We had an opportunity to talk with Lillian Pierson, the founder of Data-Mania, about her work in data science – and her latest book “Data Science for Dummies.” This interview is part of our #DataTalk series.
Lillian, when did you fall in love with data science?
I have always been more interested in the results I can get through using data science rather than the science itself. To me, data science has always been more of a means to an end.
The first time I had the experience of using analytics to help me achieve what I wanted, I was 17 years-old working on duetertation reactions of nucleosides… it was a form organic chemistry within a computational biology context.
Although I had not taken organic chemistry at that time, I could achieve my goals by using NMR data analytics to show me what was working and how. Then, I could read up on the physical mechanics of the reaction and tweak my approach to achieve more of the desired result.
In my opinion, there is nothing cooler than data science and analytics in action. You can achieve almost anything!
Was data science something that came easy for you? Any aspects that you struggled with early on that you now help others on?
Yes, data came very easy. My very first job was in data. Also, computers came easy to me – which makes sense, because I grew up in the 80s and we had a computer at home that I could mess around with after school and on the weekends.
As for the areas I have struggled with in data science, I’d say statistics has been my weakest point.
As an engineer, I naturally take a mathematical approach to problem solving, rather than a statistical one. Although I took Stats for Engineers, I sort of feel like I had to play catch-up with statistics.
Honestly, most people except Statisticians have not taken more than one class in statistics, but it is important in data science. That is why I developed it as my first course offering at Data-Mania.
When did you know you wanted to work in data science – and what led you to want to teach others about it?
As an engineer, I was always being called upon to analyze and model design scenarios using our chosen design parameters. This was part of any system design. I was also asked to use statistics to determine whether pilot study approaches had been effective or not.
As an environmental engineer, I did a lot of spatial data analysis and modeling. The design work component of the job, with the drawings and measurements, was just super boring to me. So, I moved into a position where I could focus on spatial data science – stuff that I found fascinating.
Once I was working in that capacity, I got into data analytics system design, and then custom scripting work. All of that was super fun. I was doing all of this without even realizing it was data science. Data science is a label that came later.
I want to teach people how to do it for themselves because:
1. I believe there is a real need for people to become data literate in order to stay relevant in their field, no matter what field that is – people are going to need to know this stuff in the future.
2. I see a lot of programs out there that, while valuable, are quite excessive. There is no need to spend $60k and two years in school to learn what you need in order to begin using data science.
A few basic skills are enough to get going and you can pick the rest up as you go. I wanted to offer people a place where they could quickly and affordably get the skills they need to get going. That’s what I built.
Tell us about “Data Science for Dummies.” Who did you have in mind while writing this book?
This book is for data scientists. If you’re already working in data science, then you might have doubts on whether this book is for you. Let me put your mind at ease. As you’re aware, data science is an incredibly wide discipline. It’s close to impossible to master every area.
More often than not, data scientists are strong in certain areas of the field, while relatively naïve about others. This is where “Data Science for Dummies” is helpful to the practicing data scientist.
This book overviews each area of data science, in easy-to-read language, and tells you about what specific goals you can achieve by taking on other areas of the field. The book is an overview of the space. Let it serve as a road map while you develop your professional expertise.
This book is for novice analysts who want to get started in data science – So you’re data-curious and would like to know the best ways to get started, eh? The good news is that data science is a very wide field, and you might already have a head start in at least a few areas of the discipline (even if you don’t know it now).
This book defines the space. And it’ll show you where to best focus your energies in order to get started mastering the areas about which you’re most passionate.
Data science skills are useful and valuable. Building even a few strengths in data science is likely to make you a much more marketable employee. And if you’re an entrepreneur, like me, you’ll probably love the chapter on data science for ecommerce growth!
This book is for business managers and decision-makers – If you’re a business manager, then you’ve probably been curious about how data science and advanced analytics could work to improve your business.
This book provides a plain-language, results-oriented description of areas that comprise the data science field. I made sure to get straight to the point throughout this book, focusing on the purpose of each technique and the benefits that you will reap by incorporating these.
Taking complex ideas and making them accessible to newbies (like me) takes mad skills. How did you decide what topics you would focus on in this book?
That is a true statement! Gosh, I decided what to cover in the book by:
1. Taking an overview of the entire space.
2. Making sure to get all of the major bases covered – despite the fact that they are super technical.
3. Adding in chapters on less technical stuff that would be easy for newbies to understand and/or begin using. These chapters are the ones on free tools and apps, or the ones that describe how data science is being used to solve problems in ecommerce, the environment, criminal science and journalism.
I tried to include enough basic information to keep the book interesting and compelling for newbies, while enough technical stuff to make the book useful to practicing data scientists who are exploring new approaches in the field of which they may not have been aware.
One area that frightens many of us (especially us English majors) is learning how to code. How should we think about coding? What languages do you recommend people learn?
Gosh, I get that. It could be scary. I guess, to an English major, I would say that you may want to think of it like learning a language that you can use to tell a machine what you need it to do for you.
Just like in English, there are structural forms, syntax, and other rules. It’s really a matter of picking up one those in the beginning.
Honestly, the best place for an English major (or anyone really) to begin would be Python. It’s human readable, which means the code actually makes sense in English. It’s really not that hard to learn and is quite useful in data science.
We read these stories about teenage computer whizzes who build startups in their bedroom. Some people seem like naturals when it comes to computer science and data. Is there hope for us on the outside – and older? Where do we start?
Yes, like I was saying, you just need a few basic skills to get going. The more important thing that you need is a sense of determination and curiosity.
If you have a goal and are determined to reach that goal, by whatever means necessary, then you will pick up what you need to get you there.
Contrary to what some people will have you believe, this stuff is all pretty intuitive. A sense of determination and fearlessness can get you where you need to go, but you will need to pick up new skills, techniques, and methods along the way.
You can’t be afraid of a little learning adventure and experimentation. Also, you can’t let the more advanced skills of others intimidate you into thinking you are less than.
Subject matter expertise is a huge and vital component of data science.
In your introduction, you write “A lot of times data scientists get so caught up analyzing the bark of the trees that they simply forget to look for their way out of the forest.” Can you elaborate on this?
This goes back to the point I just made about keep your work goal-centric.
In data science, there are many ways to achieve the same or a similar result. If you go in there and get caught up on looking to deeply into the minutia, you can easy lose sight of the end goal.
This comes more down to process design. You set a goal and then, with that goal in mind, you build each part of the system that is required to get you to that goal.
If you don’t keep your end goal in mind, it is very easy to build sub-components that can’t get you to the outcome you need, or that don’t answer the questions you need (but do answer other questions that seem to get interesting along the way).
Don’t get caught up in the all the glitter and gold along the way. Stay focused on only what you need and want from the adventure.
Many of us don’t work in data science roles – but could probably do better jobs if we were more familiar with how data can be leveraged – and analyzed. What types of professionals can really benefit with a better knowledge of data science?
I agree. Knowledge-workers of any and every discipline need to become data literate.
I am not saying they need to become advanced statisticians or software engineers, but they need to understand how they can generate and use data insights to improve their performance, make their work processes more efficient, and get “better” results in return for the time and investments they are using.
Any last minute tips for those interested in learning about data science?
Yep. Honesty, I would suggest people just to start reading online about data science – what people are doing and how they are doing it. Let that information guide you deeper into what you want and need to know for yourself.
Lillian, where can everyone learn more about you and the work you do?