Welcome to episode 200 of the Super Data Science Podcast!
Today's our anniversary!
It’s the 200th episode of SuperDataScience Podcast and let’s celebrate it by doing a digest on the most pressing topic that’s affecting every business, every industry, and every person today and in the future– artificial intelligence and data science.
What does the future hold for the field of AI & Data Science? Are we to prepare for a future of recognized superiority or a future of complete powerlessness for our field?
To answer these questions, let’s go back to the best/controversial/trending conversations about what Artificial Intelligence is all about and what lies ahead in the future for the field of Data Science. I’ve made a compilation of clips from previous episodes where I talk to AI and data science experts.
If you’ve been following SuperDataScience since Day 1, then you might remember the voices and the nuggets of wisdom they shared last time. If it’s a bit blurry, then nothing to worry since this will serve as a recap for you.
I suggest you even bookmark this for you to go back when you need a quick guide when pre-planning, making decisions, or assessing your tasks. It’s a good practice to see the trends if you’re wanting to be the best data scientist in your career or for business leaders, to be the most disruptive in the market.
We, as data scientists, hold the wheel and are responsible for where we want to direct it. These 16 different ideas can help you see the future for the field of data science and consequently, for your own future. Some ideas from these experts might seem unbelievable and unreachable for the near or far future. It’s for us to weigh our best judgement and expertise if we want to help these ideas progress or maybe, regress.
For the new listeners, I suggest you go back to what’s been featured here and listen to the whole conversations. What I served you are just the little bites from the fully-loaded treats. And if you’re interested in other topics aside from today, check out also the other episodes!
Thank you so much for always patronizing the content the SuperDataScience produces every time. I am always glad that I can reach out to every person and help them through this podcast. I hope to feature more interesting topics from the experts and influencers in the fields of Data Science, Machine Learning, Artificial Intelligence, and more so make sure to watch out for them.
Items mentioned in this podcast:
- SDS 008 : Data Science in Computer Games, Learning to Learn and a 40M Euro Case Study with Ulf Morys
- SDS 011 : Learning Resources, Thinking Like a Data Scientist and Data Exploration with Garth Zoller
- SDS 013 : 95% Accuracy Models, Winning People Over, and Saving Lives with Damian Mingle
- SDS 021 : Applications of Data Science, Democratizing AI and Advice with Sinan Ozdemir
- SDS 025 : Women in STEM, Bench Science to Data Science, and Data and Medical Ethics with Kimberly Deas
- SDS 033: Building a Personal Brand in Data Science with Senior Insights Manager Josh Coulson
- SDS 035: Build Your Own Data Science Master’s Degree with David Venturi
- SDS 037: Develop your Dream Data Science Career with Experfy with Harpreet Singh
- SDS 043: Solving an Optimization Problem with a Custom Built Algorithm with Deblina Bhattacharjee
- SDS 101: What a Data Science Headhunter is Looking For with Urie Suhr
- SDS 109: Business Consultancy in the Space of Data Science with Matt Dancho
- SDS 111: The Power of Soft Skills in Data Science with Eric Webber
- SDS 125: A Glimpse into the Virtual Reality World of the Future with Andrew Borisov
- SDS 131: The One Purpose to Data Science and The Truth about Analytics with Eugene Dubossarsky
- SDS 145: How to Use Data Science In Offline Business with Josey Parks
- SDS 151: Women in Data Science & How to Help with Lucy D’Agostino McGowan
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- What topics do you want to be featured in the next 100 (and more!) episodes of Super Data Science Podcast?
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- Music Credit: Mesmerize by Tobu [NCS Release]
Welcome to the Super Data Science Podcast, episode number 200. Welcome back, ladies and gentlemen to the Super Data Science Podcast, and today is our anniversary, episode number 200. How crazy is that? It feels just like not that long ago, we started this whole show but it's actually been two years.
Been over two years since we started the podcast and I'd like to say a huge thank you for all of you for being part of the show. It's been an amazing journey and in order to commemorate this very special 200th episode, we decided to double down on a very pressing topic.
On a topic that is all over the planet, that is affecting every industry, every business and very soon is going to be affecting every single person on this globe. And you're right, you guessed it. That topic is artificial intelligence.
