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You are listening to the HumAIn Podcast. HumAIn is your first look at the startups and industry titans that are leading and disrupting artificial intelligence, data science, future of work and developer education. I am your host, David Yakobovitch, and you are listening to HumAIn. If you like this episode, remember to subscribe and leave a review. Now onto the show.
Welcome back listeners to the HumAIn Podcast. Today our guest speaker is Chris Bishop¹. Chris is a multimodal nonlinear careerist who has launched an exciting new course on LinkedIn Learning called Future-Proofing Your Data Science Career.
Chris and I originally met at the ODSC Conference in New York City, where we were exploring topics around data science and research and fast forward now to our digital first society with so many career transitions, I had the firsthand look at seeing the beta and the launch of Chris’s course on LinkedIn. It is a lot of fun. And Chris, thank you so much for joining us on HumAIn.
Thank you, David. I’m delighted to be here and look forward to talking about what lies ahead, certainly in data science. There is a lot of really interesting stuff going on, potential opportunities to do really interesting creative work that not only is fulfilling, but can help the Planet.
We’re seeing it already with research sciencing the heck out of vaccines and cure investigations as well as new models around how people get healthcare and how people telecommute. Here we are, in this environment full of Zoom meetings and again it all represents lots of opportunity across a range of disciplines and verticals, and especially for data scientists.
I think it is incredible that having you here today on HumAIn there’s so much we can talk about, like reopening the economy and re-imagining education and even re-imagining careers. You call yourself Chris, this nonlinear multimodal careerist. Can we break down? What does that mean? Let us set up the historical perspective for our audience.
I have a degree in German Literature from Bennington College, a small liberal arts school in Vermont. I started music after getting out of school. I got a gig touring with this band called McKendree Spring. I did three albums with them, including one at Electric Ladyland Jimi Hendrix Studio in New York. We opened for bands like the Eagles, ZZ Top, Fleetwood Mac and Frank Zappa and Weather Report.
Anyway, the band eventually broke up. I moved to New York, became a studio musician, played with people like Robert Palmer. I did two tours and a live album in London at the Dominion Theater, playing bass and keyboards and guitar with him, played with Chuck Berry at the Meadowlands and played with Bo Diddley at Danceteria.
And the bottom line, I ended up in the jingle business, writing music for television, running a Sync Claveria, which was the digital musical instrument at the time, played bass and sang on the first kid catch jingle, ‘give me a break oh, give me a break, break me off a piece of that kit kat bar’ And because you get residuals, these cheques would just appear in your mailbox. It was pretty amazing.
And then I became intrigued by the web and taught myself to be a web producer and worked at a couple of seminal interactive agencies in New York. And then much to my surprise I was hired by IBM into their fledgling corporate internet programs division. And I worked at IBM for 15 years doing lots of different things. We were pulled into strategy roles pretty quickly as these lines of business executives with P&L’s realized they could actually sell stuff and service customers and interact with journalists and analysts and partners using this wacky web thing.
And I did a lot of social media stuff there, worked for the IBM Foundation but then was eligible to leave about 7 years ago and left. I’ve been out on my own doing freelance writing and speaking. And to be honest, I realized when I got invited to give a keynote at Bennington College about my multiple careers, I’m the poster child for the way today’s learners are going to work. They’re going to have multiple careers.
The U.S Bureau of Labor and Statistics says today’s learners will have 8–10 jobs by the time they’re 38. Other data says 85% of the jobs that today’s learners are going to do haven’t been invented yet. That certainly applies to data science. They’re going to use technology that doesn’t exist today, that’s going to make things like smartphones look really clumsy and stupid and way too big, and you had to carry it around in your pocket, what a waste of time grandpa, that’s pretty lame.
And so I’ve just been talking and thinking about this and as luck would have it, soon after we met at the ODSC Conference, I connected with a gentleman from LinkedIn Learning and he said, I think your content would be valuable to the LinkedIn Learning audience and here we are, my course just came out, on May 1st. It is out in the world. So that’s my nonlinear multimodal path so far.
I think it is such an incredible story to see how anyone can reinvent themselves, because it is all about not only the classic follow your passion and some of these topics that we’ll hear you talk about as we discover your course during today’s live show and for those playing back on our Spotify or Apple platforms, but also to see that it is all about persistence and commitment and discovering how you get into industries. And when we think of the workplace models, what was work in the 1920s, the 1950s, the 1980s, even today. Where do you see data science fitting into those historical workplace models?
