Episode 179: A career in data science: what is it, who is it for and how do you get into it? – with Nicholas Cifuentes of WorldQuant University

Data science is a rapidly growing field, it’s a well-paid profession and it’s in high demand.  But what does a career in data science really look like? 

Nicholas Cifuentes-Goodbody teaches several data science boot camps and is the Chief Data Scientist at WorldQuant University. 

He explains what it’s like to be a data scientist, what the day-to-day role of a data scientist is, and how people can transition to a career in data science. He also talks about the different ways to learn data science skills, character traits that successful data scientists share, and the threats and opportunities artificial intelligence presents.

Today’s guest

Nicholas Cifuentes-Goodbody of WorldQuant University

Website: WorldQuant University

LinkedIn: NC Goodbody

YouTube: Nicholas Cifuentes-Goodbody

Nicholas Cifuentes-Goodbody has taught English in France, Spanish in Qatar, and now Data Science all over the world.  He completed a PhD at Yale University and went on to work as a professor and administrator at Williams College, Hamad bin Khalifa University (Qatar) and the University of Southern California.  After pivoting to a career in Data Science and teaching at several bootcamps, he is now the Chief Data Scientist at WorldQuant University.  There he built and leads the Applied Data Science Lab, which serves thousands of students around the globe.  He also runs data operations for the university and is focused on finding innovative ways to use data science tools to improve online learning.

What you’ll learn in this episode

  • [1:45] What WorldQuant University is and the types of programs they run. 
  • [3:42] How a free university is possible in America.
  • [4:13] The motivation for a hedge fund to run a free university. 
  • [4:50] What data science is and how it’s used. 
  • [6:03] The biggest employers of data scientists. 
  • [8:21] What a typical day as a data scientist is like. 
  • [10:12] The different specialities within data science. 
  • [12:08] What it means to be an AI engineer. 
  • [12:40] What qualifications you need to become a data scientist. 
  • [14:43] The level of education you need to become a data scientist. 
  • [16:27] Character traits that successful data scientists share. 
  • [18:10] The amount of nerds working in data science. 
  • [20:08] Why musicians become data scientists and doctors. 
  • [21:15] How to transition your career to become a data scientist. 
  • [27:37] What you need on your resume when applying for a data science role. 
  • [29:07] The best ways to learn the skills you need to become a data scientist.  
  • [30:29] How to edit your CV when applying for a data science job. 
  • [31:55] How to find a good data science boot camp. 
  • [33:51] The base knowledge you need prior to joining a data science boot camp. 
  • [36:30] The income potential of a data scientist. 
  • [38:17] The career path of a data scientist. 
  • [39:28] The impact AI will have on data scientists. 
  • [42:12] The ever-changing nature of data science.

Resources mentioned in this episode

Please note that some of these are affiliate links and we may get a commission in the event that you make a purchase.  This helps us to cover our expenses and is at no additional cost to you.

Episode 179: A career in data science: what is it, who is it for and how do you get into it?

Jeremy Cline 0:00
When I look at the careers discussion forums, one of the types of careers that comes up repeatedly is data science. What is it that makes this career such a hot topic? Why does so many people want to get into it? What even is data science? That's what we're going to talk about in this week's episode. I'm Jeremy Cline, and this is Change Work Life.

Jeremy Cline 0:36
Hello, and welcome to Change Work Life, the podcast that's all of our beating the Sunday evening blues and enjoying Mondays again. If you want to know how you can enjoy a more satisfying and fulfilling working life, you're in the right place. It's not often that we talk about specific careers on the podcast. But sometimes there's one which I see talked about so much that I want to find out more. Data science is one such career. So, this week, we're going to find out a bit more about what data science actually is, what a career in data science can look like, and the threats and opportunities artificial intelligence might present. To introduce us to the world of data science, I'm delighted to be joined by Nicholas Cifuentes-Goodbody. A former language teacher, Nicholas is currently Chief Data Scientist at WorldQuant University, where among other things he finds innovative ways to use data science tools to improve online learning. Nicholas, welcome to the show.

Nicholas Cifuentes-Goodbody 1:32
Well, hey, thank you so much, Jeremy. I'm really happy to be here.

Jeremy Cline 1:35
Now, I'm guessing a lot of people probably won't have heard of WorldQuant University. So, why don't you start by telling us a bit about it?

Nicholas Cifuentes-Goodbody 1:43
Absolutely. Thank you for that opportunity. So, WorldQuant University is a free online university. We offer two different programmes. We have a master's in financial engineering, and we have a certificate programme in data science. And the idea of WorldQuant University is that there are talented and hardworking people everywhere, right? There are smart people everywhere. But unfortunately, opportunity is not everywhere. And so, how can we leverage online education to open up new opportunities for hardworking learners all over the globe?

Jeremy Cline 2:17
And when you say it's a university, I mean, I actually have no idea what it takes for a university in the UK, let alone the US, to be able to call itself university, so what is its status, if you like, in the US?