So in today's show, you will hear what artificial intelligence is all about and what the future of data science looks like according to the top AI experts that we've interviewed in the past. To do this, we've created a special mashup, or you may call it a recap episode with our 16 biggest AI experts.
That's right. We took the top 16 AI experts that have been on this show and have put together their thoughts into this one episode. So I can't wait for you to check it out and without further ado, please enjoy Double Down on Artificial Intelligence.
Today, we had a very special guest, Ulf Morys, who is the Finance Director of the German branch of the game design development and distribution company, Ubisoft. Now, I'm sure you've heard of Ubisoft before. They're the maker of such game brands like Watchdogs, Assassins Creed and Far Cry.
So as you can imagine, this is a huge company, it's a huge name in the world of computer games.
From what you've seen and what you've learned about data science, where do you think this field is going in the future, in the coming years?
Tools will get easier to handle, so there will be a sort of democratization of data science, certainly. So there's a huge chance also for the vendor companies, like you mentioned, Tableau and what I personally got very endeared with digging into data science, was, of course, open source software because both R and Python are open source and I just find it incredible, the quality of support that you're getting when you're having questions, for example. It's just amazing.
So I think data science, we will also advance the cause of open source software to a certain extent. But to answer your question then, where do I think what we had with data science in the near and medium future, in the narrow professional sense or really concentrating just on finance, I would say it will really help to what we said previously, separate permanently, data from the data processing logic to get rid of this mess that Excel is making.
When you take it to a bigger scale, it will help to combine more systematically the use of data analytics techniques and data visualization for really the discovery of patterns and rules in your data, which is like by my previous example of what I said, do some predictions like, “Where do I invest my money? Is this customer better than this customer?" And I have more than just the guts feeling to base my decision on.
Ultimately for me because it's really the most important part, I think of the traditional finance role in an operative company. Not on a corporate level, that well, that too, but it's still different. But on the operative level, the planning process is really important because you always ... I mean, you need to know where you want to be tomorrow and integrating predictive methods into concrete plannings of costs and especially revenues, will become paramount.
If you really focus on the financial function, I think this is where data science can really improve things and will play a major role, but if you take it to a larger sense, data science, the most intriguing field right now and I don't think you can make a huge mistake. Learning about it is unsupervised learning because it's like again, I only have the theory really.
But I mean, the difference is, of course, with supervised learning, you need to know the outcome. You need to have a training data set, and with unsupervised learning, you just learn structures from data by itself.
So it's Sui generis, they would say in Latin, as also from your own origin. The data structure emerges from nothing. Te very simple fact is that as soon as you leave the field of structured data in NLP systems and if you enter the field of big data from resources as disparate as let's say, Facebook entries, tweets of feedbacks from websites, commentaries and so forth, you just end up with so much more unstructured data and any tool that will allow you to have structure emerged from initially unstructured data will be enormously important and helpful.
Today, I've got one of the top students of super data science, Garth Zoller. From where you are now, from what you're learning about data science or from how far you've gotten into the field, from what you see now, where do you think the field of data science is going and what would you recommend to our listeners to look into, to prepare for the future of data science?
Based on what I've seen and what I'm getting a sense of is, it feels very much like the late '90s, early 2000s, with the technology boom that happened at that time, that was largely centered around network administration and system administration.
That's where data science is to me, today, in the industry. It is the thing and what to me is a little different about data science compared to that earlier time, that earlier time was about the hardware or the software.
Data Science, yeah, we have some tools, but it's really, again, about that way of thinking about problems and being disciplined in how you create little miniature of quick experiments to vet out proof points of whether something is valid or statistically meaningful.
That extends well beyond a tool and well beyond a particular timeframe. To me is more like maybe personal computers way back when they started. Can you really get sustainable employment if you don't have some basic computer skills today? It's going to be difficult.
Data science to me is like that going forward. It will be the differentiator between organizations. The organizations that have a deep concentrated knowledge of data science skills are going to have a competitive advantage and whatever you're doing, even if you're not a data scientist, maybe you're a people manager or whatever else, applying data science to what you do will differentiate you from your peers.
It ultimately always affects the relationship you have with your customer. You're able to create more authentic and provable relationship, giving them exactly what they need based on the data and also driving the bottom line.
