The Artists of Data Science

Watch the video of this episode: https://youtu.be/SWSLiGmnpao Find Dana Mackenzie online: https://danamackenzie.com https://scholar.google.com/citations?user=sQhKQ5cAAAAJ&hl=en Memorable Quotes from the Episode: [00:20:28] "At one point he realized something very fundamental and remarkable, which is if you switch the fathers and sons and you plot the sons side as the independent variable and the other side is independent variable, you get the same thing, you get the same fuzzy thing and you get the same correlation. And so correlation is something that is completely independent of causation." Highlights of the Show: [00:01:22] Guest Introduction. [00:03:02] Where you grew up and what it was like there? [00:04:23] As a kid, you loved writing, but then you ended up studying math at like the highest levels. Was that something that you foresaw happening? Were you always into math? Was it like a choice between math and writing? How did this play out? [00:10:13] if anybody who wants to develop and flex writing muscle, do you have any tips for them on how they can develop and cultivate this skill? [00:14:18] In view of your book "The book of Why", what is this computational cognitive faculty that humans certainly acquired that our chimpanzee cousins did not? [00:17:28] Concept of counterfactuals. [00:24:48] "Every statistics book says correlation is not causation. And they forget to tell you what is causation." [00:41:55] What is the ladder of causation? [00:48:57] "Smoking causes cancer", discuss. [01:01:11] What is the do operator all about? What makes it so revolutionary and special? [01:16:00] It is one hundred years in the future. What do you want to be remembered for? [01:17:58] What are you currently reading? [01:21:13] What song do you have on repeat? [01:25:29] What is one of your favorite comfort food comfort foods? [01:25:53] What have you created that you are most proud of? [01:26:03] Who inspires you to be better? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://www.youtube.com/watch?v=wjueYMuS7kw Resources: https://calendly.com/harpreet-comet-ml/30min https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning https://cloud.google.com/architecture?doctype=concept%2Creferencearchitecture https://craftinginterpreters.com/ https://fullstackdeeplearning.com/spring2021/lecture-11/ https://kubernetes.io/blog/2020/12/02/dockershim-faq/ https://kubernetes.io/blog/2020/12/02/dont-panic-kubernetes-and-docker/ https://missing.csail.mit.edu/2020/version-control/ https://theartistsofdatascience.fireside.fm/kurtis-pykes https://www.amazon.ca/Software-Architecture-Trade-Off-Distributed-Architectures/dp/1492086894 https://www.youtube.com/watch?v=a6kqyqTNJM4&list=PLhr1KZpdzukdeX8mQ2qO73bg6UKQHYsHb https://youtube.com/playlist?list=PLhr1KZpdzukdeX8mQ2qO73bg6UKQHYsHb Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Watch the video of this episode: https://youtu.be/AAAV0wOLqQo Find Christian Espinosa online: https://christianespinosa.com/ https://www.linkedin.com/in/christianespinosa/ Memorable Quotes from the Episode: [00:24:58] "...the final step is Kaizen. Kaizen is a is a Japanese word that means constant and never ending improvement with any of the six steps prior or the entire methodology. It's a journey, and you're not going to perfect it right out. The gate is taking this first step and the next step and the next step, and then making improvements as you move along. So that's the seven steps to the secure methodology." Highlights of the Show: [00:01:15] Guest Introduction. [00:02:43] Where you grew up and what it was like there? [00:05:43] Does Christian has the crazy interest to climb mountains? [00:06:13] When you're growing up as a kid man, did you ever think that you'd be this crazy ultra marathon running Iron Man, mountain climbing cybercriminal fighting awesome individual? [00:06:48] Where does that self rigor to be able to want to put yourself through these really challenging types of situation come from? [00:09:19] What does it mean to be the smartest person in the room? What does that mean to you and when is it a bad thing? [00:12:33] Is there a correlation or a relationship between the need to be the smartest person in the room and having like a fixed mindset? [00:14:14] Who are these "paper tigers" and why are they so dangerous? [00:19:20] How can you tell that somebody knows what their 'why' is? How do you assess for fit against a cultural fit? [00:20:53] What is "secure methodology"? What are the seven steps involved in it? [00:31:08] Do you think it's possible to identify whether we have a real growth mindset or a false one? [00:33:02] Being congruent with your belief and the philosophy behind growth mindset. [00:33:57] What are these NLP presuppositions in the context of your secure methodology? [00:35:00] What are your top two favorite presuppositions