Nicholas Cifuentes-Goodbody 2:31
Yeah, so to be a university, you first need to register in one of the states with the Department of Education in that state. So, we're incorporated in Louisiana. And then also, there's a national process of accreditation that you go under with an accreditation body. And we've done that as well. And then, you register with the Federal Department of Education, and then you're ready to go. And then, they finally give you that .edu website you've been wishing for. So, that's what we've done. And so, we're a fully accredited online university.

Jeremy Cline 3:04
All right, so someone who goes through your process will get, by the end of it, a degree, which is equivalent to a degree from Harvard, Yale, Stanford, those are the universities I've heard of in the US.

Nicholas Cifuentes-Goodbody 3:18
Well, I'm glad that you're putting me in such a good neighbourhood with those other universities. I'm very impressed. But yes, if you join our master's in financial engineering, you will get a master's degree, an MSc in financial engineering. And then, our certificate programme is, you get a Credly certificate.

Jeremy Cline 3:35
And how are you able to offer this for free, given that my understanding is that US universities charge rather a lot for their programmes?

Nicholas Cifuentes-Goodbody 3:42
It's true. We're a little notorious for that. Although, I should say that UK is not far behind. Let's be honest.

Jeremy Cline 3:48
That is true.

Nicholas Cifuentes-Goodbody 3:49
So, what I would say is that we have WorldQuant University, but you have also may heard of WorldQuant the global hedge fund. So, we're a non-profit venture of WorldQuant the hedge fund. So, that's where our main funding comes, and from our founder, Igor Tulchinsky.

Jeremy Cline 4:10
And what's the motivation of the hedge fund to set this up?

Nicholas Cifuentes-Goodbody 4:14
Well, the motivation of the hedge fund is that, that's a good question, but basically, it's a non-profit, so the idea, the motivation is exactly what I said, right? It's like how can we open up avenues of opportunity to folks, and how can we bring the sort of the quant sciences to places where they haven't been before? So, you know, it's just a non-profit venture.

Jeremy Cline 4:38
Interesting. All right. So, let's get into basics. In simple terms, explain it to me like I'm a five-year-old, what is data science?

Nicholas Cifuentes-Goodbody 4:50
First, I have to say that you look very mature for a five-year-old, but I'll see if I can do my best. So, what is data science? Data science is the idea that we are surrounded by more and more data, right? Whether it comes from websites, whether it comes from Internet of Things, sensors, whether it comes from survey data. And so, what we need is, we need people who are capable of being stewards of that data, people who are able to analyse it and manipulate it in ways that provide insight and answers for people who want to use that data to achieve some sort of mission. So, whether it's a non-profit that's trying to analyse sexual harassment data in the Egyptian public transit, or whether it's somebody who wants to maximise their business sales, right? Data science is the practice of using data to answer questions in really all aspects of the private and public sectors.

Jeremy Cline 5:56
So, who employs data scientists? Who has the data science departments?

Nicholas Cifuentes-Goodbody 6:03
Almost anyone can have a data science department, but you'll find it in universities, under sometimes institutional effectiveness, you'll find data scientists in HR departments, where they're trying to analyse worker data, you'll find it in lots of online companies, where they're analysing data from their website, and maybe they're trying to optimise the search on their website, or maybe they're trying to build some sort of predictive model, decide who gets coverage for their auto insurance or not. And so, really, you'll find this everywhere. Anywhere where somebody has a lot of data and a lot of questions, that's where you'll find us.

Jeremy Cline 6:43
Okay. So, a friend of mine works for one of the big supermarkets over here, and he explained to me how his job is making sure that they've got on the shelves the stuff which they know they're going to be able to sell, and they might look at seasonal variations or big events, or that kind of thing. Is that, essentially, data science?

Nicholas Cifuentes-Goodbody 7:04
So, that sort of supply chain work might be a good place for a data scientist. You might also find them seeing, let's see here, what else would a data scientist do in a supermarket? You might also find a data scientist working with how to best and most efficiently circulate coupons to customers of a supermarket, so that they can more appropriately, we have overstock of a certain item in this particular area, how do we send out coupons to buyers in that area, so that we can balance that supply and demand? So, yes, that is one instance of how a data scientist might do their job.

Jeremy Cline 7:43
So, it sounds like pretty much anything where you want to make some kind of an evidence-based decision, then data science is what you need.

Nicholas Cifuentes-Goodbody 7:54
I really love, I think that's a good way to say it. I think the key word in data scientist is a scientist, right? In the sense that we have hypotheses, and we are checking them against the data, and then drawing conclusions from that rigorous process. Yes.

Jeremy Cline 8:13
So, someone who is a data scientist, I mean, what could a day in the life look like for them?