Today we have a unique episode because we have a very special guest, Damian Mingle. Damian is the Chief Data Scientist at WPC Healthcare, but not only that, he's also a speaker and author and he has been ranked in the top 1% of data scientists across the whole world by Kaggle.
"From where you stand and from all of your experience, where would you say you think the field of data science is going and what should our listeners look out for, to prepare for data science in the future?
I think more and more of what will come is more of the automation. There has been a real big emphasis on the fact that there's not a lot of data scientists out there, which I don't know, maybe that's true, maybe that's not true. At least that's what the media is saying. So there's a scarcity and there's a need to create some synthetic or artificial data scientist.
So some of what we do can be automated, absolutely. I think we'll see more of that come into our wheelhouse, if you will, and that could be a good thing. It's much like if we take our own medicine, like we were talking about with the doctor, maybe there's some things we don't necessarily want to do in data science, and there's other things that we're interested in doing.
I think there'll probably be three major things that will show up in the next maybe five years. One is, can you ... as a way to prevent being automated out of data science, I think you'd have to think along the lines of, “Can I convert a business problem into a data science problem?”
Right now, you think about IBM Watson and some of these other big cognitive platforms are really great, it's really exciting times. But there's a couple hangups. One is, if you can't get the data to them, they can't do any of that stuff. So sharing out how to get to your data, that's the new bottleneck. It's not crunch time on the numbers and that sort of thing. That's gonna be an interesting thing to overcome.
The other thing is trying to create targets. We talked about creating data science solutions for business problems. If you don't know how to create a target, something you want to predict, that's not obvious, I think that you will have a difficult time in the future with data science. I think that'll become ... I really think ... and even in a meeting on your own, depending on what your role is with the company, thinking through, “How would I make this a data science problem?" is a good mental experiment that you can do. You never have to share it with anybody, but it's a way for you to start doing some brain training on how to think machine learning in the world. And that's very, very powerful.
If you can't implement these data science solutions, I think that'll be another struggle. That other third one, which I think is going to be everybody's future field day, if you have the IBM's of the world and some of these other data science players come in and they start automating the ... it's going to sit everybody's floor, let's call it 80% area under the curve.
The new gold will be trying to move it from 80% to 85, 86, 87%. So you're going to have developed techniques on future engineering and how to work with data and some of the stuff we talked about sub-setting, subspace, ensemble, all these sorts of things, to be able to get yourself the lift that you need because today, people would pay from going from 50 to 85%. That's huge. That means lots of money and a lot of cases, a lot of saved lives.
In the future, I think you'll see those margins shrink or there's percents shrink, but they're still gonna want to pay for them.
Today, I've got a very exciting guest with me, Sinan Ozdemir, who is an author, a head instructor of data science at the General Assembly and the founder of Legion Analytics.
Where do you think the field of data science is going and what should our listeners prepare for to be ready for the future that's coming?
That is a big one. I think that the future of data science, the future of machine learning and AI ... I'm gonna piggy back off of a previous answer that I had and say, the future of data science won't necessarily be in the most innovative software technologies but what it's going to be, is the ability to apply these technologies to fields that have not been updated, in some cases centuries.
I think that to prepare yourselves, listeners, to prepare yourselves, as you're learning data science, as you're listening to a lecture on Chi Square tests or listening about Pandas or data frames or data manipulation. As you're learning this, never ever forget why or how you're going to use this information. Never get lost in the theory or even in the practice, but always keep yourself grounded by saying, "You know what, my wife's shop uses data and I think they're using it incorrectly. How can I bring this to them or my buddy is starting a vineyard and I want to be able to use image recognition to help them know when their yields are coming in or when their yields are good or bad."
So always remember, you can apply this not necessarily to the most ... Uber or Tesla, SpaceX, Solar City, not even to the most innovative companies, but never forget that there is a plethora of SMBs, smaller to medium sized companies who could also use this technology.
Wonderful. Love that answer. And yeah, it definitely ... that's something we've already discussed a bit and it's good to reiterate that. Always look out for where you can apply the knowledge that you're learning.
Today, we've got an interesting guest-
Today, We've got Kimberly Diaz joining us and Kimberly is using data science for something very, very noble. Kimberly is a data analyst in the space of medical research and she uses data science to help provide healthcare and support services for individuals living with HIV and Aids.