for the communication part of the security framework? [00:39:49] What is mono tasking? [00:43:17] What are some of the NLP presuppositions that we can use to remind ourselves that it is time to get down to to multitasking? [00:45:28] What are a couple of presuppositions that we should have in mind for the Kaisen? [00:46:38-00:46:38] Talk to us about the four phases of kaizen. [00:50:57] It is one hundred years in the future. What do you want to be remembered for? [00:51:37] Random Round. [00:51:37] When do you think the first video to hit one trillion views on YouTube will happen and what will it be about? [00:52:11] What do most people think within the first few seconds of meeting you for the first time? [00:52:41] What are you currently reading? [00:53:35] What song do you currently have on repeat? [00:53:59] What's your earliest memory? [00:54:32] When was the last time you changed your opinion about something major? [00:55:37] What's the best piece of advice you have ever received? [00:56:29] What's the right way going about finding a mentor in your experience? [00:59:01] Who was your favorite teacher and why? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/3GG9snF8p7o Find Kourosh Alizadah online: https://www.linkedin.com/in/kcalizadeh/ https://philosophydata.com/ Memorable Quotes from the Episode: [00:20:22] "...one word that's very commonly used in philosophy is the word substance and in everyday language. It just means like stuff. But in philosophy, it means like the substrate upon which all the properties change, right? So like what is the substance of a stone that stays the same even when it changes color or breaks or something like that." Highlights of the Show: [00:01:16] Guest Introduction. [00:03:34] Where you grew up and what it was like there? [00:04:41] How did you figure out who you want to be? - What did you think your feature is going to look like? [00:06:13] Do we still have philosophers who study "philosophy and ideas"? [00:07:59] The philosophy of Data science is if we had to kind of pin that, would there be a philosophy to Data science or of Data science? [00:09:22] What is Data? How is it different from information or data and information? Are they the same thing? [00:10:29] The concept of "philosophy data project". [00:11:41] Transition from a capstone project to flat iron Data science boot camp. [00:18:39] Did you actually read a lot of books? [00:24:25] What are prediction probabilities? [00:55:10] Random Rround [00:55:12] When do you think the first video to hit $1 trillion views on YouTube will happen and what will it be about? [00:56:07] What do most people think within the first few seconds of meeting you for the first time? [00:56:24] What are you currently reading right now? [00:57:18] What song do you currently have on repeat? [00:58:29] Pet, peeves. [00:58:37] Who are some of your heroes? [00:59:30] When people come to you for help, what do they usually want help with? [01:00:01] If you lost all of your possessions, but one, what would you want it to be? [01:00:14] What fictional place would you most like to go to? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/SOW9wUY3FpA Resources: https://medium.com/@grepdennis/how-a-sql-database-engine-works-c67364e5cdfd https://medium.com/building-the-metaverse/evolution-of-the-creator-economy-9e038e8411af https://medium.com/data-driven-fiction https://snap.stanford.edu/data/roadNet-CA.html https://theartistsofdatascience.fireside.fm/guests/anderson-silver https://theartistsofdatascience.fireside.fm/guests/donald-j-robertson https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309 https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507/ref=sr_1_1?keywords=inspired&qid=1637361494&s=books&sr=1-1 https://www.amazon.com/The-Feed-Season-1/dp/B086HVT7JH https://www.hel.fi/uutiset/en/kaupunginkanslia/a-new-minecraft-city-model-introduces-helsinki-in-more-detail https://www.linkedin.com/in/dkjapan/ https://www.tigergraph.com/resources/ https://www.youtube.com/watch?v=YT0CScFzp1o Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/cSOXStI5sjg Find Steven Cardinale online: https://twitter.com/scardinale https://www.linkedin.com/in/stevencardinale/ Memorable Quotes from the Episode: [00:53:20] "I was talking to somebody the other day and I said, What are you selling because I'm selling media coverage for football teams? I'm like, OK, great, you know, because all football teams need people to know where they're at. Nothing but what are you really selling? Well, I'm selling it to mostly the high school teams, and really what I'm selling is, you know that parents can see their kids and media coverage. Great. What are you selling? It took him a minute and goes, Well, I'm selling the fact that parents are spending money to be have their kids on a football team. They want to see their kids names in the newspaper. So now