Nicholas Cifuentes-Goodbody 8:20
Usually, a data scientist is spending their time drinking coffee waiting for their model to train. That's what we sort of say as our joke. But a data scientist would be doing, so what would a data scientist be doing day to day? Well, the first thing that a data scientist would be doing would be talking to stakeholders, right? So, talking to the CEOs, talking to the VPs, talking to people in different parts of the company, to figure out what their needs are. So, whereas we think of this as a quantitative science, a lot of this is communicative. So, there's a big communication component. And from there, you might find a data scientist trying to operationalise or trying to take the needs and develop them into questions. So, what is a question that we can ask the data that will help you get what you need? From there, you might find them poring over spreadsheets, or kind of the Python programming equivalent of spreadsheets. And there, you might find them building models, so a predictive model that will tell you who might bounce from your subscriber base from one month to the next, or who might be worthy of extending credit to, or who was not a great bet. And so, you might have them building these sorts of predictive models. And then finally, they would be circling back to communication. So, once you've built that model, once you've done your data analysis, how can you explain what's happening with your analysis to those stakeholders, so that they get the insight they need to make a decision to move forward? So, that's what you'll see a data scientist do, doing a lot of talking to people, doing a lot of dialoguing with numbers, and building things. like predictive models to help companies or private organisations get what they need from their data sources?

Jeremy Cline 10:08
And are these roles typically done by the same people? Or do you have those data scientists who specialise in communicating with the stakeholders, and then those who specialise in building the models and crunching the numbers?

Nicholas Cifuentes-Goodbody 10:25
It really varies a lot. If you're in a small company with one or two data scientists, they'll probably be doing everything. Oftentimes, you'll see data scientists embedded within different units in a company. So, there'll be maybe one data science for HR, for example. And in that case, they'll pretty much be doing everything. However, there are places where they have larger data science teams working together. And in that case, you will see a division of labour. So, for example, you might have a data analyst who's building a data dashboard. You might have a machine learning engineer, who's taking a model and trying to deploy it so that somebody can make API calls on their website to that model. And then, you might also have a data engineer, who's making sure that that model has the actual data that it needs, moving it from one database to another, making sure that it's undergone the right transformations. So, you will see some specialisation within the data science field, but it can be just as common that somebody's doing a little bit of everything.

Jeremy Cline 11:34
And are those terms that you used typically quite well recognised in the industry, or do you really have to check what someone means? So, if someone says a data engineer, then someone else might actually interpret that as a data analyst or something like that?

Nicholas Cifuentes-Goodbody 11:49
I would say, if you're talking about data analyst, data engineer, machine learning engineer, all of those are pretty well understood, although it's always good to ask. Data scientists like to ask questions, so you should definitely ask questions. The one where we're beginning, the one job title where we're seeing it emerged now is AI engineer, and that's a little more amorphous. But generally, it's kind of what machine learning engineer does, which is they take large amounts of data, they build models, and they try to deploy them into production environments. So, yes, I would say that it's pretty well divided. There's a clear taxonomy throughout the industry.

Jeremy Cline 12:31
So, what qualifications typically does one need in order to start the journey as a data scientist?

Nicholas Cifuentes-Goodbody 12:39
That's a great question. So, a data scientist will usually have expertise in three areas. They'll have some mathematics training, so that they can understand the models that they're working with, and they can manipulate those numbers. They'll also have some statistics training, so that they can perform statistical tests. So, mathematics and statistics. And then lastly, they'll also have some programming chops as well, generally, in a programming language called Python, although not exclusively, there will sometimes be a programming language called R. And of course, a data scientist needs data, so they need to be able to get data from databases. So, they'll probably know some database languages, like SQL. But basically, those are the three big ones: math, statistics, and programming. And you need to know a little bit of each of those, kind of enough to be dangerous in each of those fields, you don't need to be a super specialist. And from there, you can jump off into the world of data science. Now, that's not exclusive. For example, there's a whole branch of data science called natural language processing. So, you may be heard of Chat GPT and those large language models. These are the people who are building those large language models. And they might also have a background in linguistics and computational linguistics. But what I would say, actually a data scientist described this very well to me, a data scientist is somebody who tries to do something with a computer, gets an error message that they have no idea what it means, and yet they need to fix it by the end of the day. So, anyone who's kind of willing to dive in, solve problems, and learn on the fly, that's sort of what you need to be a data scientist.

Jeremy Cline 14:27
If anyone who's listening to this hasn't heard of Chat GPT, please do email me and let me know, because I will be astonished if there's anyone at the moment.

Nicholas Cifuentes-Goodbody 14:37
I know. We're in a very big hype cycle with Chat GPT.

Jeremy Cline 14:42
What sort of level do these skills need to be taken to? So, math, statistics and programming. I mean, I did all of these up to what we in the UK call A level, which is basically up to age 18. And then, from that I went and did a law degree. So, what level of qualification can you expect data scientists to need?

