Data Science & Critical Thinking with Deborah Berebichez

Listen to this episode on Anchor FM

Are you curious and inclined to look beyond the surface of data? In this episode of the DATAcated on Air podcast, host Kate Strachnyi talks with Deborah Berebichez about critical thinking and data science. Deborah is the Lead Scientist in Quantum Computing at VTT Technical Research Centre of Finland and a television host at Discovery. Her passion for physics has inspired many to pursue their dreams in STEM. Listen to learn more about Deborah’s journey of overcoming discouragement and how you can apply those lessons to your journey.

You will want to hear this episode if you are interested in...

  • Overcoming educational stigma [01:52]
  • Data science training [09:15]
  • Free vs. paid resources [12:05]
  • Defining “data scientist” [16:17]
  • Promoting STEM careers for women [22:01]
  • The future of data science [27:44]
  • Embracing failure [34:20]

Pursuing academic dreams

Deborah grew up in Mexico City in a community that discouraged young girls from pursuing careers in STEM. Because of that, she learned to hide her curiosity and love for math and physics. Deborah chose to study philosophy in college because she could at least ask questions about the universe. Two years later, she realized that her hunger for math and physics wouldn’t fade away. That realization started Deborah’s process of applying for a school and scholarship in the U.S. to pursue her academic dreams.

Brandeis University was the first place Deborah experienced having someone believe in her. Someone finally saw her curiosity and perseverance as gifts that shouldn’t be wasted. Because of the investments of time and opportunities from people at the university, Deborah was able to thrive. She has made it her mission in life to return this investment by encouraging young people, especially young women, who are attracted to science or technology but feel like they cannot achieve their dreams.

Data science bootcamp

Metis is a data science training company that trains people in twelve weeks with the most basic and advanced tools to become data scientists and then helps those people to find jobs. The company also has live, online courses that are a pre-boot camp for people who aren’t quite ready to take the boot camp.

Metis also does corporate training, which means going into corporations such as technology companies, financial institutions, and healthcare companies that want to train part of their workforce in a particular aspect of data science. Metis designs the curriculum, customizes it for the organization, and then instructs people for upskilling.

The boot camp Metis provides is an intense program, and the company tries not to accept people who would be easily discouraged or who don’t have the right skills to begin boot camp. Most people who apply do have a background in some mathematics, statistics, or something similar. However, that background isn’t required. Metis has had some great success with people who don’t even have a college degree. People come with backgrounds in marketing, music, and advertising and complete the course.

What is a data scientist?

Aside from perhaps basic programming and mathematics, there isn’t a specific skill that determines whether someone is a data scientist. The requirements depend greatly on the field of business. One perspective is that people could call themselves data scientists when they can make a living in that role. The moment someone can explain insights and gather information that others cannot without the same skills is when that person becomes a true data scientist.

The data science field is changing drastically and rapidly. Things like linear regression and classification algorithms are being automated. The platforms will even be able to choose the parameters and the algorithms and generate insights. More translators will be necessary to interpret what the machines are showing. These people wouldn’t necessarily need to understand the intricate details behind the algorithms or be able to create new ones. Still, they would need to be able to interpret the data and the algorithm at hand. Also, the more sophisticated skills that come with big data and deep learning and the more obscure the algorithms become, the more data scientists will need domain knowledge to operate in various fields.

Resources & People Mentioned

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