From where you are and I think it is your answer is actually gonna be very valuable to lots of all our listeners because you are in a very different space, you're in biology or in medicine and a lot of our guests, very few of us are operating in that space.
So the question is, from where you are and from what you've seen about data science, where do you think this field is going? So what'd you think is coming for the world in terms of data science and in specifically in your profession and what would you say listeners need to prepare for to look out for the future, so that they can build their careers in that space or just be prepared for what's coming in terms of data and medicine?
So, I think one of the bigger, more significant applications of data science in terms of medicine, is that information that is collected about you on the data side can be used to ... from anything, from collecting genomic data can be used to determine which drugs will better treat your cancer, from coming from HIV, what areas of the county should we be looking forward to see the next outbreak of a certain disease.
So I think in medicine, the sky's the limit in terms of how useful it can be in and ultimately with the goal of saving people's lives and improving people's lives who are dealing with diseases which can many times be fatal. So that's the biggest thing that I see, data science being able to use in the medical space, is that it has an overall goal of improving care.
I think one of the challenges to data science being applied heavily in the field of medicine is that oftentimes people are reluctant to change and that there is a big learning curve with it. I'm seeing that a lot with the implementation of various EMRs in hospital settings, that there is a learning curve involved with learning how to properly use an EMR, EHR to document information in the chart for a patient, but I think that the overall goal of saving lives and decreasing healthcare costs makes it a challenge that's worth people in that profession and in that space meeting.
So I guess that's my comment about medicine and data science. I think the sky's the limit, I think ultimately will improve the health of people and that will ultimately drive costs down. At least, that's the hope.
Yeah, I totally agree with you and-
Today we have Josh Colson, who is my friend back from the Deloitte today. So back in 2012, we met and we did some work together and then he moved on to a new role at LinkedIn and he's been there ever since and he's really grown in the team.
He's actually now a manager there and has a team of his own and manages a huge part of the insights team of LinkedIn for Asia Pacific, where he helps employers find the right talent for their organizations and understand how to go by.
From where you sit, from all the things you see about data, you have a team, you worked at LinkedIn, you have so much access to data. You actually have this bird's eye view of what's going on in the world. Where do you think the field of data science is going and what should our listeners prepare for coming in the future?
I think I mentioned earlier. Every organization in the world who hasn't ... who sees that data is part of the value that they create, is going to continue to see that data as more valuable to their bottom line.
I think the real challenge for data science in the near term is connecting with the business and really focusing on measuring their impact and making sure that the models that they're putting in place, the algorithms that they're driving, they are driving real revenue and bottom line profit and it's all about ...
And that's it. Really, I think that's the biggest trend that's happening right now. I think the data scientists that will be the most valued are the ones that have already been able to build a brand for themselves because they have to live it success to businesses and I'm thinking the same way that a private equity and venture capitalists provide value by stripping companies and turning every company and that kind of thing. I think data scientists will build personal brands based on the work that they can do with organizations to reinforce the top line and bottom line.
We've got a very interesting guest today. Today we're talking with David Venturi and David is a fascinating person because he, at some point in his life, understood that he wants to study data science. He wants to be a data scientist and he actually enrolled in one of the prestigious universities in Canada for a data science degree, for a data science masters.
But within two weeks of studying there, he dropped out and he didn't drop out because he was not satisfied with his career choice and he wanted to do something else. No, he dropped out because he realized that what he was learning wasn't sufficient enough, and guess what he did instead? Instead, he created his own masters program based on courses available online.
So he looked at different data science masters programs that are offered by different universities, then based on that, he compiled his own program using the courses that he can purchase or find for free online. I thought that it was an ingenious idea. It saved him $30,000. And also, of course, he was free to pick the best quality of content that was available to him.
Where do you think this whole field is going? What are the most important things people should focus on to prepare themselves for the future of data science coming in two, three, five years from now?
Yeah, so one thing that I'm currently working on and that I didn't realize this is really important going in, is the software engineering aspect; being able to make your code reproducible and it happened the best practices for testing or testing your code and debugging all of the software engineering skills that I think a lot of new data scientists may not have, going in, if you didn't study computer science for example.