Nicholas Cifuentes-Goodbody 15:05
Right. So, you have A levels in the UK, or you might have an Advanced Placement or AP in the United States. I would say you need to be a little bit higher than that. You need to have basically taken a couple courses in university, and then maybe had a little bit of specialisation, or hands-on experience in the workforce. So, I would say, above A level, but you don't need to have a master's in all of these. So, somewhere in that university sweet spot.

Jeremy Cline 15:08
Okay, and so not even necessarily an undergraduate, which is dedicated to maths or statistics, as long as you've done some of it.

Nicholas Cifuentes-Goodbody 15:44
Right, exactly. So, speaking from my own case, I went to a liberal arts college, which for folks who haven't heard that term before, means you kind of study a little bit of everything. So, you're a master of nothing, basically. But I took statistics courses, I took computer science courses, I actually didn't take any math courses, I wish I had now, of course, I was busy taking Spanish courses. But all of that served as a foundation for me when I begin my own self-guided learning into moving into being a data scientist.

Jeremy Cline 16:16
Are there any particular characteristic traits which you've identified in people, which makes them particularly well aligned to work in data science?

Nicholas Cifuentes-Goodbody 16:27
Yes, absolutely. So, I think the first thing is a willingness to learn. The field of data science is always changing as new techniques emerge. Think of Chat GPT, a couple months ago, none of us had heard of it. And now, it's on everyone's lips, right? And so, the ability to quickly learn new things and to be excited and to dive into new subjects, when you're not sure what's going on, is a really important trait. So, that's the first one. The second one is communication. A data scientist, 99 out of 100 times, is working with non-technical stakeholders. And so, they always need to bridge that gap between the technical work that the data scientist is doing, and the non-technical audience that wants to get value from that data or get value from the tools that the data scientist has in their tool belt. So, being able to communicate, gauge your audience, and speak to them in a way that they understand is also really important. And then finally, you need some interest in numbers, you know, looking over, it might not be a spreadsheet, but looking over numbers, seeing what they have to tell you, and being excited to hack around on a computer and do programming and explore different features in a data set, that should be something that gets you excited, because you're going to be doing it a lot. So, those would be kind of the three areas that I think a data scientist really needs to excel in, lifelong learning, communication, and being kind of a nerd, basically.

Jeremy Cline 17:59
I was going to ask you about the nerd part, so is data science something which is populated by nerds, and I'm conscious that that could mean something to one person and something else to someone else?

Nicholas Cifuentes-Goodbody 18:10
Yeah. So, this is an excellent question. Not that I was not a nerd before I started as a data scientist, but I was a different kind of nerd. But one thing that really shocked me when I moved into data science was the amount of board games that data scientists played. I had never been a big board game person. So, just the amount, the level, the correlation between data science and board games should give you a good idea of the level of nerdery in data science. Let me give you another example. In data science, you often have random numbers and random number processes that you need to set a starting point for. And in data science, we always set everything to 42, like Hitchhiker's Guide to the Galaxy. And so, the fact that the entire field of data science is basically built around nerd jokes should give you an indication of just how nerdy we are. And you know what? More power to us. The beautiful thing about being a nerd is that you have technical, intellectual subjects that are exciting to you, and you're excited to share them with other people. And so, I think being a nerd is a really positive and wonderful thing and a real basis and foundation of being a data scientist, something we should celebrate.

Jeremy Cline 19:22
And I think a lot of people are nerdy in their own way. I mean, I think about a barber that I used to go to, who just you could see the way that he used to cut your hair, he had a very sort of precise, particular way of doing it. It was very much about the technique. And musicians I mean, you look at rock stars, the greats of the pop world, they're nerds. I mean, they have learned the musician craft, they will understand the technical stuff, they will understand all these things about music theory and that kind of stuff. So, I reckon if you look a bit more closely, anyone who is high up in their game is a nard of some description, I reckon.

Nicholas Cifuentes-Goodbody 20:02
Definitely. I think there are nerds everywhere. And so, we should definitely be celebrating them. And actually, you mentioned musicians, a ton of musicians end up working in data science and programming, specifically because I think there's a little bit of intellectual crossover in the way you need to approach the subjects. So, yeah.

Jeremy Cline 20:20
Yeah, I notice the crossover between musicians and those in the medical profession as well, particularly when I was at university, the number of medical students who'd be involved in music was quite definitely, well, seemed more than average. Sure, you'd be able to tell me whether it was statistically significant or not.

Nicholas Cifuentes-Goodbody 20:37
I'd need to see the data. But yes, I'd be happy to dive in.

Jeremy Cline 20:41
So, for someone who is maybe at a career crossroads, who doesn't have what you might call a typically nerdy background in terms of what they have done so far, or I'll say non-technical, maybe they're, to pick a random example, a lawyer, and they're thinking of a career pivot, and they're thinking, 'Okay, this does sound quite interesting.' Where does someone like that even start? I mean, maybe you could share your story, you went through teaching languages to data science.

