Coffee With a Data Scientist: Lillian Pierson (aka Big Data Gal)
In the third issue of DZone's Coffee With a Data Scientist, we speak with Lillian Pierson, aka Big Data Gal. A well-known face in the industry, she heads up Data-Mania, a site dedicated to providing Big Data resources and services.
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In the third issue of DZone's Coffee With a Data Scientist, we speak with Lillian Pierson, aka Big Data Gal. She heads up Data-Mania, a site dedicated to providing Big Data resources with an active blog and several books under her belt, and services like live training sessions and 1-1 coaching. Lillian is constantly on the move but was kind enough to take some time out of her busy schedule and share a bit about her experience with DZone.
For those of you new to Coffee With a Data Scientist, our goal is to interview various data scientists and professionals in the field working on projects in machine learning, deep learning, data analytics, and/or big data in an effort to learn more about data science from the people who know it best. Oh yeah, and the coffee aspect of it all... we always like to offer our interviewees a coffee. So, if you're a data scientist who would like to share your thoughts on the subject and you'd enjoy a cuppa on us, please get in touch.
Lillian sips a freshly brewed coffee from a tiny DZone mug we reserve especially for those working in the field of Big Data.
DZone: Can you start off by telling us a bit about yourself—particularly something that your many followers might not already know about you? That is, if you can think of anything! :-)
Lillian: I got my degree and license in the Environmental section of Engineering, so my coding is all self-taught. I love traveling, so after college, I thought it might be fun to become an international patent attorney. I went so far as completing my first year in law school and getting a job in the field... when I figured out what the profession is like in practice, I decided to drop the idea and stay close to the STEM that I know and love.
You’ve become a well-known face in the industry with your Big Data Gal brand and Data-Mania website. However, your initial education was in Environmental Science and Law, correct? Can you talk a bit about what influenced your decision to learn more about data science and big data? And, when did you decide to make data science your primary focus?
First, I must inform you… like I wrote up above, I have a degree and professional license in Environmental Engineering — not science. I have never taken an Environmental Science class in my life. I actually started out my college career in Chemistry and spent two summers doing chemistry research in a computation biology lab, and using NMR analytics to reverse engineer my way into deuterating Hydrogen atoms in nucleic acids… but that is another story. Anyway, I have always had a proclivity for data… working as an engineer, I was the person who was using data to model systems. At a certain point, I got a job with local government and that role took me straight into data analytics, which quickly led me to data science — and I’ve been stuck like glue ever since.
What’s your day-to-day work look like with Data-Mania and what do you find to be the most challenging aspect of what you do? Can you tell us about some of the training and resources you offer, books you’ve written, etc.? Want to offer us a glimpse into what’s included in the member’s area?
Lately, I have been working on a deep learning course to follow-up my Python for Data Science course on LinkedIn Learning. I go on about 1 international work-related trip per month. I am also working on putting some bells and whistles on a high-level Big Data and Data Analytics course that I am scheduled to teach for 1-week in Kuala Lumpur. That’s a five-day, 25-hour course — so preparing and delivering it is always intense. I have written two small books and one full-length book — that’s called Data Science for Dummies. Wiley has been a great client for a long time, I must say. I am also working on getting my R Programming for Data Science course back up online, as well as putting up my Python Crash Course, and the Big Data and Data Analytics Course. On the side, I have coaching clients, speak at events, and raise my 11-month old baby girl. Lately, I have been very challenged by all of the different directions I feel like I am going with Data-Mania. That coupled with the need for reliable income on a regular basis has me seriously considering getting a J-O-B! I am looking into some remote roles that I can do from home on a part-time basis. That would be ideal!
I imagine that you’ve gotten to work with and meet lots of different interesting companies and individuals. What’s the biggest problem you observe in enterprise organizations that are trying to implement a data science strategy? How about startups or smaller companies?
Enterprise organizations often have legacy IT systems and personnel. Staff usually can’t get access to their data without needing to go through a chain of gatekeepers, and with security concerns lurking in the minds of most organization leaders, I don’t know when this problem will be resolved. A lot of what I have seen with start-ups is an idealism that’s not grounded in what’s reasonably possible. People have some amazing ideas, that might really add up to something cool—but they need training and experience with the data technologies and methodologies out there before they can start putting together any sort of feasible plan.
What specific software do you regularly use for your work and where do you recommend our readers start tool-wise in their quest to learn more about data science?
Software? LOL – I mean, I use the Office Suite. I use R… I guess R-Studio is a "software" and I use that. I do my Python coding in Jupyter. I know how to use Tableau, SQL, and GIS software, but I am not doing too much of that these days. If someone wants to learn to do data science, I usually recommend starting with Python. It is human-readable and it’s useful for much more than just doing data science. If you’re going to pick up a new language, why not choose one that has a broad range of applicabilities.
Do you think having domain-specific knowledge is necessary to be a successful data scientist?
A person can use data science methods to solve a very specific problem without much domain knowledge. But, to develop a strategy or to understand how incremental findings affect an entire system (whether that’s a business system, an oilfield network, or a telecom network, what have you), you need domain-specific knowledge.
What's your take on data science automation? Will it be a hurdle or an asset to upcoming data scientists?
What is the role of data visualization in data science and are there any specific tools or projects you would you recommend checking out in regards to data visualization?
As a trainer, expert, and data science coach, do you have any other advice for our readers that want to get more serious about learning data science?
Half the battle is in planning and organizing yourself to make the transition. The other, more difficult, half is in executing your plan. Focus, focus, focus… persistence pays off. There is no free lunch!
What is the future of data science in the next 5 to 10 years as you see it? Feel free to talk about how you think it will change the world and/or how the field of data science (and the profession) will change compared to how things are now, etc.
I fully expect more of the same; not enough STEM workers able to fill the demand for data science and engineering in the USA. I don’t think that problem is going anywhere.
We are ordering our first robot, to clean the window of our villa. Automation, bots, and improved quality of life for us all… We are already experiencing it, and I believe this process will only accelerate.
Aside from working in data science, what other things do you like to do?
I enjoy taking care of my baby, Thai massage, sight-seeing with my family (we travel together most of the time), and going shopping at various duty-frees around the world!
Had you ever come across DZone previously? As an expert data scientist, what are your suggestions for improving our coverage of Machine Learning and Data Science to meet the needs of data professionals?
Yes, of course. DZone rocks! I particularly like the recent re-branding efforts. It’s good to keep fresh. If you want to meet the needs of data professionals, you could offer more in-depth technical tutorials (not that easy to source, I understand), or you could continue to promote the work of fun data scientists, as a way to inspire professionals on their path forward.
Is there anything I haven't asked you about that you'd like to add? (Big Data projects you’re working on or following? Interesting happenings in Machine Learning that you want to mention? Shout-outs to give? etc.)
will be was at IBM’s Fast Track Your Data Event in Munich on June 22. Join me there!?!?! *Writer's Note: Sorry Lillian, I was a little late on publishing this!*
Thanks for the interview, Lillian.
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