So that maybe not ... that may not be what the future is, but I think that's a really important part that can be overlooked.
Today, we've got a very interesting guest. Today, we've got the founder and Co- CEO of Experfy, Harpreet Singh.
The other interesting question I had as well, which I'd really be curious to get your opinion on is, from where you sit, from all the things that you see going on in the space of data science, where do you think this field is going and what should our listeners prepare for to be ready for the data science of 2020 or the data science of 2025? What would you recommend for them?
So, yeah, I know this field is changing so rapidly that it would be a fool's errand to make many predictions but one thing is for sure, that there is a lot of automation going on.
We have a lot of tools that are developing and that are being developed rather and this is going to be a very exciting space and it's going to impact every industry and the industries that are going to see the most change are the ones that have the best data or the richness of data.
So those we will see evolving much faster than the others. If you are in such an industry, then I think it's a very good idea to embrace analytics. Even if you're not a data scientist, even if you're a manager, understanding how one can become data driven, how processes can benefit from different types of analysis is really important.
Making sure that the company has some kind of a data strategy to capture the right data is another important consideration because companies that are not going to do that are frankly going to be ... they're not going to be very competitive. They probably won't even exist in the next five, 10 years.
It's a bold assumption but if we look at how many fortune 500 companies exist from the last century, let's say 1950s, I would say at least 30 or 40 have disappeared. So I think companies that do take data science seriously are the ones that are going to stick around.
Yup. Yup. Totally agree with you and that's some very interesting advice and overview of what to expect and you're totally right. It's evolving so quickly. It's hard to make very definitive predictions, but very interesting what you said about automation and that managers should also look into data science and I totally agree with you that there's even some predictions that out of the fortune 500 companies, over half of them will disappear in the next decade just because of what's happening in the space of data sciences; what's going on, so it's a huge disruptor as well as an enabler for companies.
Today we've got a very interesting guest, Deblina Bhattacharjee. She is calling in from Seoul, which is South Korea and she is an AI researcher working at one of the universities there or doing her degree at one of the universities there.
From what you've seen around the world and from the research you've done, what do you think the future is of artificial intelligence?
As far as I know and also from the opinions of the scientists and researchers with whom I met in the conferences around the world, what all of us we think is the field of data sciences headed towards a fusion with intelligent systems to create smarter cities of the future. That's the main vision.
So I also strongly believe that with the ongoing research with real time, big data and Internet of everything right now, data science is going to explode in the future.
With a lot of stuff happening, the thing is that as of today, in the morning when I got up, I just read this, "Strata and Scalar, they have been replaced by Hadoop already and ... sorry, Strata and Scalar have replaced Hadoop and there have been-
Just a bit of a different direction.
... and there are the data Ops tools which are being developed to help data engineers like a DevOps tools which used to be previously for the developers, now they are data Ops and they have been built by companies like Nexla and Data Kitchen and it's really great how data field is progressing and also the automated predictive analytics, which is like the thing which is happening right now.
So this predictive analytics had been automated last year and the data robot was created and people were like, "okay, so by 2025, everyone is going to be out of jobs." But then it was a bit too soon to say because the data robot as now, it's just speeds up the model development for any model that you're building and it's the one stop solution to speed up whatever you're implementing in the industry.
So it has a long way to go, definitely. It's a budding field. So both AI and data science together, it's going to be really powerful in the future.
Yeah, we've got Urie Suhr on the show and the Urie is in the short, a headhunter. She's the director of talent acquisition for collective[i] which is a data science company which uses artificial intelligence and predictive analytics to help their clients with things like sales, CRM and creating a better customer journey.
From what you've seen from all the work you've done, the work you do and the candidates you've seen and the job descriptions, where do you think the field of data science is going and what should our listeners prepare for in order to be ready for the future?
This is a tough question, so I truly feel that the title of data analytics or data analyst is probably going to go away in the near future. I think that it will soon be replaced by a traditional data scientist and a lot of data scientists are now moving more into the deep learning space and towards AI and machine learning.
So I think it's really important to always make sure that you have the ear on the ground and recognize what's going around as far as not just what's trending but what makes sense as far as where the momentum is going, when we talk about just the world of data.