Nicholas Cifuentes-Goodbody 21:15
Yeah. Absolutely. I would love to share my story, and I think a lot of what happened to me speaks to how you might make this transition in the field in general. So, the first thing I would say is, oftentimes when you start thinking that you're going to make a pivot, you say, 'Oh, no, why didn't I study more statistics at university!?' And what I would say is, don't give yourself a hard time for the things that you didn't study. Really give yourself credit for the things that you have studied, because they end up being your superpower in a lot of ways, they end up being a differentiator. But to speak for me personally, I started out as a language professor. I spent a lot of time studying Mexican literature, so very deep into the humanities. And what happened is, I ended up working in a department of translation studies. Now, translation studies, very big in Europe, not so much in the US, but it's essentially how we translate texts, and also the phenomenon of how texts are translated. So, almost like a sociological study of how things are translated. And then finally, there's a very technical part to the field. So, think of machine translation, for example, like Google Translate. And so, I was there studying my texts on the Mexican Revolution, and I was looking over at the sort of technical computational folks, and I was going, 'That's pretty interesting. What are you guys doing? Can we have lunch? Can we see what's going on?' And so, I started being kind of pulled into the technical side of things. And then, what happened is, I was working abroad, and when I wanted to come back to the US, and I think this is a lot of people's feelings, especially in academia, is that there really wasn't a career path for me in the US. Academic jobs are very hard to come by anywhere. And so, it was really getting to the point where I didn't have a lot of the flexibility that I wanted in my career. And so, what happened is, I ended up getting a job as a university administrator. So, I started by doing some self-study. I found an online sort of a self-guided course called DataCamp, and I began working on the courses there. And then, what I would do is, every time I could think of a reason to do a data science project for my boss, I said, 'Hey, let me convince him.' And so, I would basically figure out a project where I knew maybe 50% of what I needed to do, and then the other 50%, I needed to learn on the fly. And so, I began building up a portfolio of data science projects with my boss. And then, from there, what happened is, I applied to a boot camp. So, in the US, and actually in the UK, as well, we have an increasing number of boot camps, because data science is a new enough field, where universities are still a little bit behind the curve in offering graduate degrees, and sometimes you don't need a two-year graduate degree, sometimes you just need a six-month boot camp. And so, I ended up taking a boot camp there. And when they looked at my CV, they said, 'Hey, you have a lot of teaching experience, would you like to stick on afterwards and maybe work with us and be an instructor?' And I said, 'Absolutely, I would love to.' So, I was an instructor there. And this is one of those cases where your previous life can sometimes be a superpower. But people have always seen that I have teaching chops, and so that's always often where I find myself doing my data science work, as data science plus teaching. And actually, going back to languages, in data science, you find a lot of physicists, a lot of theoretical physicists, because they also have a hard time getting a job. It's hard to be a physicist, I had no idea. But anyways, what I found is that, when we were working with the technical subjects, they were much higher than me, because they had a much better grasp of the math than I did. But what happens when we move to natural language processing and manipulating language using these programming tools, all of a sudden, I was a lot higher, a lot further along than they were. And so, again, whatever you bring into your next phase of your career is always something that's going to set you apart, and something that you can leverage and use as a foundation to make you special, let's say. And so, what happened is, once I stayed on as an instructor there, I worked there for several years, I then moved to another boot camp. And then, I moved to WorldQuant University. And again, we have our data science certificate programme that I built, it's self-guided. And so, for people who are wondering how to pivot, it's a really great place to start. We go through eight projects, they are end-to-end data science projects, you go all the way from data acquisition to building a communication piece, you travel all around the world from figuring out companies, figuring out how buildings are going to be damaged in the Nepal earthquake, to figuring out volatility on the Bombay stock exchange. So, I'm getting a little off topic here, but the point is that, along my transition path, I started with self-learning, so self-guided courses, and there are lots of free resources out there, I then moved to a boot camp, which gave me a small intensive practice, and all along the way, I was figuring out ways to apply the things that I was learning in my jobs that I could build my resume or build my project portfolio to help me get that next job, as I was moving along in the process. So, that's how I made my own transition.

Jeremy Cline 26:50
Okay. So, say, I was interested in a transition. So, as I mentioned, I did maths, which included an element of statistics and computing up to age 18, then did a law degree, and for the past 20-ish years, I've been a lawyer. And having the maths background is actually quite useful, because it means I'm a lawyer that can count, which is a minority, I found out, and computing.

Nicholas Cifuentes-Goodbody 27:15
This is another example where your past life ends up being your superpower.

Jeremy Cline 27:20
So, someone in my position, where do I start? I mean, what do I need on my resume to start just even getting past the first gate when it comes to getting jobs?

