DataHack Radio #4 – Data Privacy, Women in Data Science and More with Carla Gentry

Pranav Dar 21 Jan, 2019 • 4 min read


Carla Gentry is one of most popular social media influencers in the data science field. She has over 300,000 followers on LinkedIn and 48k followers on Twitter. Her experience in this field is unparalleled and we are grateful for leaders like her who consistently give back to the community.

She has been a regular reader of Analytics Vidhya’s articles for quite a while now. It was a pleasure to have her appear on the podcast and to hear her views on data privacy, how the domain has changed in the last few years, her advice for women in data science, and a whole host of other topics.

This article contains highlights of Carla’s conversation with Kunal Jain. You can listen to the podcast by clicking on the above SoundCloud link or on our iTunes channel. Happy listening!

You can subscribe to DataHack Radio and listen to this, and all previous episodes, on any of the below platforms:


Carla’s Background

Carla holds a number of degrees including one in advanced economics, one in advanced mathematics, among others. Right after college she got an internship at one of the few econometrics firms in the United States.

There she worked with terabytes of data (this was well before ‘Big Data’ was a buzzword). Her experience started with tools and platforms like SAS, Pico, etc. Her role was working with credit card data but the nature of the work was such that it became monotonous after a while.

From there, she moved to the Weinstein Organization as a Senior Analyst and Data Specialist. Here Carla’s role expanded to include direct marketing experience. This was followed by a year at the University of Chicago Booth School of Business as a Research Support Analyst where she taught Ph.D students how to work with data, how to do data mining, etc.

Carla then moved on to work as the Marketing Information Manager at Career Education Corporation for the next 4 years. Post that she spent the next 3.5 years in the marketing and data field at PromoWorks, Tandus and Area203. She is now the owner and data scientist at Analytical Solution, where she has worked with clients like Kellogg, Johnson & Johnson, among various other organizations.


The Importance of Data Mining and Dealing with Bias in the Data

Carla considers data mining a critical aspect in any data driven project. It helps you understand trends, see patterns, interpret reasons why a person was denied a loan or credit card, etc. It’s at the core of the business and should be respected as such.

But she stressed on the importance of privacy and the need to be clear with your clients and customers about where you are going to use their data, for what purposes, and how it might impact them (if at all). GDPR has of course changed the game in Europe with regards to being transparent with users but Carla feels everyone, regardless of laws, should have this as a best practice.

With the amount of data that’s being generated in the world, from websites to social media, it’s critical to have that level of sensitivity.

Data scientists have a responsibility to be unbiased, have integrity and use their experience to add a positive background to the dataset, rather than let their feelings cloud the model building exercise.


Changes in the Last Few Years in the Data Science Domain

Carla recalled that if a business had terabytes of data in the 90s, running a program on that was next to impossible because the mainframe would have crashed. Now a mainframe isn’t even required! If you have a good database architecture set up, you can access millions and billions of rows in a fraction of the time it used to take previously.

COBOL, PASCAL, C++, SAS, Mathematica, MATLAB – these were the only programs available back when Carla started her journey. Of course now we have much more robust tools like R, Visual Studio (SQL), IBM’s suite of tools, etc. One of the biggest reasons why the older programs have been phased out is because of their inability to handle gigantic datasets, which the new tools can.

Of course with the rise of this data wave, and the advent of the digital era, the number of hackers and cyber thieves has also risen. So as things have gotten better in many ways, they have also gotten worse when it comes to security of your data.


Changes to be Expected in the next Few Years

Carla expects more laws like GDPR to come to action in the next few years that will dictate how organizations collect and deal with your data. She has a warning for businesses that abuse data – people will leave and look for ways to go incognito, which will leave your business with no data at all.


Women in Data Science

“We have got to get rid of the thinking that it’s always been this way, so it should stay that way.”

Carla is a champion of women in data science. She strongly believes that in order to incorporate more females into this field, the change has to start from the top. The CEO should have an obligation to encourage diversity into the organization by going to the HR department and digging deeper to understand the percentage of women in the company, and how to further improve upon that.

Her advice to aspiring female data scientists was to the point – stand your ground, be confident in yourself, find mentors, keep going and keep learning. You will find the perfect fit for you as long as you continue to believe in yourself and your abilities.


Social Media

Carla is an avid social media user and a HUGE influencer, especially in the data science field. It’s very time consuming (2-3 hours per day at times) but if what she shares helps even one person, she definitely considers it worth that investment. She feels it’s her responsibility to give back to the community, given that she has gotten so much from it over the years.

There are some awesome rapid fire questions at the end of the podcast – ensure you listen to those as well!


*P.S. – All views expressed by the guests on DataHack Radio are their own, and not of Analytics Vidhya.

Pranav Dar 21 Jan 2019

Senior Editor at Analytics Vidhya. Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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