What I find really interesting currently right now is that data science is almost becoming interconnected with big data. I mean it all still isn't in the data family, but it's almost becoming ... I'm seeing more and more cross pollination between the intersection of big data and data science.
So there have been very interesting candidates that I can give an example that I've seen that maybe they were more of a Hadoop developer or working more on Cloud and things like that of that nature and being more on the engineering side, now migrating over to the data science field. Then also, same thing. I see a lot of data scientists now ramping up on technology such as Spark and so forth. Being able to really be more connected on the big data side of things. That wheelhouse.
... inspiring new guest on the show. Matt Dancho is the founder of Business Science Consulting Firm in the space of data science which works with companies ranging from startups to fortune 500 companies. Not only is Matt the founder of businesses, but also he's the author of multiple R packages, so just tidyquants to empty case, we put table time and others.
Philosophical question; from what you've seen in data science and from the way it's evolved, where do you think this field is going and what should our listeners look into to prepare for the future?
So in terms of the field of data science, I think we're still just in the beginning with it. We've got a lot of tools that are evolving and I think we're gonna to be going more towards automated tools like I love what H2O is doing right now with actually making it easier to get these high accuracy models now.
So where I really see the benefit is in a different part of data science, which is actually communication. We talked a little bit about Shiny web applications. I really see that as being a huge benefit for businesses. That's something that at Business Science, we're actively investing a lot of time into.
Some of our first projects ... actually, I think our first five projects with the client were Shiny web apps, but it's being able to distribute analytics interactively. So you've got a machine learning algorithm, you're trying to forecast something or you're trying to predict whether an employee is going to churn, you have to have a way to distribute that information to nontechnical people in a wide audience and I think Shiny is the way to do that or at least one of the ways. You've got Power BI, Tablo, as other that, Carl, you had mentioned.
I really see that as the future of data science, as being both good at the tools, understanding you need them to know the tools inside and out and understand them. I think the tools are going to get easier to use, but I think down the road, what's really going to become very powerful is being able to communicate that data in a interactive way.
Gotcha. Awesome. Thank you. That's a very apt answer. Sums it up very nicely and I totally agree with what people should be looking into to become super powerful data scientists and change the world.
Just got off the phone with a senior data scientist at LinkedIn, Eric Weber, and we had a great chat. Really, really enjoyed the chat.
From what you're seeing in data science and education and from all the experiences you've had and where you stand now, where do you think the whole field of data science is going and what should our listeners prepare for to be ready for the future that's coming?
This is a tough question. In terms of where it's going, I think we're going to see some radical changes as companies start reckoning the investments that they've made in data science and by that I mean, some companies are going to decide that they haven't seen a return on investment and I think in a lot of cases it's because maybe they're not measuring the right thing, but you're going to see other companies that decide to double down and say, "Okay, we really need to take seriously how we're measuring the impact of our data scientists on this organization."
So to me, what the near future feels like is really developing proof of concept for really looking at ROI of data scientists, really turning the data science on to the actual data scientists, "How are you going to measure your value for us? How are you going to test your value for us?" And that's a question I think we're going to have to answer.
To this point I think, data science being the sexy career and having high salaries and all of this stuff, that's great, but companies at the end of the day are business and they need to see the lift and the amount of improvement they get from adding data sciences, and to me that's a near term thing. That's something that a lot of companies will be grappling with. And the thing to me, where we're going, I mentioned scalability at the start of the conversation.
Scalability is a big deal. I continue to not sometimes care if someone can eek out an extra 0.1% of performance in their model and I'd rather see them be able to scale and productionize their model so that we don't have to rely on someone maintaining it over time, so that we can actually build it to deal with the size of the data that we have.
This is an issue that we see at LinkedIn, but it's not unique to LinkedIn. The volume of data is not going to decrease. The speed at which it comes in is not going to slow down all of a sudden and so I think really dealing with scalability, that is a big issue to grapple with here in the near future.
We have got a super exciting, super prompt episode, super futuristic episode with Andrew Borysov from the MMOne project.
From everything that you've seen so far, where do you think the space of data science is going? Spaces of data science, AI, machine learning is going and what'd you think our listeners should prepare for to be ready for the future that's coming?
I don't think it's gonna make sense for me to talk about different areas where data science could be applied in particular and only like legal or everything about the images and how you process them.