I think for one thing, the workplace model is kind of blown up. As we’ve seen people can work from home or from wherever on the train or in a Starbucks and be more productive, because they’re more in control of their time. Now, there are things you miss such as serendipitous interaction with other employees or coworkers or partners or whatever in the workplace, but I think models around how people collaborate and work have been completely blown up by the pandemic.
You have the gentlemen and women who run for example, large financial services organizations saying, why are we putting 7,000 people in a building in Midtown and paying not only the rent, but the fully burdened rate to heat it and cool it and run phone lines and IT support. Let them do it in their pajamas and let’s do something else with the money. That’s a little simplistic, but that’s certainly the direction.
And I think, again, data science is going to have lots of opportunities to take these learnings, as you said about education. So all these kids are learning from home now. It feels bad for the high school and College seniors who didn’t get to really graduate. They are driving in a car, holding up a sign, which is not quite the same as convening on the quad and having some illustrious speaker give you words of wisdom, but again, it is a new model. And I think the opportunity again, for data science to rethink how information is shared and distributed represents a huge opportunity.
This opportunity is something that I think is just changing every day, but we’re going to be moving to a society where everything is tech first or digital first, even as we reopen the economy, re-imagine work. What we’re seeing today has a lot of history.
In fact, when we’re thinking about in your course and in data science, all these big tools like Python and Scala and Julia and all these interesting languages with frameworks like TensorFlow, there’s a lot that led up to them. What have you seen? What are some of the precursors to today’s data science tools?
So one of the devices I always love to cite and I think to be honest, they edited it from my course, but, I always referenced something called the Antikythera mechanism and I encourage your listeners to check it out. It is a shoe box sized device, It was discovered in the Mediterranean in the early part of the 20th Century by some sponge divers, but it is this very complex device handmade of course from brass and wood.
And it was able to calculate the Orbits of the four Planets closest to the Sun to register cycles of the Olympics every 4 years, to represent Solar and Lunar eclipses, I think over a 100 year span, no one really knows who built it. They describe it as the first analog computer. But again, the idea is humans have been creating devices to make work simpler and faster and easier for literally thousands of years.
So this is the first example that I cite of human brains, getting together to design and build not only design, but create a really complex device that delivered value and that made it easier, made their lives simpler. I actually was in Athens and went to the museum where this is housed and it was out being repaired and I can’t believe that it started here. There are lots of devices like this. I think of Charles Babbage, in his first, is the Difference Engine then the Analytical Engine, a physical device that was designed to do addition and subtraction and eventually multiplication and division.
And it was never really built in his lifetime, but they did in the 70s when a team at the British Museum put together a version of it that actually works. So there’s lots of history and precedents for the kinds of tools that led to humans manipulating data, that is what we do today with algorithms and using artificial intelligence and machine learning. So it is part of a long arc that goes back thousands of years and is going to continue for thousands of years.
It is so incredible to see where modern math has the magic. I know when I was growing up by participating in math competitions, and one of the things that was prided from the best teachers in the industry was, could you do mental math? Can you solve these complex trig problems with no calculator?
Sometimes even no pencil or paper. It was really to see those proficiencies, but we’re seeing now that the business perspective has changed. We’re trying to build applications. We’re building technologies that are all about automation and scaling into the #fourthindustrialrevolution. Chris, what have you seen or why, and where are these data science applications being explored and being expanded?
I think an interesting example to share is the New York Stock Exchange. So they opened yesterday after the Memorial Day Weekend. Governor Cuomo rang the bell with a mask on, but the bottom line is actually with all due respect, that space is basically a catering hallnow, because there are algorithms that are doing most of the trading.
There are certainly people in there doing work, but back to your comment about math, algorithms can make assessments and recommendations, buy and sell way faster than a human can. So that’s the model, it is like, let’s use tools that will help us move faster, work better, work more efficiently and improve productivity.
And that’s one clear example. We are also seeing AI being used to help radiologists examine X-rays. And now their level of accuracy has exceeded humans in every setting, but things like lung cancer can more accurately identify tumors and AI than say a seasoned radiologist.
it is the application of data science and it is a couple of examples happening in all kinds of disciplines and verticals. And I think it is exciting, including travel and transportation. A lot of data science is being put into the unfortunately scrubbed mission today, but hopefully we’ll see the SpaceX launch on Saturday.
