The Artists of Data Science

We're continuing down this path of understanding the interplay between product management and data science, it's an important topic and something we should all be knowledgeable about. On this episode we speak to a data scientist who wears many hats: Tim Enalls. He’s an MBA, CAP, PMP, YouTuber, blogger, and thought leader - and though he has many identities, he has one passion: Data Science. [00:01:33] Guest introductiuon [00:02:38] We learn about where Tim is from [00:03:50] What Tim thought his future would look like when he was younger [00:05:22] What was the journey like coming from high school to where you are now? [00:06:56] What would you say would be a experience that contributed to shaping who you are today? [00:07:50] Some frameworks for innovations [00:12:27] The innovation framework Tim has used most in his career [00:13:21] The importance of self-teaching [00:14:08] Business strategy frameworks that every data scientist should know [00:17:49] What can a data scientist do to build and develop their product sense or their business acumen? [00:19:47] What do you think are some reasons that Data science projects fail? And how can we as data scientist prevent that from happening? [00:23:19] Best practices for helping your company grow its analytic maturity [00:24:51] Strategies for problem solving [00:28:29] The difference between a product manager and a data science manager [00:30:24] What can Data scientists learn from product managers, product managers? [00:31:14] In what ways has your experience taking the PMP exams made you a better data scientist? [00:31:45] How can data scientists be more out of the box thinkers? [00:33:14] We geek out over our mutual appreciation of Seth Godin [00:34:31] Do you subscribe to things like business newsletters or anything like that? [00:36:09] The importance of emotional intelligence [00:38:47] It’s 100 years in the future, what do you want to be remembered for? [00:40:53] The creative practice [00:43:24] The random roundSpecial Guest: Tim Enalls.

Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Register for future sessions here: http://bit.ly/comet-ml-oh Checkout Comet ML by visiting: https://www.comet.ml/ Or on Twitter: https://twitter.com/CometML Connect with Ayodele LinkedIn: https://www.linkedin.com/in/ayodeleodubela/ Twitter: https://twitter.com/DataSciBae Check out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom [00:01:05] When we talk about data validation, what is it that we mean? [00:03:19] A data sheet? What is that? [00:05:30] What’s a good approach to asking questions for a data science project? [00:10:41] Sampling is important [00:17:54] Where does data validation fall in the data science lifecycle? [00:18:31] Where in the pipeline do I perform cross-validation? [00:21:00] How do algorithms know which content to push to you and how can I affect the content being pushed my way? [00:26:26] There is a lack of transparency when it comes to these algorithms [00:30:50] Some more excellent discussion around ethics in machine learning [00:32:52] Ayodele drops some sage insight on how machine learning algorithms are used and the ethics of it all [00:36:20] How to go from data to decisions [00:43:10] What exactly is an insight? [00:47:09] What comes first: the question or the data? [00:53:22] How do you create a narrative around your analysis? [01:02:09] How do you talk about the narrative in your project? [01:05:14] Eliminating data feeds that are wasting money, why are you collecting data that you don’t use? [01:11:23] What’s your favorite data science book?

The Data Science Happy Hours keep getting happier! Check it out and don't forget to register for future office hours: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh If you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70 Watch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm [00:03:19] The rise of new roles in data science [00:04:10] What is it going to take, going forward, to start making money with machine learning and help companies on that road to maturity? [00:06:54] What is an ML architect? [00:09:13] Should a research oriented data scientist learn about architecture? [00:12:41] Do you have to be a great software engineer to think like one? [00:18:48] What is a feature store? [00:20:57] The more I get into this data science/machine learning space…it's like the more I realized I don't know shit. [00:23:22] Mikiko comes in with some awesome insight about feature sores [00:28:48] When do I use a partition for a database? [00:36:46] What are some other types of correlation? [00:42:04] Thom with some wisdom. [00:44:23] A question on web scraping (not people information, but product prices) [00:55:17] The legality of web scraping [00:58:15] How to understand how to help someone in the most effective way [01:11:26] Figure out what the “ground truth” really is [01:14:09] Why you need an emphasis on customer focus and how you can cultivate that mindset Some useful links from our discussion 00:23:14 Greg Coquillo https://www.linkedin.com/posts/greg-coquillo_datascience-machinelearning-artificialintelligence-activity-6760977800963985408-ys5y 00:28:41 Joe Reis https://www.youtube.com/watch?v=o4q_ljRkXqw 00:36:24 Mikiko Bazeley https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/ch06.html 00:37:55 Mikiko Bazeley https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html) 00:42:50 Mark Freeman https://drive.google.com/file/d/1qkURyDrEa4IkQRm0in26CNIpP84ws0Tf/view 00:43:51 Harpreet Sahota https://realpython.com/numpy-scipy-pandas-correlation-python/ 00:44:02 Mitul Patel https://easystats.github.io/correlation/articles/types.html in R 00:47:07 Mark Freeman https://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598 00:54:25 Mikiko Bazeley https://realpython.com/courses/python-lambda-functions/ 00:54:35 Mikiko Bazeley https://learn.datacamp.com/courses/streaming-data-with-aws-kinesis-and-lambda 00:55:43 Mark Freeman https://docs.aws.amazon.com/toolkit-for-eclipse/v1/user-guide/lambda-tutorial.html 00:56:54 Mark Freeman https://docs.aws.amazon.com/lambda/latest/dg/welcome.html 00:58:14 Mikiko Bazeley https://www.forbes.com/sites/emmawoollacott/2019/09/10/linkedin-data-scraping-ruled-legal/?sh=a0f2baa1b54b 00:58:20 Joe Reis https://www.eff.org/deeplinks/2019/09/victory-ruling-hiq-v-linkedin-protects-scraping-public-data 01:06:32 Mark Freeman https://www.datascience-pm.com/crisp-dm-2/ 01:19:05 Mikiko Bazeley https://www.linkedin.com/posts/crmercado_datascience-deeplearning-artificialintelligence-activity-6767800296333811712-Jf8r 01:24:20 Mark Freeman https://voltagecontrol.com/blog/5-steps-of-the-design-thinking-process-a-step-by-step-guide/ 01:24:45 Mark Freeman https://steveblank.com/category/lean-launchpad/ 01:27:27 Vikram Krishna Kotturu https://join.slack.com/t/artofdatascienceloft/shared_invite/zt-dgzn8abm-ge_dKGxrc9Dsuhnly90WTw 01:27:45 Harpreet Sahota https://join.slack.com/t/artofdatascienceloft/shared_invite/zt-dgzn8abm-ge_dKGxrc9Dsuhnly90WTwSpecial Guests: Brandon Quach, PhD, Greg Coquillo, Mikiko Bazeley, and Vin Vashishta.