Nicholas Cifuentes-Goodbody 27:37
So, that first gate when it comes to getting jobs, what you would need is, you would need some evidence of your ability to work with numbers. When it comes to the data science continuum of jobs, the entry point for many people is often a data analyst role. And in that role, you're looking at spreadsheets of numbers, and you're often creating visualisations or dashboards. So, any evidence that you can do that is what you need. So, what I would do is, I would start by finding datasets that are of interest to you, you can go to places like kaggle.com, or openafrica.net, find data sources that are interesting to you and begin exploring them, either using a spreadsheet or beginning to learn how to work in programming, like pandas, a programming language like Python and pandas. And I would begin building a portfolio where you do data analyses of numbers. And so, then, when it comes to applying to a job, the first section of your job isn't your work experience, but it's your portfolio. So, maybe somebody can click through, go to a website, see the different work that you've done, and that would be a good way to get your foot in the door.

Jeremy Cline 28:53
How would I learn how to do this? How would I look at stats on Africa, and then decide to do some kind of analysis in a spreadsheet, when I don't know much about how to use spreadsheets?

Nicholas Cifuentes-Goodbody 29:06
Right. So, the first thing is, you should come to my course and come to my boot camp. That's the way we would do it. But I would say that ours is one of many offerings. So, looking around on places like Coursera, or Udemy, or just Googling data science courses, you'll find lots of free and paid resources that will start bringing you into that fold, that will start teaching you how to do data wrangling with Python, for example. And so, what I would do is, I would take those things that you're learning through these free courses, and I would then try to apply them to a dataset that's interesting to you. Basically, I would say that the data science community is one that's very much based in open-source thinking, and there's a lot of open resources and lots of sharing in the community. And so, you can just kind of get interested in that conversation, maybe find a data science meetup near to you that you could join. And so, it's by hooking into the community and those resources that I would think would be your first step to becoming a data scientist.

Jeremy Cline 30:10
When it comes to your resume, do employers look only at your portfolio to see what you can do, or do they also look at what sort of a boot camp you did, where you did it? Is that relevant when you choose what to do?

Nicholas Cifuentes-Goodbody 30:29
I wouldn't say that the name of the boot camp is particularly important, in the same way that sometimes the name of the university is. But I think it's more important that you've done it at all. So, I think your portfolio would be the first thing. And then, the second thing that I might do is, I might look through a bunch of data analyst jobs or data scientist jobs, see what the keywords are in those ads, and see what it is that you've done in your previous life, that you can maybe shoehorn into a description that uses those keywords. So, a little bit of rewriting your work autobiography, so that it reflects the needs of the employer. So, if you kind of rewrite the work that you've done, and then you add that portfolio, it's not so much the boot camp that's going to matter, but the fact that you have somewhat of the experience, and the fact that you can actually show that you have built these data science projects or products.

Jeremy Cline 31:28
So, which boot camp you did might not matter in terms of your resume, but presumably, you do want to pick one where they know what they're talking about, and they're going to teach you well. So, do you have any tips for filtering out the bad ones and narrowing down the good ones?

Nicholas Cifuentes-Goodbody 31:47
Yes, definitely. So, one way to find a good boot camp is often... Let's think about this here for a second. So, what's a good way to find a good boot camp? Well, one, you want a boot camp that approaches you in a professional manner. So, one that has an admissions office, one that can speak to you about what the curriculum is, one that maybe has a component of helping you find a job and getting your resume up into shape. So, something that looks at your entire transition process, and doesn't just give you a little bit of theoretical founding, foundation. The second thing I would look for is, I would look for a boot camp that was hands-on. So, one in which you were going to be creating projects, one in which you might have a capstone project. So, not one that's going to be theoretical, but one in which you're going to have your hands on your keyboard. And then, the last thing that I would look for is, I would Look for one that the things that they teach in their curriculum fit into the areas in which you need a little bit more practice. So, if you haven't been working in maths in quite some time, you might want to find one that has a little bit more math in it, for example. So, that's what I would look for. I would look for one which seems professional, in the sense that they can explain what it is they're doing. I'd look for one in which they were going to be working with you in the whole career transition. So, not just learning, but also getting your resume ready. I would look for one that's hands-on, that gives you portfolio pieces. Those are the main things that I would look for if I was looking for a boot camp.

Jeremy Cline 33:29
Do any of them have minimum entry requirements? Or will the good ones work with you to figure out where, as you said, where you might need the help if you're interested, but have had no previous education that might be maths or statistics or computer related?

Nicholas Cifuentes-Goodbody 33:51
Well, certainly, you want a boot camp that has some sort of admissions threshold, because if they're just letting anyone in, then it's possible that they don't really have a clear vision of what it is that they want to teach, or what is the level that they can bring people in, and the level that they can bring them out. So, your question is... Actually, repeat your question, and let me see if I can answer it again in a better way.

Jeremy Cline 34:16
I'm thinking about the person who maybe has virtually no background, other than maybe what they had to do at school in maths or statistics or computing. And that person wants to find a boot camp that suits them. Do they need to do anything, if you'd like, to get to first base? So, to get that knowledge in terms of minimum entry requirements. Or can they find something where, I don't know, the boot camp might just check that they've got the right sort of attitude and aptitude, but otherwise will provide all the tools they need, even if it's starting from that very low base.

