The Artists of Data Science

On this episode of The Artists of Data Science, we get a chance to hear from Paul McLachlan, a data scientist who has over a decade of experience applying his knowledge and expertise to academia, corporate businesses, and entrepreneurial endeavours. His contributions and expertise have led to numerous startups and nonprofits inviting him to serve as an advisor. He gives insight into how what sparked his interest into the data science field, his tips for beginners in data science, and how he stays motivated. Paul shares with us his powerful journey from being a high school dropout to getting his PhD in computational social science and becoming the A.I. research leader for the Consumer and Industry Lab at Ericsson Research. This episode is packed with advice, wisdom, and tips that will change your mindset. WHAT YOU WILL LEARN [21:37] How A.I. can help fight COVID-19 [27:15] Extended reality vs. virtual reality [32:11] Tips for breaking into data science [35:29] Important soft skills for data scientist [44:22] Staying motivated in difficult times QUOTES [19:05] "Data science is really a collective endeavour… even the most skilled and successful data scientist is going to have to be able to successfully work with technical stakeholders, non-technical stakeholders…" [34:51] "…Start from a position of humility…that that can go much further for data scientists than always trying to be the smartest technical person in a conversation…" [45:29] "Having fun and staying connected and staying entertained is actually part of your job responsibilities rather than something that can be set aside." SHOW NOTES [00:01:40] Introduction for our guest today [00:03:38] What sparked your interest in the field of Data Science? Where did you start and how did you get to where you are today? [00:05:50] How to not be afraid of math and overcome imposter syndrome [00:07:42] Where do you see the field of Data science machine learning and A.I. headed in the next two to five years? [00:09:38] What do you think will be the biggest area of concern for the application of A.I. in the next, say, two to five years? [00:11:22] What do you think will separate the great Data scientists from the good ones? [00:12:57] Ericcson's involvement with the White House Office of Science and Technology COVID-19 open research dataset challenge using information retrieval and NLP [00:13:24] What is information retrieval? [00:14:02] What is Natural Language Processing? 00:14:40] How information retrieval and Natural Language Processing played a role in the innovative solutions that Ericsson data scientists developed for the challenge. [00:19:31] What the resulting product looked like [00:20:52] Interesting findings that came from the challenge [00:24:30] Congratulations on your new role. AI Research Leader for the consumer and industry lab. So can you tell us a little bit about how the consumer and industry lab fits into Ericsson? [00:26:56] What XR and VR are and share with us what aspects of XR and VR are most interesting to you. [00:31:45] How to build a culture of data science [00:35:13] What do you look for in a data scientists beside those those technical skills? [00:37:47] How to gain industry experience if you don't have any [00:39:52] How to communicate with executives as a data scientist [00:42:10] Thought leadership in data science [00:44:06] Tips to stay motivated when you're feeling down in your learning journey [00:47:03] What's the one thing you want people to learn from your story? [00:48:40] The lightning round Special Guest: Paul McLachlan, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Santona Tuli, a physicist and data scientist who has a PhD in physics specializing in nuclear science and quantum chromodynamics. She currently leads a team of five doctoral and postdoctoral physicists studying a new plasma phase of matter and the elusive nuclear effects in high energy proton and nucleus collisions at the Large Hadron Collider at CERN in Geneva, Switzerland. Santona shares with us her journey into data science as a physicist, and her perspective on the future of the field. She also discusses the differences between data science and decision science, tips to break into the field, and advice for women in STEM. It was an absolute delight hearing Santona’s advice, and I believe her unique perspectives can help all data scientists! WHAT YOU'LL LEARN [4:46] Where the field of data science is headed [32:42] Is data science an art or science? [49:07] Tips for breaking into data science [57:55] How to get over the perfectionist mindset and feeling like a failure [1:02:07] Diversity and inclusion of minorities in STEM QUOTES [34:51] “...just being able to step outside and think of alternative approaches, stepping outside the predefined paths. To me, that's how the creative part of my brain is really engaged when I'm doing Data science.” [39:38] “...the audience should be able to look up at this screen and see themselves reflected in it, being able to understand that the physics that's going on...physics is very much within their reach. Science is very much within their reach.” [52:40] “...separate or distinguish what the end goal is and the steps that you need to take in order to get there” [55:21] “get over this idea that it has to be perfect before [you] push it out...What's