That’s going to open up incredible opportunities for data scientists, not just around NASA and ancillary businesses. Keep in mind that there’s a whole support system and ecosystem that lets those two guys sit in that capsule and blast off to the ISS. Lots of businesses and data scientists working to put that together to make that go. And the next frontier is Mars or mining Asteroids.
I talk a little bit about Solar Sails in my course, the ability to use photons to propel devices through space as the source of energy. That’s a data science problem. Mining of Asteroids is a #datascience problem and weather is a data science problem as well. IBM bought the Weather Channel for lots of different reasons, one of which is to equate trending in purchasing with weather patterns and what kind of insights they could gain from data crunching. It is pretty well.
I think what’s so wild about these acquisitions is everything’s about the data and there’s such a deluge of data. As we’re talking about SpaceX, the first attempt to bring humans back into space from North America, we’ll see where those missions go. But there was data that informed the teams that they had to postpone this launch to help ensure it was safe and successful? And data is everywhere. There is such a deluge of data. Why is data growing? Why is it everywhere now?
Well, I think there are lots of reasons. Everything is generating data now and the idea is that data is empowering. It can also be disabling. And there are certainly conversations about privacy and confidentiality. But I think at the end of the day, the ability to capture data and represent it accurately is a good thing. I like being prompted based on my preferences about things to buy or restaurants to go to or movies to see or flights to book or whatever.
I think that’s all good. I think also the burgeoning IOT, when everything from your shoes to your car, to your garage door, to your refrigerator has an IP address and is collecting and distributing data as daunting but that’s partly where it is coming from. But again, using tools like AI and #machinelearning, we can take that data and make sense out of it and rationalize it, not only to live more comfortably, but also to drive innovative business models. I think that’s a key as well.
And it is so interesting to think about these business models, because there’s so much non-traditional work that is going to change our world. And we’re thinking of these big ideas that today seem very far away outside of our scope, like mining Space Rocks, or having life cohabit today on Mars. But some of these actually, could they be future data science opportunities for people who are learning the code today?
They could be absolutely. One of the things I described in my LinkedIn Learning courses is the fact that interesting new careers, jobs and certainly in data science are emerging at the intersection of unlikely or historically disconnected disciplines. So by that, an example I cite is Nanopharmacy.
So they’re now creating ingestible bots that can carry Pharmacology at the atomic or molecular level, to the affected area, to the tumor or to the wound or to the area where the medicine is needed. So that job, that career is going to require skills around Nano mechanical engineering and there’s that fantastic center at MIT, If just, your listeners are unaware and they publish newsletters as well.
It is a $400 million facility on the campus that MIT focused on Nanoengineering. And the other scale is going to be around Pharmacology again at the atomic or molecular level. How do you create medicines that can be targeted very specifically? They’re based very often on genetic information we’re seeing like RNA and DNA approaches to developing vaccines and cures for COVID-19.
All that kind of science that’s going on now in these crazy times is going to be expanded. it is going to set models and precedents for how medicine is created and delivered, how healthcare and biomedicine is created going forward. I think it is, again, as crazy as these times are, I think we’re going to see some innovative uses of data science coming out of it. That’s just one area, one discipline.
I think definitely healthcare is one of the big winners in #digitaltransformation in 2020. And this is going to cause a complete renaissance in the health tech community for technology. Whether we’re looking at temperature scans or CT scans, these are things that we could not even think about using data to generate insights 5 years ago. And now this is so quickly accelerating. It is just helping me be reminded that careers are always changing.
And the data science industry is continuing to change. Chris, you’ve shared with us that you’ve been through this journey of eight careers and they’re constantly evolving into new seasons and trends. And one of the core pillars you’ve established with your course on LinkedIn is this Future Career Toolkit. Can you share with us about these tools and how do they work?
Sure. So again, after I was asked to give this keynote address to a group of seniors at my Alma mater, it dawned on me and looking back that I’d had these multiple careers and again, having done some research around trends in the global economy, I’m a big fan of books by economists, a great book by the way I just finished is called More.
The author is an economist. He writes for The Economist. The book is a fantastic history of the global economy from the Iron Age to the present. But he captures how the economic model has changed and gets a lot of implications around data science, for sure because there’s always data on some level.