So I would speak more philosophical in general. One day, we're going to come to the ... I'm sorry for calling it like this but 'matrix', in some kind of way, maybe not the way the brothers described it.
What I mean is that people spend less and less time offline and more online and it's gonna be even more. Even now in the United States, there is a big problem. A lot of shopping malls are shutting down and there is nothing that people are intended to do with this big amount of space.
So people will get even deeper into virtual world since this is where we are as play and when you have the virtual world, you need to have artificial brain there that can talk to you, that you can do something about, I mean talk to another person in this world and so on.
This is where the data science approaches need to be implemented in one way or another? So I'm thinking more globally about this.
Yeah, no, that's a very ...
... accurate picture.
Yeah. So, and the other part is of course billions of people one day losing their jobs simply because, let's call this computure.
Yeah, we'll do it ...
I'm excited about this episode because I have the legend of data science, Eugene Dubossarsky on the show. And legend is in no way, not even the slightest and overstatement because Eugene is indeed regarded as a thought leader in the space of data science indefinitely in Australia that I can tell you for sure and I would even go as far as saying across the world, across the Globe.
Eugene is a person who started multiple companies and has participated in multiple companies in the space of data science. Just recently started Advantage Data. It's a world class data science consulting firm that works with C-level executives and CEOs.
He's also the director and principal trainer at Prescient Analytics. He's the head founder of Data Science Sydney and he's also the Chief Data Scientist at Alpha Zeta and Global Network of Data Science Consultants.
So this one we kind of touched on as well, but maybe a summary would be great. Where do you think the field of data science is going and what should our listeners look into to prepare for the future that's coming?
Well, I think there's gun for a data literacy, a general data literacy, which means the more demanding clientele in the buying side of the market. Something I'm betting on very heavily at the moment and also we mentioned before is quantum computing. So Presciient has a quantum computing course coming out and there'll be more quantum computing offerings and material from Presciient and Advantage Data in the near future.
Like I said, I think statistics is key. I think getting a good grounding in statistics is the most important thing of all. I find people, mentorees, come and say, "Which data science and Masters course should I do?" And I say do a Masters in statistics. And once again, basing in statistics, in particular, causal impact analysis, you can't go wrong with those.
I'm still wondering what sort of machine learning tools and what sort of mathematical tools are going to come to the forefront now that quantum computing is on the verge of becoming mature technology.
So yeah, I'd say statistics, statistics, I'd say do Kaggle competitions. Especially if you're a beginner, just do them incessantly. If your question is, "Shouldn't we learn Python or R?" It's the wrong question in two ways. One is, the language is the least important thing, Focus on the methods. Two is, you should learn both of them. You should probably have a look of Julia as well.
I think curiosity, entrepreneurship and rigor, which is a very rare combination, those personality traits. With those personality traits, it's going to be key. I think being experimental, this one is a real problem for a lot of people.
So one of the ways in which data science is so unlike IT is data science is about experimenting. It's about trying though you'll fail, about not knowing what you're going find until you get there and that's a huge challenge for a lot of people, both from the executive side of the world and the IT side of the world.
So dealing with being comfortable with uncertainty and being comfortable with uncertainty about your own career. No one knows what a data science career path looks like. No one knows what it's going to be like in the future. So it's having a certain courage, a sort of comfort.
We've got a super energetic guest for you, Josie [inaudible 00:39:50] So what do you need to know about Josie is that he's a serial entrepreneur who uses data and technology in his businesses. So Josie is involved in initiatives such as metal roofs, construction, designer glass specialties and others.
At the same time, he actively leverages data science to improve the way he approaches his customers, the way he sells to his customers, the way he services his customers. And he's seen massive success from that initial.
What do you see the future looking like and where do you think the world's going? What should our listeners prepare for that's coming in the next five or 10 years? What do you think the world of data and technology will be like?
So I do think it's a bit scary with the ... like here recently, some Toyota engineers, they built a robot that can beat any player, any statistic on shots. It made 100% accuracy; free throws, three pointers.
So data scientists are getting very, very good at creating this AI. So there's going to come to a point where it's gonna start pushing us out, but that's where we have to create new ways because God has given us this mind and this ability and so we have been amazing opportunity to leverage that.