Nicholas Cifuentes-Goodbody 34:55
Generally, what happens is, if you come into a boot camp, and they see that you don't have the kind of lore knowledge that you need, what they'll do is they'll suggest a series of resources to you, whether it's a free online course from MIT, or whether it's a paid course from somebody like a data camp or Udemy, to kind of get you up to that baseline first. So, for example, what we do is, we have an entrance exam, and for students who don't pass the entrance exam, we can in a customised way tell them where they need to bulk up their learning, before they come back and become part of the programme. So, what I would say is, if you're at kind of an absolute zero on all three of those areas, I might reach out to a boot camp and see what resources they recommend that you study on first, before coming back, and then being able to kind of hit the road running.

Jeremy Cline 35:53
An objection which I always see to the idea of changing career generally is going back down to the bottom rung of the ladder and starting on a much lower pay, especially if you're in a career, and you've been in it 20 years, and you've got up to a level of seniority. So, you said that you might start by building together your portfolio, and then you'd be typically going for this sort of entry level data analyst role. So, the person who's concerned about going back to the bottom rung of the ladder and taking a pay cut and doing that, I mean, what advice you would have for them?

Nicholas Cifuentes-Goodbody 36:31
Well, I would say that, unless you're a high-powered doctor or a lawyer or something, generally moving into the data science professions is well paid. And so, while you might be taking a little dip initially, you might not even, you might even be starting where you left off in terms of pay. So, I would say that this is not so much of an issue. But the other thing I would say is that you need to balance what you want in your career when it comes to finding a job that's fulfilling, and finding one that pays the bills. Sometimes, a job that pays a lot is a job that isn't one that you find particularly fulfilling, or one that you're interested to go to on Monday morning, right? We talk about getting rid of those Monday morning blues. And so, I would say that that's just something you need to balance. And the other thing I would say is that, because there's such a demand for data science, often when you start at the bottom in some careers, you're thinking it's going to be a five- or 10-year ramp up before I get back to a senior position. But in data science, it's much more accelerated. So, we were talking about a one- to three-year ramp up to get back to a senior position. So, what I would say is, generally, the salaries in data science are good, generally, moving up can happen pretty quickly. And lastly, if you're already thinking about moving to another job, because where you're at isn't where you want to be, then maybe it's okay to take a pay cut, as you kind of work into that new career.

Jeremy Cline 38:04
If you've got this potential one-, two-, three-year progression, what does progression look like after that? Is there progression, or is this something which plateaus out fairly early on?

Nicholas Cifuentes-Goodbody 38:18
I don't know if it plateaus out fairly early on. Generally, you work as maybe a data analyst, and then you might work up to a senior analyst position, or you might move over to data science and then a senior data scientist. Generally, those are the two levels, scientist and senior. And from there, you might move into management, you might be a manager of data science, you might be a lead data scientist and overseeing a whole section of data science. But I wouldn't say there's so much of a plateau, only because the profession is always changing, right? So, if you're a senior data scientist, it doesn't mean that you're sitting on your laurels. It means that every two to three years, you need to be learning new ways of doing things, because the tools have completely changed. And so, while your job title might not change, everything is always changing around you. So, it's definitely not, it's definitely never boring in terms of a plateau, no.

Jeremy Cline 39:19
That leads very nicely onto my next question, which we've already touched on. And that's the role of AI. I mean, there's so much talk about how AI is basically going to replace a lot of functions in jobs. And it seems to me, as someone who admittedly doesn't really know very much about it, that any profession which involves taking huge amounts of data and crunching those numbers, this could be something which AI could substitute the sort of work that people were doing. So, I'd love to hear from you what your take is on what the threats are from Ai, but also what the opportunities might be?

Nicholas Cifuentes-Goodbody 40:03
Well, I think, data scientists, as the people who are designing and train those models, are in a very unique position, not only because they are making the models, but also because they are on the leading edge of, you know, we talk about how we treat each other in society, well, data scientists are the ones who are thinking through how we treat each other algorithmically in society. And so, I think it's a very exciting time to be a data scientist. The question is, how is AI going to change our jobs? Well, right now, you can think of AI as being able to do many tasks at a C or C+ level. Does that work in UK grading? Does that make sense?

Jeremy Cline 40:49
Yeah, absolutely, yeah.

Nicholas Cifuentes-Goodbody 40:49
Okay, good. Just making sure. So, kind of like mediocre text. And so, where I've found it very helpful is in writing small snippets of code. And AI generally will help you train models a little bit quicker and determining what type of model you want to train. But I don't see the profession of data science being under threat, only because there's somebody who needs to be asking questions as to what, right, maybe the process of building the models become more automated, but why you're building the model, the parameters of that model, making sure that it's being deployed ethically, those are things that data scientists will always need to do. So, while we don't know what the future brings, I would say that data scientists are in a unique position not only to determine the future of how AI is used in our societies, the data scientists are in the unique position of determining how those models are built. And so, AI isn't coming for your job. You're the person who's figuring out what it is that AI will be doing in its job.

