I have been following Renee Teate since the day I joined Twitter and I have learned so much from her (technology, ethical practices, gender bias issues in tech) since then.
The host of the popular podcast Becoming Data Scientist and Data Scientist at HelioCampus. Renee not only has a successful career, but she also does her best to inspire and help others through her popular podcast and blog.
If you are into data science at all, you should not miss out following her Twitter handle @BecomingDataSci where she shares great information, calls out the unethical material and also pushes out some really funny tweets into the universe. As she points out in the interview, she also has a Twitter handle @untappdpipeline where she calls out gender biases in the tech industry and shares resources that would alleviate those biases.
Like Maya Angelou said, “When you learn, you teach”.
SDD: Renee, your work is a true source of inspiration. Can you tell us a bit about how you got interested in this field and your journey so far?
Renee: Thank you! I have worked with data throughout my career. I found out that I was pretty good at relational database design while taking a course in the Integrated Science and Technology program at James Madison University, and I started my professional life off with independent contracting work that involved designing databases and data-driven websites.
Then, I worked as a data analyst at Rosetta Stone, generating reports on customer support activity and product usage, and at James Madison University, developing a SQL “reporting layer” on top of a database of alumni and fundraising data, and building tools to help the university reach out to the right constituents for fundraising efforts.
I enjoyed building reports and interfaces that helped people understand their business by understanding their data, and get things done more efficiently.
While I worked as a data analyst, I also got my masters degree in Systems Engineering at the University of Virginia.
In that program, I learned more in-depth mathematics and statistics, as well as topics like optimization and simulation & modeling, and took an elective in machine learning, which was my first introduction to data science.
I also started listening to data science podcasts. It was then, more than 10 years after finishing my undergraduate degree, that I started getting interested in data science as a career path. I got a job at HelioCampus as a Data Scientist in 2016, and have really enjoyed problem-solving with data in the higher education industry!
SDD: What is your advice for the Women in Data on how they can get started and make better progress forging careers in this field?
Renee: Most of the people I know who are Data Scientists transitioned into the career from other fields, like the physical sciences, social sciences, software engineering, statistics, and various analytical roles. Since my path into the field included a job as a Data Analyst, and I still use many of the skills I gained through that work now.
I recommend finding jobs that allow you to gain those skills and get practice with coding, data manipulation, and reporting tools, even if the title isn’t “Data Scientist” at first.
Building reports and dashboards that communicated summary statistics and trends to stakeholders is something I still do now to communicate results of predictive models. And software engineering skills are useful for setting up data pipelines and “productionizing” machine learning models.
So, though most courses for Data Scientists focus on Machine Learning, make sure you are also picking up other tools and skills, like SQL, an interactive dashboard development tool like Tableau, and strong communication skills.
Being able to communicate well to show that you understand the business context of the problem to be solved, and can explain your results in a way that non-technical stakeholders can understand what they mean is extremely important for Data Scientists.
SDD: What is the driving force behind your work for the Data Science community? What is your opinion on the misogyny and bro code that exists in the world of tech?
Renee: What drives me to do this additional work is that I know there are a lot of awesome people from diverse backgrounds who want to get into this field. And, there aren’t many resources out there that are encouraging for beginners and people transitioning into data science from various career paths. I want to cheer those people on!
I know from experience that the learning process can be challenging, especially if you feel like you’re on your own, learning online, or trying to get back into ‘learning mode’ when you’ve been out of school for a long time.
I learned so much from the Twitter community myself when I was first learning data science, and want to help grow that community. [Check out an Article – I wrote about using Twitter to learn Data Science]
On misogyny in Data Science.
Yes, there is a lot of misogyny and ‘bro culture’ in tech, and I think the worst issue is that there’s still a lot of denial about gender bias and racism in the workplace in general, and we can’t address the issue when many people won’t even acknowledge it is a problem.
I try to highlight examples and resources on my twitter account @untappdpipeline.
There are also a lot of people on social media, often (but not always) shielded behind anonymous accounts, who go out of their way to attack and discourage discussion of these topics, or otherwise make people who aren’t part of the majority groups feel unwelcome in tech.
I do hope that maintaining a generally positive presence on social media, and being a vocal woman in data science myself, that I can help encourage other women and people from underrepresented groups to work with data and bring their skills and knowledge to this field as well, and not be discouraged by the “gatekeepers” out there.
Namita: For those children who have an aptitude for technology, what are your thoughts on incorporating data science, ML and AI in our current education system?
Renee: I think incorporating data topics in general in education is important, as there is a lot of “data illiteracy” even among otherwise well-educated people.
Too many people freely give up their personal information (grant permissions to apps on their phones) in exchange for a free game, for example, and don’t think about who wants that data, or why.
Additionally, many people are fooled by statistics and visualizations meant to mislead them. More savviness with data and statistics can go a long way.
Increasingly, people are going to need to be savvy about Machine Learning and Artificial Intelligence as well, because it will soon surround us.
Our smartphones and websites are already making use of predictive models and responding to us in ways they never have before.
Fake images, videos, and audio can now be generated that is difficult to distinguish from the real thing. In order not to be fooled or scammed by automatically-generated tailored content, we’re going to need a population (and leaders and legislators) that understands how these technologies work, in order to be sensible about their use, know how they can do harm, and regulate them as necessary.
SDD: What keeps you going every day?
Renee: Seeing people online increasing their skills, achieving their career dreams, and helping one another inspires me. Seeing the incredible achievements people are making in science and technology inspires me. I’m also inspired by people in my family who are doing amazing work. I’m Christian, so I’m inspired by Jesus and the Gospel. Also, people who call out injustice in our world, advocate for change, and right wrongs inspire me, too!
You can also read this interview on the SHEROES app here. While you are there, don’t forget to drop us a hello at the She Drives Data community on SHEROES! We would love to hear from you ❤️️