the worst that can happen? Maybe someone criticizes in some way...But it might turn out that this criticism that you're receiving on it is actually going to help you iterate on that project and make it better.” WHERE TO FIND SANTONA LinkedIn: https://www.linkedin.com/in/santona-tuli/ SHOW NOTES [00:01:21] Introduction for our guest today [00:02:40] The path into data science [00:03:20] What the heck is quantum chromodynamics? [00:03:54] Data science and the study of nuclear forces [00:04:49] The future of data science [00:08:17] Data science and empathy [00:09:27] How to be a great data scientist [00:10:48] What is CERN? [00:13:13] What is this Y particle? [00:15:15] The data science work flow and particle physics [00:20:25] Data reduction and data bottlenecks [00:23:43] Selection cuts and rules based clustering [00:29:43] The importance of feature engineering [00:32:31] How do you view data science? Do you view it as an art or a science? [00:34:17] How does the creative process come to life in Data science? [00:36:39] Santona talks about the IMAX movie that she stars in [00:40:43] The difference between interpretable and explainable machine learning. [00:44:22] Decision science and data science [00:48:49] Words of encouragement for people learning new things [00:51:04] What does it mean to think like a product manager? [00:54:14] Break free of the perfectionist mindset [00:57:00] How to deal with feedback and criticism [00:58:31] What are some soft skills that you think Data scientists are missing? [01:01:29] Advice and words of encouragement for the women in our audience who are breaking into tech or currently in tech. [01:05:48] Santona talks about the impact she hopes to have on young women in STEM [01:09:08] What can men do, in particular in the Data community, to help foster the inclusion of women in STEM, in tech and Data? [01:11:28] What's the one thing you want people to learn from your story, [01:11:57] What's your data science superpower. [01:12:02] What would you say is the most fundamental truth of physics that all human beings should understand? [01:12:19] What do you think is the most mysterious aspect of our universe? [01:12:43] What is an academic topic outside of Data science that you think every data scientist should spend some time researching or studying on. [01:12:53] What's the number one book? Fiction, nonfiction? Or if you want to pick one of each that you would recommend our audience read. And what was your most impactful takeaway from it? [01:14:02] If we can somehow get a magical telephone that allowed you to contact 18 year old Santona, what would you tell her? [01:15:09] What song do you have on repeat. [01:15:28] How do people connect with you? Where can they find you?Special Guest: Santona Tuli, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Josh Starmer a data scientist who has helped empower learners from all over the globe by breaking down complicated statistics and machine learning topics into small bite sized pieces that are easy to understand. You may know Joshua from his youtube channel StatQuest, where he's beloved by his audience of over 320,000 subscribers and 15 million viewers. Joshua shares with us his powerful journey from being a cellist and music composer to getting his PhD in computational biology and then creating StatQuest. This episode is packed with advice, wisdom, and tips for developing a creative process and facing your fears. It was a great honor interviewing Joshua! WHAT YOU'LL LEARN [9:05] How music has helped Joshua become more creative [17:19] Inspiration for StatQuest [24:00] The most challenging part of creating content [28:02] The most misunderstood concept from statistics and machine learning [36:38] How Joshua approaches his creative endeavours QUOTES [9:38] "I pick up my guitar, my ukulele, and I start playing, and my head just completely clears." [19:52] "what I really want people to take home is that anyone can understand these things [statistics]. Ninety nine times out of 100, the only thing between them and understanding is fancy terminology and fancy notation" [23:31] "It's probably a good thing that I'm a little nervous…because it pushes me just a little harder to make sure that what I'm talking about is correct" [33:16] "…if you want to educate someone…you have to relate with them and you have to see the material from their perspective." FIND JOSHUA ONLINE LinkedIn: https://www.linkedin.com/in/joshua-starmer-95a554130/ YouTube: https://www.youtube.com/user/joshstarmer Website: https://statquest.org/ SHOW NOTES [00:01:40] Introduction for our guest [00:03:13] How Joshua got into statistics [00:04:12] Where do you see the field of Data science headed in the next two to five years? [00:05:12] What do you think is gonna separate the great Data scientists from the really good ones? [00:06:22] Talk to us a bit about what music theory is, what a music theorist does. [00:08:59] Do you think having a deep understanding of math has helped you be more creative as a musician or vice versa? [00:11:22] What are some of the commercials and shows that feature your music? [00:15:32] Joshua describes his process for creating music [00:17:12] The inspiration of StatQuest [00:19:27] The StatQuest mission [00:20:40] Overcoming the resistance when it comes to creating and publishing content [00:23:53] What's the most challenging part for you when it comes to creating content for the channel? [00:25:15] What's your personal favorite video from the archives? [00:26:16] The absolute must watch video from StatQuest [00:27:53] The most misunderstood statistical concept [00:30:23] Why you don't need to memorize forumals [00:32:37] Can you recommend a good book for learning statistics? [00:34:27] The art and science of data science [00:36:25] Creativity and data science [00:38:05] What would you say are the similarities and differences in the creative process for, let's say, writing a research publication, composing music or creating youtube video? [00:39:38] What's the one thing you want people to learn from your story? [00:40:47] The lightning round. Special Guest: Joshua Starmer, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Chase Cunningham, a retired Navy chief cryptologist with nearly two decades of experience in cyber, forensic and analytic operations. He holds both PHD and Masters in Computer Science, and has been named one of Security magazine's most influential people in security for 2019. Chase shares with us the definition of cybersecurity and cyberwarfare, how cyberspace has evolved over the past decade, and the dangers of operating within this space. Chase’s knowledge within cybersecurity will help data scientists identify ways for them to build models that have better real world outcomes and give them insights into a field that impacts our work. WHAT YOU'LL LEARN [4:15] What is cyberwarfare and cybersecurity? [5:33] How does cybersecurity impact data science? [8:19] The truth about hackers [16:22] Autonomous vehicles and cybersecurity concerns [26:20] Ways for data scientists to prevent biases within their models QUOTES FIND CHASE ONLINE LinkedIn: https://www.linkedin.com/in/dr-chase-cunningham-54b26243/ Twitter: https://twitter.com/CynjaChaseC SHOW NOTES [00:01:30] Introduction for our guest today [00:02:37] Talk to us a bit about your professional journey, how you first heard of cyber security, cyber warfare, and kind of what drew you into that field. [00:04:06] Can you define what cyber warfare and cyber security are? [00:05:19] Cyber security and data science [00:06:01] Cybersecurity, data science, and machine learning [00:06:52] What are some of the biggest concerns in cyber warfare that we'll face both kind of at individual user level and at the organizational level over the next two to five years? [00:07:56] Hollywood hackers aren't real like hackers [00:09:05] How hacking has evolved overtime [00:10:02] How to practice for cyberwarefare [00:11:03] How can machine learning help detect or prevent these hacking incidents from occurring? [00:11:29] Cybersecurity projects [00:13:01] The Cyber Shot Heard around the world. [00:14:04] What you mean by kinetic outcomes? [00:14:33] Modern cybersecurity and kinetic outcomes [00:15:02] Perimeter based security mode [00:15:42] Alternative to a perimeter based security [00:16:09] What does cyber security have to do with autonomous vehicles? [00:16:50] Cyber security attacks on autonomous vehicles [00:18:14] How cyber security, social media, and A.I can be used for bad [00:19:15] How to not be tricked by deep fakes [00:20:38] Weaponizing biometrics [00:21:26] Cyber warfare campaigns [00:22:26] Societal impacts of deep fakes, machine learning, A.I. and cloud computing? [00:24:18] What the history of warfare can teach us about cyberwarfare [00:25:04] What happens, when Data and A.I. studies go awry? [00:26:05] How to prevent bias in machine learning systems [00:27:01] What do you think would be the equivalent of the nuclear bomb for cyber warfare, cyber security? [00:27:38] You've got six patents that are credited to you. Which one is your favorite one? [00:29:05] Why should we kill the password? [00:29:38] What would be the alternative to passwords? [00:30:07] What's the one thing you want people to learn from your story? [00:30:39] The lightning roundSpecial Guest: Chase Cunningham, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Marco Andreoni, a statistician and data scientist who has a master's degree in mathematics and machine learning, as well as a master's degree in mathematics and cryptography. He is the lead data scientist at Quantyca, where he covers every part of the data lifecycle from ingestion, storage, analytics, web applications, cloud storage and beyond. Marco shares with us his passion for teaching other statistics in a more meaningful way. This led him to create Flash statistics, a way to make statistics more accessible to people. Marco brings an interesting perspective into sharing knowledge in a creative way, that all of our listeners should develop to be competitive! WHAT YOU'LL LEARN [5:59] Relationship between cryptography and data science [23:57] What happens when you deploy a model to production [27:11] The importance of version controlling models [28:47] The importance of version controlling data [30:33] Evaluation metrics for post production [32:00] The importance of creativity [36:00] Tips on communicating effectively QUOTES [21:03] "You don't need to memorize every single equation…But you must know the underlying idea." [31:23] "Only if you measure something, you can control something" [35:00] "Focus on the process, the result takes care of itself" FIND MARCO