Jeremy Cline 41:59
What haven't I asked you that people should know about working in data science?

Nicholas Cifuentes-Goodbody 42:04
I think you've covered everything. But there's one thing that I think bears repeating, which is really the idea of lifelong learning. Data science is always changing. There are always new techniques, there are always new technologies. And so, one thing that you really need as a data scientist is a willingness to learn and learn new things, whether it's studying on your own, whether it's working in informal groups, or whether it's working in a boot camp, like the WorldQuant Applied Data Science Lab. So, if that's something that is interesting to you, you always want to come to job with your learning hat on, then I think data science is a career to investigate.

Jeremy Cline 42:46
And for someone for whom this has piqued their interest, do you have any recommended resources, podcasts, books, blogs, sources of information that people can maybe take a look at?

Nicholas Cifuentes-Goodbody 42:59
There's one resource which, let's see here, what would be a good start? So, I think there are a couple resources, a couple books that I think are very good. So, one which would be a good start to statistics would be Introduction to Statistical Learning by Gareth James. Another for machine learning and different types of data science would be Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurélien Géron. And a book that I love, in terms of a deep dive into Python, would be Fluent Python, by what is the name? Luciano Ramalho. So, those are some resources that I would recommend. You can see that I'm very book heavy, but that's just, I'm still a professor at heart. Another book which I would recommend, as long as we're recommending books, but one that I think cuts to the heart of what learning should be and what learning in the quantitative sciences can be, would be Mathematics for Human Flourishing by Francis Su, where Professor Su talks about the process of learning and teaching math, and how math can teach you about everything from democracy to beauty, right? And so, it's a really great way to see how what you think might be a closed off subject actually reaches into many important corners of our lives.

Jeremy Cline 44:30
Excellent. Well, there'll be plenty of material for the show notes for this episode. On which subject, if someone wants to get a hold of you, or find out more about WorldQuant University, where would you like them to go?

Nicholas Cifuentes-Goodbody 44:41
Absolutely. So, first of all, I am always excited to talk to people who are in career transitions. It's a passion of mine, as someone who's also been through the fire, helping other folks as well. So, if you want to reach out, I encourage you to do so. You can find me on LinkedIn at ncgoodbody, or just look for Nicholas Cifuentes-Goodbody. That's always where you'll find me on LinkedIn. And you can reach out there. And then, if you're interested in learning data science, I really recommend our Applied Data Science Lab at WorldQuant University. And you can go to wqu.edu, and then just go under our offerings, and then you'll see the Applied Data Science Lab. So, you can reach me at ncgoodbody on LinkedIn, or come to wqu.edu.

Jeremy Cline 45:31
Brilliant. And I will put links to those in the show notes as well. Nicholas, thank you so much for coming on, for sharing your story, and for introducing us to the fascinating world of data science.

Nicholas Cifuentes-Goodbody 45:42
So, Jeremy, I want to thank you, it's such a delight to talk to your audience. And one of the great joys in my life is talking to people about data science, both as a field, but then as a profession and positioning into that profession. So, thank you again, it's been a real pleasure for me.

Jeremy Cline 46:02
Okay, hope you enjoyed that interview with Nicholas Cifuentes-Goodbody of WorldQuant University, I didn't really know much about data science as an area before this conversation. And what struck me was just how many areas this career could apply to. Any industry where decisions are made based on data is going to require some kind of data science function. And when you stop and think about it, that could apply to almost any industry. And there's that theme, again, about how your previous career can actually be your superpower, rather than something which holds you back. All those experiences, all those connections, everything you learned in your previous career, you can take forward, it's going to be useful. This doesn't just apply to data science. But this applies pretty much to any career you can think of. It's something I've said before, it's probably something I'll say again, but it really is a really very important message. As always, you'll find a transcript, a summary of all we talked about, and links to any resources that Nicholas mentioned, and they're in the show notes page for this episode at changeworklife.com/179, that's changeworklife.com/179. Something I'd like to know from you is whether this type of episode is helpful, or is normally we look at themes which apply pretty much to all careers, is it helpful to dive into particular careers, particular industries. If you'd like me to do more episodes like this, and if there are industries or careers which you'd particularly like me to cover, then do get in touch on the website at change worklife.com/contact, that's changeworklife.com/contact. There's a form there where you can send me a message. I read all of them. So, do get in touch that way. There's another great episode coming up in two weeks' time, so if you haven't subscribed to the show, what are you waiting for? Make sure you do it so you never miss an episode. And I can't wait to see you in two weeks' time. Cheers. Bye.

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