the main concepts behind the most commonly used models. What really lies behind the models are basic optimization techniques. It is important to develop an intuition of how to build a model and know what techniques work in what use case. It really makes sense to spend time and develop that foundation if you are serious about data science. I think data science is a combination of art and science, that uses key ideas from mathematics, statistics, machine learning, and physics, so it is useful to review the basic statistics and linear algebra concepts, then just keep building on that foundation.

There is so much information available nowadays, you just need to allocate time and use books and lectures, read blogs and start developing code, and practice modeling. I think if a person wants to learn something, now is the best time to be living and achieving it. There is so much quality information available, you should use it as learning opportunity. The only commodities we need are time and perseverance.

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