Greg is an Amazon Private Brands Program Manager and content creator. He was recently named one of LinkedIn's Top Voices in Data and Analytics for 2020 FIND GREG ONLINE LinkedIn: https://www.linkedin.com/in/greg-coquillo/ QUOTES [00:10:06] "Let your curiosity be your driver." [00:13:48] "The product manager is there to take a look at the product vision...a manager is there to guide through the vision." [00:20:52] "Communication skill helps me translate that technical solution into a solution that your stakeholder relate to. One of the best ways to learn from stakeholders is to invite them into the technical solution building session." [00:28:47] "When you don't invite the business stakeholders into your model building sessions, you will miss out on capturing the level of risk that those business stakeholders are willing to take." HIGHLIGHTS FROM THE SHOW [00:02:04] Guest introduction [00:02:50] An experience that shaped Greg [00:04:25] What Greg thought he was going to be when he grew up [00:06:51] The path that led Greg to where he is today [00:09:59] How Greg taught himself data science skills [00:12:43] What role does the product manager play on a Data science team? [00:15:21] What part of the Data science lifecycle does the product manager own? [00:16:47] How is a product manager different from a manager of a Data science team? [00:18:44] What can the data scientist learn from the product manager? [00:21:40] What can the Data scientists do to help make their product manager more effective? [00:22:44] How can a data scientist learn product management skills? [00:24:40] The ten dysfunctions of product management [00:26:53] How do we measure what really matters and how do we determine what matters? [00:30:40] The difference between AI and BI [00:32:42] What qualities make for a good BI leader? [00:33:16] What qualities make for a good AI leader? [00:37:40] What do you think will be the biggest positive impact that AI will have in the next two to five years on society? [00:39:30] The scariest application of AI? [00:40:46] An AI code of ethics [00:43:11] Auditing algorithms [00:45:19] Compliance as a service [00:47:06] What data scientists need to know about compliance and how they can learn about it [00:48:29] Should you be afraid of job descriptions? [00:52:22] First order and second order thinking [00:56:09] The importance of communication skills [01:00:30] It’s 100 years in the future, what do you want to be remembered for? [01:03:01] The random roundSpecial Guest: Greg Coquillo.

Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Register for future sessions here: http://bit.ly/comet-ml-oh Checkout Comet ML by visiting: https://www.comet.ml/ Or on Twitter: https://twitter.com/CometML Connect with Ayodele LinkedIn: https://www.linkedin.com/in/ayodeleodubela/ Twitter: https://twitter.com/DataSciBae Check out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom [00:00:09] Friendly banter between the hosts [00:02:17] Did you learn anything new this week? [00:02:56] What is MLOps? [00:09:33] How MLOps is used in the finance industry [00:11:04] Topics to brush up on if you’re looking to get into the finance space as a data scientist [00:12:06] Resources for learning about GLMs [00:15:00] The struggle of being a data scientist [00:17:19] What to do when you feel like there is so much to learn and not enough time [00:18:56] A few key things you should focus on when you’re breaking into the field [00:22:01] The hardest part about SQL [00:25:59] What skills do I try to showcase in my portfolio project? [00:30:42] How am I supposed to gain business acumen when I don’t have a job? [00:37:30] How do I get my profile noticed? [00:38:52] Understand how to develop KPIs and how your model impacts them [00:44:05] How would you split your time amongst different activities when doing a project? [00:51:17] There are multiple algorithms to use, how do I choose? [00:53:33] How to deal with these crazy job descriptions? [01:01:09] How do I position myself as a valuable candidate for a job? [01:07:19] Get people to do mock interviews with you [01:07:48] Convincing business stakeholders of your results when they want to follow their gut [01:15:17] Resources for picking up some nonobvious skills you need as a data scientist [01:16:13] Template methodology for problem framing: https://www.comet.ml/reports-templates/project-scope-template/reports/template/project-scope [01:18:14] Why you need to get as much hands on practice as you can