ONLINE LinkedIn: https://www.linkedin.com/in/marcoandreoni1/ Website: https://www.flashstatistics.com/ SHOW NOTES [00:01:24] Introduction for our guest [00:02:43] Talk to us about your journey, how you first got interested in statistics, machine learning, data science? What drew you to the field? [00:04:10] Can you give us an overview of what cryptography is? [00:05:52] How do you see machine learning and cryptography kind of inter playing in the near future? [00:07:52] GDPR and data science [00:08:53] Talk to us about the genesis of flash statistics? What was your inspiration for creating it? [00:09:35] The mission of flash statistics [00:10:23] Did you feel any type of internal hesitation or a fear with creating the content? And if you did, how did you overcome it? [00:12:19] The challenge of creating content [00:13:21] Do you have a personal favorite graphic from the archives? [00:13:57] Correlation and causation explained via the story of the Stork. [00:16:20] The one flash statistics painting you need to check out [00:17:21] What would you say is the most misunderstood concept from statistics and machine learning? [00:17:51] Would you mind clarifying or demystifying that concept for us? [00:20:35] Do you think it's important to learn all the formula and equations even though we have advanced software that doesn't work? [00:21:15] Do you have any tips or any good ways for somebody to learn the underlying idea behind what the software is doing? [00:22:27] Do you consider Data science and machine learning to be an art or purely hard science? And why? [00:23:57] What happens when you deploy a model to production [00:27:11] The importance of version controlling models [00:28:47] The importance of version controlling data [00:30:33] Evaluation metrics for post production [00:31:46] How to be creative [00:35:57] How to effectively communicate [00:38:22] The creative process in data science and the artistic process [00:39:24] What's the one thing you want people to learn from your story? [00:40:12] The lightning roundSpecial Guest: Marco Andreoni.

On this episode of The Artists of Data Science, we get a chance to hear from Irena Cronin, the co-author of "The Infinite Retina". She currently serves as CEO of Infinite Retina, an organization which provides research and business strategy to help companies succeed in spatial computing. She gives insight into what sparked her interest into spatial computing, how she sees spatial computing influencing our world, and the potential data problems that will result from more spatial computing technology. Irena shares with us what led her from leaving her career as an equity research analyst on Wall Street to working with AR/VR and other spatial computing tech. This episode is packed with interesting insights in our future, and I believe anyone listening will have something to ponder on! WHAT YOU WILL LEARN [4:09] Spatial computing is all the technology associated with bringing a 3D realm to it's users. [8:15] Concerns of spatial computing [17:20]The four technical paradigm shifts [28:56] Spatial computing and autonomous vehicles shaping our future QUOTES [16:59] "Technology…it's always been a tool for us. But even more so with spatial computing." [43:12] "I'd say the most important thing you can ever do is to be extremely persistent, no matter what" [44:42] "I think it's extremely important to have professors and the students in a class, …take time to listen to everyone who wants to speak… and not let anyone monopolize that precious time." FIND IRENA ONLINE Instagram: https://www.instagram.com/infiniteretina/ Twitter: https://twitter.com/IrenaCronin LinkedIn: https://www.linkedin.com/in/irenacronin/ SHOW NOTES [00:01:33] Introduction for our guest today [00:02:52] How did you get to where you are today? [00:04:02] What is spatial computing, and how is it different from regular computing? [00:04:53] In what ways is spatial computing already a part of our daily lives? [00:06:47] Where is spatial computing technology headed in the next two to five years? [00:08:06] What do you think are some of the biggest concerns that society will face due to spatial computing technology in the next two to five years? [00:10:51] What is the prime directive? [00:13:04] How does spatial computing play into meeting that prime directive? [00:14:35] How will spatial computing change what it means to be human? [00:17:07] What is the fourth paradigm? [00:20:08] What's the intersection between spatial computing and artificial intelligence look like? [00:21:29] Voice first technology, spatial computing, and the prime directive. [00:24:42] Can AI create a government for itself? [00:28:36] How will spatial computing and autonomous vehicles help shape cities of the future? [00:31:02] Can you talk to us a bit about what Data bubbles are and what they have to do with the cities of the future. [00:33:24] Concerns that local municipalities are having with the use of this spatial computing technology. [00:39:39] How spatial computing will change the way we attend live events in a COVID world [00:42:55] Advice for women who are in STEM fields [00:44:07] How can we foster the inclusion of women in Data science, in AI, and in STEM? [00:46:08] What's the one thing you want people to learn from your story? [00:46:38] The lightning roundSpecial Guest: Irena Cronin.