we're starting to understand something a little more interesting." [00:11:21] "...if you think about a data scientist, you guys are alchemists, people who work with, you know, the big data lakes and the uncertainty of data and then convert it into a decision that is the essence of alchemy." Highlights of the Show: [00:01:24] Guest Introduction. [00:04:43] Where you grew up and what it was like there? [00:04:41] How did you figure out who you want to be? [00:07:34] What are the two definitions of entrepreneurship as mentioned in your book? [00:12:34] What are the terms Prima Materia and the Philosopher's Stone. How is it that they fit into this three step process? [00:40:14] The "Albedo stage". What's so unique about this stage? [00:43:03] The idea of pollination and how it helps us grow. [00:48:40] "Ego is the enemy." [00:49:51] When we're moving through these three stages, like, do they happen sequentially, concurrently, all over the place? How long should we be spending each? [00:51:44] One part that I really enjoyed was just coming up with better questions because I feel like this is something that I've heard from my mentees. They really struggle with is like, they don't even know why questions are important, let alone how to even come up with better questions. So can you share some tips on how we can do that in our work? [00:55:43] The rubato mindset. How is this different from the other parts that we've discussed? [01:00:29] What are some tips you can share with us for how to use and implement these ideas that you talk about? [01:02:18] It's one hundred years in the future. What do you want to be remembered for? [01:03:00] When do you think the first video to hit one billion views on YouTube will happen? What's it going to be about? [01:05:01] What are you currently reading? [01:06:12] What songs do you currently have on repeat? [01:07:15] What's your go to dance music? [01:07:44] What is one of your favorite smells? [01:07:58] In your group of friends. What role do you play? [01:09:05] What's the best piece of advice you have ever received? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/IRkGuRMnZ6o Resources: https://fossa.com/blog/analyzing-legal-implications-github-copilot/ https://github.com/jupyter-naas/awesome-notebooks https://hbr.org/2009/01/picking-the-right-transition-strategy https://papermill.readthedocs.io/en/latest/ Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/6UJED0scgy4 Find George Firican online: https://twitter.com/georgefirican https://www.linkedin.com/in/georgefirican Memorable Quotes from the Episode: [00:42:51] "So I think everything needs to start on the business side first, so ideally, that's very clear for everybody where the business with a five year plan, if you will, for the business is so that anything else is a strategy to support that plan, right? Otherwise, it's kind of just wishful thinking. If if you want to go to Mars from a Data perspective, how can you create models for the company to be able to do that? But then if the company doesn't want to get there, then it's pointless. So that's why it's you need a business to take that first step." Highlights of the Show: [00:01:29] Guest Introduction. [00:02:53] Where you grew up and what it was like there? [00:04:02] What did you think your future was going to look like at the age of 15? [00:08:2] What was the nudge that got you into Data? What was the experience that you had that made you realize that Data was right for you as a great teacher? [00:09:45] As data scientist, machine learning practitioners, we're end users of the data, right? [00:12:22] What the heck is Data governance? [00:14:26] Responsibilities of a data analyst. [00:15:47-00:15:50] Can anybody be a data steward? What does a data steward mean? [00:19:33] Metadata, master data, what are those? What do they have to do with data governance? [00:22:19] Why should Data scientists care about these types of data? [00:23:48] Discuss data governance in action in the workplace. [00:27:28] When you say business driver, what does that mean? [00:29:1] So what is the goal of the organization at a high level? [00:30:02] What are your concerns around data governance or is there kind of a a business way to ask the question so that we can translate it into our own lingo? [00:31:06] Why is it so painful to get to have the report or access them from a dashboard in a timely fashion? [00:33:14] What would be the types of individuals that we would want to see on the council? [00:35:11] What are the biggest challenges you foresee her facing when he's starting out a Data strategy at this massive organization? [00:37:05] What can Stephen King teach us about Data governance? [00:38:41] What are Data Management and other such principles? How do we identify these principles? [00:41:18] What does Data strategy have to do with helping us get ahead in our Data