This reminds me, I was at an event recently sitting next to an economist and someone at the podium was speaking about future jobs and global economies. And that many of the jobs from 50 years ago, don’t exist today. And he turned to me and he goes, ‘thank goodness they don’t exist today’. Like, can you imagine if we were still doing stuff we were doing 50 years ago? I think of health care, even medicine. But anyway, so my toolkit is me reflecting on how I navigated these careers and trying to codify them into these future career tools.
And I always say this perspective was banished in the crucible. This is not something I did not read a lot of academic papers and cite a lot of academicians and scholars. This is like me schlepping around New York with my base on my back in the subway trying to get a gig as a musician where, getting the job as the job. I call them voice antennas and mesh and simply put, voices finding your brand, your value prop, what it is that you do that no one else can do.
And I use an ideation technique based on analyzing or selecting a favorite movie, TV show, even a game, a book, could be a graphic novel and then teasing out, bubbling up what it is about that book or movie or game or TV show that resonates with you, using that to set up criteria around what your voice is.
Then the second one is antenna, where I asked people to look at sources, where are conversations going on around these topics? So for me, my favorite movie recently is Blade Runner 2049. So I’m a tech guy and I’m into future culture. So the follow on for antenna is where are those conversations going on?
So I looked to BBC Click, the TV show, that’s a weekly program that explores interesting new technology and how it is being used in business by certainly looking at the New York Times, Bloomberg Technology is a source of information about future tech and culture. So that’s the antenna piece. And then the third piece is mesh, which I like to describe as a three-dimensional #datavisualization of your network.
It is like trying to track down based on the antenna exercise, where the people and companies and organizations are having conversations about the topics you’re interested in, where is this work getting done and tracking them down and connecting with them, getting on their proverbial radar and finding out what groups they’re in, who they’re connected to. I describe it as doing the Twitter math, like who are they connected to? Who do they follow? Who follows them? Then who do those people follow?
And building out this mesh of contexts is again a numbers game at the end of the day. So it is the more people that know who you are and what your value prop is and what you’re interested in, the better, your odds of finding your next data science career and the one after that.
So exploring these three areas of the toolkit, voice and tenet and mesh, they’re all so important. And I think they’re all underrated. I don’t think enough people spend enough time working on them. For me, voice has been something that well, we’re talking about it on the podcast by building that over time. And I think soft skills are often missed in a lot of the communication for data scientists. Why do you think that is? And how can we change that?
I encourage data scientists, once you go through these exercises, but keep in mind that you want to try to be moving up the value chain, to use an economics term. And the model I use is music based because I spent a lot of time in the music industry for 20 years as a professional bass player in New York. You start out as a musician and then maybe eventually you work your way into a role as an arranger, and then maybe you become a composer and then the final destination for me is being a producer.
That’s the path that I took, certainly in the jingle business. So by the end, as a producer, I was hiring people that I thought were great to play on my compositions. So the same model applies to data science. So get in, first of all, get into a disciplinary vertical that you’re interested in, a topic area that you’re passionate about because then you’ll be successful if you’re interested in it and then find ways to step back and provide more strategic higher level business perspective, and respect the fact that you are knowledgeable, more than you think about how say a business is run and some it is not for everybody.
Certainly some people are content just to be the session-based player or the data scientist, helping do the work on the ground. But I would encourage data scientists again, as this is such a rapidly evolving and morphing field to think about how to move up into say a management role or a strategy role, to not be afraid to contribute ideas about solutions for innovative products and services that a company might take on to drive their business model. Because you, as a data scientist have a very unique perspective about what kind of data gets collected, how it gets used, how it gets applied, what the benefit of doing that might be.
And it sounds like a lot of that from honing your voice and sharing your opinion and talking about the tech leads into, as you’ve shared Chris, the antenna. Building this intuition to see where is the industry going? Where’s the marketplace? I’m sure just like yourself, I live my life in my email inbox. Sure, I’m using different tools like Slack and Teams today, but I will tell you the amount of newsletters I get weekly goes upwards of a thousand.
I can tell like there’s so much and we have to build these ways to go through the data deluge. it is just the data deluge of information. We’re living in a world where there’s so much access and equity in this information, but how do you act on that information? How do you form that antenna? That’s so important.