I do think certain aspects of it are scary, but I think it's going to be one of the most amazing opportunities for those that focus on creating and not getting bogged down in looking at the dark side of it, but keep the mind positive and make sure ... and to use that except as poison or fuel.
And so when you see things that are coming in scary, that are like, "Oh man, where am I going to be? How am I going to do that?" Use that as fuel and not poison, and so I really think that the future is a ... we're going to work hand in hand with bots and I mean, we're gonna have a lot of sophisticated things within our life.
Everything is going to have a sensor on it. And we're really focused on the Internet of moving things. That's gonna be a big part of our lives or we're going to have technology always around us and I'm not ... my wife started me in Instagram. I've never had a Facebook ... I'm really not one that likes to consume except Flipboard. I love consuming Flipboard because it's amazing and it gives me a lot articles I really like.
But just finding those things that help you be the best you and that is going to help you to create the best products and services. So whatever you consume is what you're creating from, so you have to be very, very safe with what ... safeguard your mind, safeguard what comes in.
If you catch yourself just consuming stuff that you know is not producing good results, cut it out and do ... be self disciplined and really focus on that and don't limit yourself because it's an unlimited world out there, especially with the skills that you have.
Today ... energetic guest on the show today, Lucy [D’Agostino Montgalen 00:43:19]. Lucy is a person of many, many, different talents and different skills. So what you need to know about Lucy is that she started off by studying a Bachelors in Religious Studies and Italian, right after that she has successfully switched her career into data science. She's just completed a PHD in Biostatistics. She also runs the 'Our Ladies' group in Nashville, Tennessee.
As we talk about that for today, what sort of is ... from your perspective, from all of the things you've seen, we've covered so many topics just even in just this podcast and I'm sure you have a much broader exposure to the field in general.
From everything you've seen, where do you think the field of data science is going and what should our listeners prepare for to be ready for the future that's coming ahead in the next three to five years?
Yeah, that's a great question. So I think in general, we're moving to that age of information overload, which has two consequences for data scientists. So the first, I think, is largely really a good thing.
So when I see a study now, I want to know exactly where the data came from and how it's analyzed. And often if it's something that I actually think is important to me, I want to be able to reproduce the results. I see this more and more of this craving for even more information even though we're in this information overload space, because we have the ability to share information so easily and I think it's really cool and it's leading to really neat innovations.
What it means as data scientists, is that we're really pushed to use best practices in terms of reproducibility so that this can be possible. So things like version control and working in a scripting language instead of the gooey and documenting and justifying data changes and all that kind of stuff.
I think it's becoming increasingly important because we have this ability to transfer information so easily. Then the second consequence of data or information overload, it would be that it can be hard ... this reflects on what I was saying earlier, but I think it can be hard for the general public to be able to sift through what's important and what's noise.
So I think that means that as data scientists, we need to brush up on our communication skills and being able to distill really complex things, this really complex model that I've just had ... fully documented it for those people that want to be able to reproduce it. I also need to be able to condense it into a digestible sound bite that doesn't water it down, but also doesn't overload the public with unnecessary information.
So that's kind of what you were saying before about being able to make it exciting and interesting, but somehow still hold onto the truth that comes from it, being able to make sure that for communicating things that are both true and also important and not being able to sift through all of the nitty gritty, they get it. They condense down to the single point that we're trying to get across and I think that is also going to be really important in the coming years for data scientists to be able to gain that skill.
So aside from being able to fit that really fancy model, being able to explain it in a really easily digestible way so that people can actually utilize the information that you gleaned from it.
There we go. That was something special, wasn't it? 16 views of our 16 biggest AI experts who were on the show talking about what the future of data science looks like.
I hope you enjoyed and learned tons from this very special 200th episode of the Super Data science Podcast. Here's to another 200 episodes yet to come. I can't wait to interview more guests and to share their thoughts, their ideas, their perspectives with you and thank you so much for being part of this journey, for being part of the Super Data Science Show. Couldn't have done it without you and I'm so, so excited and so grateful that you are part of all of this that we do here, that you inspire us, inspire our guests to keep sharing their thoughts and ideas.
Thank you once again and I can't wait to see you next week. And until then, happy analyzing.