Is there a sector with better job prospects than Data Science? It’s unlikely. Virtually every company now relies heavily on data analytics software – which requires data pros to use most effectively.
In this Webinar, we’ll discuss:
- How to get started in Data Science — without a four year college degree.
- Some of the most meaningful and lucrative career paths in Data Science.
- General tips on building a career in Data Science.
- The remarkable future of Data Science as a career option.
Please join this wide-ranging discussion with a top leader in the Data Science sector, Kirk Borne, Principal Data Scientist, Booz Allen Hamilton
Download the podcast:
The Education Needed for a Data Science Career
Getting started in data science without a four-year college degree
- You don’t need that, actually. I think I have to focus on a two-stage process here. The first step is just getting your foot in the door, which starts by just learning the skills of data science, the coding, the algorithms, the techniques, the methods, the process, all these things.
- But if you’re really going to have a long-standing career, I always say that a degree gives you that extra career of padding, that is when the organizations look to promote people, maybe to leadership positions or whatever, it’s not just that you happen to know some coding skills, there’s a lot more that goes into that, and that comes with the things you learn in formal education programs, which are outside of the sciences, right?
- The PhD is a research degree. Okay, so if you want to be a research scientist, that is where you want to go, but most data scientists aren’t gonna be research scientists, and by that I mean, you’re actually publishing papers in research journals, peer-reviewed journals, peer-reviewed conferences, probably at an academic institution trying to get tenure.
- But if you want to have a successful data science analytics career, I’d say having a master’s degree teaches those professional skills of communication, leadership, collaboration, the things that go beyond just the academic stuff you learn in bachelor’s and beyond, and different from the research things you learn in a PhD program.
- So I say, yeah, you can get into this field right away without a degree, but for the long-term career success, think about the collegiate education as well.
Getting started with a certification program
- The number of jobs opening is far and excess than the number of people available. Once you’ve certified in any of those things, you’re gonna get a job.
- I know a number of data scientists who’ve gone on to become founders of companies, so they’re sort of managing a company now. So, a master’s in business analytics is a pretty impressive thing to have under your belt as well, because then that business analytics gives you both the analytics and the business experience.
- But I do wanna say yes, there are certification programs. [There’s the] certified analytics professional, the CAP certification, but there’s also lots of boot camps. So boot camps can teach you skills like in 12 weeks or 16 weeks that will get you the job.
- There’s also master’s, there’s a lot of master’s degree programs that are basically 11-month programs, so you get the full master’s degree, but it’s a full-time job, you can’t have a job or a life, pretty much for 11 months.
- And master’s programs are different from certifications in that any college degree programmers require state accreditation, and they have to meet certain minimum standards, like 30 credit hours, certain number of courses. And whereas at boot camp, you just take a boot camp in Python and you can get their Python certification and go get a Python job, there’s no sort of state university regulation over a boot camp.
Or are there certain specific career tracks you think are really attractive and particularly in-demand?
- I remember sort of when this data science revolution started like seven, eight years ago, 2012 time frame. There was quite like a burst of activity around this for several reasons we can go into, but I remember there was a job description that I saw that it required a lot of sort of engineering skills, building the data system and building out the whole machine learning modeling apparatus of their organization. So it wasn’t just coding, it wasn’t just being a data scientist where you play with the data and you hypothesize what models work best and you tweak the models, it’s actually building the thing and putting it into production and keeping it in production for a very, very large customer base. It was a very large e-commerce company, very, very large e-commerce company that it was advertising this position. Lot of job requirements, and I was sort of reading this ad, then I got to the bottom and it said salary, and at the bottom of the job, after the word salary. It said, “anything you want.”
- So anyway, so I think in terms of specific jobs, the AI engineer, machine learning engineer, cloud engineer, they surpass data scientists in terms of lucrative sort of salary. and the reason I say that is because these are the people who actually have to build it out, deploy it, put it in their production, keep it running. Data scientists are also well paid and you can have a really nice satisfying job as a data scientist, but most of the time you’re building models, you’re playing with data, tweaking with data, exploring data, finding the right algorithms. And that’s fine, that’s great, and that’s sort of what gets the foot in the door towards business value creation from the data, which is really what my message always is, is focus on the business value creation.
- When it’s deployed, put into production and maintained, and that’s where the AI engineer, machine learning engineer, cloud engineer, is gonna be the person or team of people who is going to accomplish that. So everyone has a value in the chain, but that engineer who’s gonna deploy, build, and keep it in production is the one who can say, “I’ll take any salary I want.” [laughter]
- So if you’re gonna build that [extensive deployment], you have to have a way more capability than your traditional data scientist, but, nevertheless, people are being hired as AI engineers and machine learning engineers because they’re being hired to do what data scientist’s job, which is to explore the data and build models from the data. And the job title doesn’t really match what I would call the data scientist job, and vice versa.
Graphic: How to be a Data Scientist
- A few years ago, I starting thinking of sort of the key skills or soft skills… I should say aptitudes, not really a skill, soft skill of a successful data scientist, and things like being curious and creative and critical thinking, collaborator, communication. I started thinking, “Oh, those things all start with the letter C.”