So as you said before, this deluge again, when I say in my course, the good news is there’s lots of sources of information and the bad news is there’s lots of really good sources of information. So, managing, parsing and doing triage on the tsunami of info is the challenge. And I struggle with it myself. I still do. I get lots and lots of emails, but in terms of building the antenna grid, it is a grid model.
I think there are a lot of indicators like your inbox, like what newsletters do you subscribe to. Those indicate subliminally or not, topics you’re interested in, you subscribe because it aligns in theory with your voice. I would look at bookmarked websites as well. Like what have you bookmarked? What’s the stuff at the top of your browser that you need to get to quickly?
The implication is that these are topic areas you’re interested in. The broader implication is, it represents focus areas for a data science career. If you’ve got QuickBooks that you use all the time, maybe you’re interested in writing some code that helps people deal with finances in a better way, or maybe Evernote, so you’re looking at managing notes, doing some analysis of the sources of information already, but the challenge is certainly to parse it down.
The other thing I created people to do is set some kind of temporal boundaries around the deluge like, I’m going to look at the BBC Click once a week, I’m going to check the New York Times website once a day, I’m going to go to the John Hagee Twitter feed, every other day or whatever. So you manage, put a box around some of this stuff and somehow that’s one way to do it.
I think that’s also smart because we can get caught into this learning trap where we go down these rabbit holes of learning theory without applying the results. With data scientists that I work with each day, I tell them we need to apply the results, translate that to the business. And part of that is knowing that, you can learn forever. As you’ve gone through your eight career iterations and myself, I think my learning list of Python packages is just endless. There’s a point that you have to know to exit and just know that you keep picking up the learning as your time permits.
Yeah, I think learning is key. I heard it stated by some writer recently that we have to stop thinking of education as an event that happened in time.
Isn’t that an interesting perspective? Like education is not the 4 years you spent in College or the 12 years in K through 12 in your town or wherever you grew up. #Education is something that goes on your whole life. It never ends, especially in this environment. In the second decade of the 21st century learning is a non-stop process. You have got to be doing it your whole life. Just like networking, the same kind of thing.
You never wake up one morning and say, I know everything I need to know. And I have all the contacts I need. I can chill now. No, that’s never going to happen. So you need to get into a mindset where every day you try to learn something new. Or in terms of the networking thing, I always admonish, especially university students and early career millennials at the end of the week, if you haven’t added 3–5 people to your LinkedIn network, get to it.
Make it a job, 5 o’clock Friday, if you missed a deadline, get out find people back to the mesh thing who are doing interesting work in this space, you aspire to be in and track them down, send them an InMail, message them, track down their email on a blog that they post or on a corporate site that they’re on and build your network. it is the old adage described to showbiz, but true in every business. Now it is not what you know, it is who you know, so building your mesh is critical.
I completely agree there. I recently ran a report called ‘Bounce Back From COVID-19’ where we were giving this presentation to College graduates and seeing what new opportunities could be for them in the software and data science world. And I talked about all these platforms, but I really honed in on LinkedIn. I talked about a lot of common things that people don’t know how to discover on the platform better.
Hidden gems, such as getting those daily emails with the titles you’re interested in, or like you mentioned, Chris, just as important as customizing a message. Hey, Chris, love to connect with you. I just studied your LinkedIn Learning course on the Future of Data Science Careers. It would be an honor to connect. Who would not accept that invitation?
No people are going to want you to have their email address, but I’d say 9 times out of 10, you’re in higher odds and that people are ready to have a conversation and get connected. I describe LinkedIn as the pleated pants of social networks, but it certainly is from a career perspective as to Lingua Franca as the coin of the realm. You’ve got to have some degree of a pimped out LinkedIn profile to get people’s attention to hire you, to invite you to be part of a project or whatever. So, I’m a big fan.
And so as we’re still in this digital world, that’s being re-imagined, we hear that the Bill and Melinda Gates Foundation is partnering with New York State to re-imagine education, re-imagine the workforce and this optimism, I think, can be founded in a good place. There is so much like we’re talking about here from not only the comments that we’ve heard on the show today, but don’t waste the good crisis. Winston Churchill, can we rise better?
And one of my big bets, this is the most contrarian thing I have said in 2020, is I think cities like New York city and San Francisco are going to be bigger and stronger and more economically vibrant post COVID than before. I think the aggregation economy is just going to couple and scale even further.