careers? [00:42:24] How can we help our organizations define a data strategy if we find ourselves in this position of having to to build a Data strategy? [00:43:30] Are there any blueprints that exist to help create a Data strategy? [00:44:24] What the heck are the maturity models like? [00:45:48] Can we have the George tech and maturity model? Does that exist? [00:50:37] What is the difference between data scientists and data analysts? [00:53:33] Does data governance care about unstructured data or is it only about structured data; how's that? [00:54:32] It's 100 years in the future. What do you want to be remembered for? [00:54:59] When do you think the first video to hit one billion views on YouTube will happen, and what will it be about? [00:55:55] What do most people think within the first few seconds of meeting you for the first time? [00:56:46] What are you currently reading? [00:56:46] What are you currently reading? [00:58:13] Pet peeves? [00:58:44] What's on your bucket list this year? [01:00:35] Do you ever sing when you're alone? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/t4HevyAyMbo Resources: https://www.amazon.com/Superminds-Surprising-Computers-Thinking-Together/dp/0316349135 https://www.forbes.com/sites/bernardmarr/2021/10/27/glenfiddich-sells-18000-super-rare-whisky-as-nfts--heres-what-that-means/ Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData

Watch the video of this episode: https://youtu.be/Zm2wrWgKn_g Find Cristina Digiacomo online: https://www.linkedin.com/in/cristinadigiacomo Memorable Quotes from the Episode: [00:36:55-00:36:55] "... I know that's sort of like a pithy answer, but it's the truth. Our thoughts shape our reality. This is a very fundamental idea and concept from many, many, many, many philosophers across the millennia. We shape the circumstances in our lives just by the way that we look at them." [00:23:14-00:23:16] "Philosophy s not just about thinking, it's about acting and acting appropriately. And so all those four things, you know, the perception of the truth and the truth. Managing your thoughts being deliberate and acting accordingly. Wisdom is the word for all of that." Highlights of the Show: [00:01:12] Guest Introduction [00:02:54] Where did you grow up and what it was like there? [00:07:08] How did you get into the DJ world? [00:14:24] How did you get into into philosophy? [00:15:41-00:15:41] Why is it that philosophy and wisdom [they] get lumped into these categories of being like "Woo Woo" out there? Why do you think that is? [00:16:41] How do you define philosophy? [00:19:46-00:19:52] Speaking of being wise, what is what is the difference between being wise and acting wise? [00:24:23-00:24:25] How do we pause? How do we first of all, get to wisdom? How do we mitigate that knee jerk reaction? [00:26:26] Talk to us about clarity as discussed in your book. [00:28:52] Did you encounter any struggles when you're first trying to think in this way? I guess almost like metacognition, thinking about the way you're thinking and forcing yourself to answer these questions? Was that a bit of a challenge for you? And how did you overcome that? [00:34:12] What are your thoughts on constantly being in thought? [00:36:15] How can we help ourselves find out when we're having those detrimental thoughts and natural way back into something more productive, right? [00:38:57-00:38:58] In your book you're talking about how people get really attached to their thoughts and their ideas. How can we avoid that? [00:39:19] How do thought patterns affect our activities and what are some detriments of that? [00:46:03] What is the real flow and how can we distinguish that from a fake flow? [00:48:03] We talked about the importance of of inaction being just as important as as action. But if you were to just spelll it out clearly for us here, why is it that this inaction is just as important as as the action? [00:49:48] What has philosophy taught you about being a better strategist? [00:56:02] Is wisdom a trait that can be cultivated? [00:56:27] Where can we cultivate this act of being wise everywhere that we are? Do we do it alone by ourselves as we interact with other people? How can we can we do that? [00:57:20] What do you want to be remembered for? [00:58:05] What do you think the first video to hit one billion views on YouTube will be about? And when will that happen? [00:58:43] What do you think most people think within the first few seconds of meeting you? [00:59:46] What are you currently reading? [01:01:28] What languages do you speak? [01:01:38] What's the story behind one of your scars? [01:02:09] What's your favourite candy? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: YouTube: https://www.youtube.com/c/TheArtistsofDataScience Instagram: https://www.instagram.com/theartistsofdatascience/ Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/ArtistsOfData