- But I think for sure, being a curious person, I mean, I can just say, for example, we had students in our PhD program at the university who, in some cases, were not curious people. I can just say it bluntly. That is, they just… When they put together a proposal to do a doctoral dissertation, it was really, “I wanted to build this software system to do data science.”
- But the one we’ve already hit upon is this continuous lifelong learning, I mean for me that’s super-duper important. But another big important one, which you may not think of, is number 10 on this list, which says “consultative.” If you’re doing data science for a company, an employer, a stakeholder, whoever, you have to be able to communicate. Not just communicate, but listen to what they’re saying and ask the right questions to make sure you build the right system, so that’s really a business focus.
- The principal of system engineering is, there’s a difference between building the system right and building the right system. So they followed the letter of the law and the requirements document, and they built the system right, but it was completely not functional for science and the research needed. It didn’t build the system that scientists would want to use.
- It’s hard to even say exactly why. My first day on the job, I worked at the Hubble project 10 years, and it was in my seventh year that I got appointed to be this NASA project scientist for the data archive. And my first day on the job, the previous guy who was the archive project scientist handed a big box to me, about the size of a typical Xerox box full of reams of paper. Literally thousands of pages. There was a lot of discussion on the system requirements and the functional requirements, but if you know anything about user experience and design thinking, no one was talking user experience and design thinking 30 years ago. [chuckle]
- Oh, that’s another one of the Cs on my list there, was that compassion. Again, the forced letter C there, meaning more like empathy, that is being able to understand that you’re dealing with users of this thing you’re building, and if it’s opaque and not understandable and uses complex terminology to explain it, you’re not being very empathetic with your end user. [chuckle]
Questions from Viewers: A Data Science Career
Sara asked, “What is the future of data science in the industry?”
- Yeah, I’m looking for my crystal ball right now, let’s see…[laughter]. I think as time goes on, we’re just seeing the data science is more being blended into organizations. There was a time where it was sort of like a side project or the team was off to the side, “Here’s our data science team.” But for one thing, I think there’s gonna be some data democratization that has to happen. [There are ] two aspects of the culture. One is a culture of experimentation, that is being able to test data for patterns that might give business insight for better actions and decisions, so culture of experimentation. And the other is a culture of, if you see something, say something. So where have we heard that before, right? [chuckle]
- If you’ve ever been in the New York City subway, you see the signs everywhere, “If you see something, say something.” And the same thing with data. If you see something, it’s “Oh it was not my job. It’s someone else’s job”. No, we are… If we’re a digital organization, if we are undergoing digital transformation, then we all need to be empowered to work with and learn from and take actions from digital data.
- Anyway, so I think data science, the future of is we’ll see less of it emphasized as data science and more in terms of its other dimensions, which the applications, like machine learning and AI. Well, AI being the application, machine learning being the technology for the actual implementations, which include cloud and other things. So we’ll start seeing more focus on those, but we’ll still be doing data science. We just may not use that word to describe the job title.
Vladimir asks, what subject to study first perhaps, and which one is previously required? He mentions machine learning, AI, data mining, deep learning, supervised learning, big data. Should you study one of those first?
- So essentially becoming immersed in data first. At that point you sort of… If you don’t catch the bug there, you’re not gonna catch it at all, ’cause once you get immersed in data, you realize there’s power, there’s patterns and trends, and correlations in data. So once you get that experience, then I’d say first thing to look at is unsupervised learning, because unsupervised learning is basically just finding the patterns in the data without any regard to any preconceived notion of what you’re looking for. Now, supervised learning is specifically designing algorithms that can diagnose or predict an outcome based upon training data. So you could start there too. A lot of people do start there because they feel like, “Hey, I can predict the future.” [chuckle] So supervised learning, it gives you a rush because you’re actually predicting something pretty cool.
- But the power of unsupervised learning is it teaches you the techniques of data exploration, data wrangling, data prep, data transformations and concepts of trend analysis, correlation analysis, clustering analysis, some of these things that are gonna matter to you when you start getting to the more advanced things. So I think just that immersion in data and exploration of data helps build, first the passion and the desire to do more.
Kashoor asks, what is the best tools you would suggest for data science?
- Yeah, yeah, it’s not really easy to answer that because there’s so many thousands out there. There are websites that do a compilations of surveys of what data scientists recommend, and so… KD Nuggets. If people are not familiar with KD Nuggets, they should check that, kdnuggets.com has been around, Greg Shapiro started that you know in 1980s.
Felipe asks, What is the future of data science in developing countries?
- Yeah. Absolutely. I’m actually a keynote speaker for a conference in Peru at the end of July. I’m giving two keynote talks, one on AI and one on data business analytics basically. So Peru is really ramping up. I know that in Africa, it’s an enormous activity going on right now, a lot of activity in Nigeria. So even a few years ago, I was invited to the South African Embassy in Washington DC, which is a very moving experience because it was the week that Nelson Mandela died. It was really an emotional experience, but they’re talking about the importance of data analytics to basically rise up the whole South African continent in terms of agriculture, economics, and business, and healthcare, and medicine, and so on, and so on. Just how the power of data to inform, to inspire, for innovation and insights… It was just really impressive, and so I don’t think anyone is gonna be immune from the benefits of this if you just go after it.