It is a very contrarian statement that we still have to see how that’s going to turn out. I think I’ve won the support of Mark Zuckerberg, because he has said that those remote employees will have salaries adjusted to their cities and States. What are you seeing as some trends, outcomes and predictions for data science or just the industry in general?
I think you’re right. Cities are always going to be the hub where there’s a lot of energy, ability to mix and mingle where disciplines can get connected where smart thinking can go on, where business and private sector and academia and public sector organizations can co-mingle and share ideas and innovate and develop solutions together. So I agree. I’m a fan of the city, for sure.
Along those lines though, my general advice, certainly to all careerists, but definitely to data scientists has always been and served me well, is, chase the maelstrom, find the chaos, go for the mayhem. So go where they don’t know what it is yet. And then you can be involved, you can have a creative role, you can do something interesting and innovative and be employed gainfully and be remunerated.
So it served me well for years and my multiple careers. I think of getting into the web biz. And so in 1995, I became enamored with this wacky new technology of the internet. And I thought this is probably going to have a global socio-cultural and business impact.
And to be honest, I thought there might be music within. I thought here is a website that certainly wouldn’t be mute. It needs some kind of underscore whatever that the IBM site would have, the big theme and then the software group would have their own version of it. And then they hire John Williamson, the servant technology group version of it and maybe there’d be like a more Marimba African themed, but it never happened anyway.
And I love it
But anyway, I went into this web thing and it served me well, it was an emerging technology that people didn’t quite know what to do with it. And people from all different kinds of disciplines and backgrounds were getting into it. So fast forward to 2020, the areas where I’ve encouraged data scientists to focus on, are things like certainly AR and VR. In the education space and in the medical space and then even in financial services, I would encourage them to investigate crypto assets, blockchain, and bitcoin. These are all going to be big opportunities, certainly 3D printing.
They used to print plastic chotskies and now they’re printing vascular tissue and organs, ears and noses. Certainly SpaceX, you can be sure Elon Musk has a hoard of data scientists who work in that project and all his projects. Travel and transportation is being transformed by hyperloop as well. Certainly not Tesla alone in autonomous vehicles, but the whole model around how people travel around the planet is being transformed.
Biotech, certainly education, almost everything you can think of is being transformed by technology and the implications are on data science. As I mentioned, David, before we were talking, I’m actually going to be the master of ceremony next week at a conference called Inside Quantum Technologies. And I’m going to be moderating a couple of panels. I was telling you, I had a conversation this morning with the gentleman who runs the quantum practice for Airbus.
He’s a Spanish guy based in Portsmouth, England, and his background is aerospace technology, but he’s been working with quantum sensors and how they apply to satellites. They run Skynet 5, which is this constellation of satellites that enable global telecommunications and navigation.
Exactly. That’s a general set of guidelines that I would encourage data scientists to explore because there is going to be lots of interesting stuff going on, so get to it. The opportunities are out there.
I think it is so fascinating that a lot of the technologies that you’ve been sharing today on our show to the listeners, Chris, could have been seen 5, 10, 15 years ago as moonshot projects so outside the realm of possibility and some of them are so close to being real today. Self-driving cars for example, I will close my eyes and we will be having self-driving cars because we already do.
We have these buses and these vehicles on the road which means how close are we to real quantum, like in the Devs show? How close are we to crypto? We’re seeing in Asia where crypto wallets are running systems and supply chains. So we’re having those shifts now and it is just incredible to see how technology continues to increase the speed of these changes. Chris, what final call to action would you like to share with our listeners today on the show?
So I would say, certainly to data scientists and really to any, and all listeners, in terms of careers and jobs, you’re going to do stuff that’s going to look like magic to me based on what was around, when I graduated from College, for example. So get to it. I want to see it before I exit the planet because there’s lots of really interesting opportunities to do fun and beneficial stuff in the data science space that can have an impact on humanity broadly on business specifically, and certainly on culture and society more generally. So, get out there and explore, please.
Excellent. Well, this has been Chris Bishop, a nonlinear multimodal careerist, and the author of Future-Proofing Your Data Science Career on LinkedIn Learning. Chris thanks for joining us on HumAIn.
Well, thank you, David it has been a pleasure to be here.
Thank you for listening to this episode of the HumAIn Podcast. What do you think? Did the show measure up to your thoughts on artificial intelligence, data science, future of work and developer education? Listeners, I want to hear from you so that I can offer you the most relevant trend setting and educational content on the market.
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