The Data Science Happy Hours keep getting happier! Check it out and don't forget to register for future office hours: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh If you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70 Watch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm

Siann is a lead data scientist and analytics manager at Booz Allen Hamilton where she helps her stakeholders cut through the clutter to make better decisions, and leads a team that transforms complex problems into simple solutions. For her contributions to data science and social good, she’s been awarded the 2020 Women of Color in STEM All Star award, the 2019 DCFemTech award, and the 2017 Prince George’s County, MD 40 Under 40 honoree. FIND SIAN ONLINE LinkedIn: https://www.linkedin.com/in/allsian/ Website: https://www.sianlewis.org/ Corporate Profile: https://www.boozallen.com/e/insight/people-profiles/sian-lewis.html QUOTES [00:04:17] "When you're an immigrant, you find any form of these social enclaves. Wherever you are with people who are similar to you, who are from similar countries as you, you form tight knit communities." [00:08:29] "You know, I went to grad school. I was terrible at it. I didn't want to be there. And I actually learned that I had no interest in actually anything health care related. So I quit after great anguish, great terror. And I was like, oh, my God, what am I going to do with my life?" [00:14:08] "I love that people think that we are magical wizards that control the world. And then I get to burst people's bubble..." [00:14:31] "I also love Data science because I blink and something new has come out that has fundamentally changed the way I did things. Literally every single day there's something new, there's a new package, there's a new technique, there's a new finding. There's a new paper that comes out. And I get to rethink what I learned in school. I get to rethink what I've done practically over the years. And I love that." [00:18:42] "You're hired to solve a very specific problem. And the problems are usually in three categories: How are you going to increase usage of something? How are you going to increase revenue? Or how are you going to increase engagement on something?" HIGHLIGHTS FROM THE SHOW [00:03:14] Where Siann grew up and what it was like there [00:05:30] The immigrant experience [00:06:18] What Siann was like in high school [00:08:13] The journey into data science [00:10:50] How data science is used in political science [00:14:03] What do you love most about being a data scientist? [00:15:21] Do you consider Data science machine learning to be an art or purely a hard science? [00:17:58] What role do you think being creative and curious plays in being successful as a Data scientist? [00:20:57] What is a model and why is it that we even build them in the first place? [00:22:39] How can we use algorithms to build models with equality and equality? [00:28:25] How to make sure you’re building a fair model [00:29:20] Some tips for feature engineering [00:35:53] Project idea for survey data [00:36:19] The importance of MLOps [00:37:51] Communicating model results with business stakeholders [00:40:17] The non-obvious skills you need for success [00:46:22] Communicating with executives [00:48:39] Don’t be afraid to apply for a job just because the description looks crazy [00:52:10] Advice for women in STEM [00:55:23] How to foster diversity in data science [00:58:59] It’s 100 years in the future, what do you want to be remembered for? [00:59:46] The random roundSpecial Guest: Sian Lewis.

Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Register for future sessions here: http://bit.ly/comet-ml-oh Checkout Comet ML by visiting: https://www.comet.ml/ Or on Twitter: https://twitter.com/CometML Connect with Ayodele LinkedIn: https://www.linkedin.com/in/ayodeleodubela/ Twitter: https://twitter.com/DataSciBae Check out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom [00:00:24] Getting to know my co-host, Ayodele [00:04:01] Ayodele talks about her new course on LinkedIn Learning [00:04:57] What is a data evangelist? [00:06:04] Why Comet ML decided to pair up with The Artists of Data Science [00:07:20] Where Comet ML fits into the machine learning lifecycle [00:12:58] What are the do’s and don'ts for anyone who wants to get into machine learning? [00:21:23] How to find labels for data in an image recognition project? [00:25:55] MLOps Engineer vs Machine Learning Engineer? [00:30:04] How do we convince senior leaders that we need an ML solution? [00:40:50] The trials and tribulations of being the only data scientist in an organization: dealing with what to learn and imposter syndrome. [00:53:37] How to learn more about a company [00:57:39] Struggles with linear algebra [01:02:56] Building data pipelines [01:04:25] Paying for internships

The Data Science Happy Hours keep getting happier! Check it out and don't forget to register for future office hours: http://bit.ly/adsoh If you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70 Watch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFw_ty92wJoWzoO7WlfaM7iYB8_qjm We were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/data_science_podcasts/

Kurtis Pykes comes by the show to talk about how he got into data science. He's got an unique back story which includes being a cult leader. We touch on a wide range of topics from how to get experience in data science without a data science job to how writing blogs can help you become a better data scientist. It's an awesome episode that you won't want to miss! FIND KURTIS ONLINE Medium: https://kurtispykes.medium.com/ LinkedIn: https://www.linkedin.com/in/kurtispykes/Special Guest: Kurtis Pykes.