On this episode of The Artists of Data Science, we get a chance to hear from Carlos Mercado, a data scientist, economist and urban studies enthusiast. Throughout his career, he's had a diverse range of experience, including time as a freight broker, a year long stint teaching English in Korea and working as a data science freelancer. He's currently a senior data scientist at a global consulting firm. Carlos shares with us his journey into data science, the importance of building your brand, and tips for those who want to break into the field. Carlos is an example of someone who has worked hard to learn the fundamentals, and his story shows that it is possible to break into data science! WHAT YOU'LL LEARN [5:16] Where is the field heading? [10:23] Carlos’s background in economics, and how it relates to data science [23:52] Lessons regarding how to get the job you want [30:39] How to use reframing and paradoxes for your mindset [45:24] Advice on building a resume for data science [51:40] Building your personal brand QUOTES [23:12] “...without the history, you’re not going to have context.” [25:51] “...your resume is a sales document. So if you don't include it in your sale, they're not going to know to buy.” [29:33} “...the most important part of data science, besides knowing math, is being able to communicate to business people and making sure that they understand...” FIND CARLOS ONLINE LinkedIn: https://www.linkedin.com/in/crmercado/ SHOW NOTES [00:01:36] Introduction of our guest [00:02:52] Let's talk about how you first heard of Data science and what drew you to the field. [00:05:12] Where do you see the field headed in the next two to five years? [00:06:42] How to be a great data scientist [00:08:31] Natural language process and voice data [00:10:15] What is economics and why data scientists should care [00:11:12] Economics and big data [00:14:11] Bitcoin and Data Science [00:17:24] What you need to know about GIS, Urban Economics, and Data Science [00:22:26] Do you have any other resources or articles that are kind of covering that topic that our readers can go check out if they want to learn more? [00:23:24] Lessons learned in the data science job search process [00:26:58] What you've learned about Data science working for a psychiatrist at a nonprofit school. [00:30:20] Reframe and Paradox [00:34:36] What it's like working as a consulting data scientist [00:39:09] How does this differ from working in a regular organization? [00:40:34] Phoenix project and Unicorn Project [00:41:04] Freelancing as a data scientist [00:45:15] How to make a good data science resume [00:49:57] How to make a good data science project [00:51:33] How to build your data science brand [00:53:05] The qualities that Carlos looks for in a data scientist [00:54:06] What's the one thing you want people to learn from your story? [00:54:49] The lightning roundSpecial Guest: Carlos Mercado.

On this episode of The Artists of Data Science, we get a chance to hear from Djimila Amimer, an experienced business leader and entrepreneur with a broad range of experience across multiple domains. She is the CEO and founder of Mindsenses Global, a management consultancy specializing in artificial intelligence with a mission to help businesses and organizations apply A.I. and unlock its full potential. Djimila shares with us her journey into the field of A.I, and some of the concerns she sees the field facing within the next few years, along with the applications of A.I. expanding to other businesses and organizations. She also highlights important soft skills everyone should develop, and advice for women in tech. This episode is packed with tips from an expert in A.I.! WHAT YOU'LL LEARN [8:32] Biggest concerns for data scientists within the next few years [16:56] Ethical concerns that data scientists should understand with general A.I [21:24] How A.I. can help in the fight against COVID-19 [27:10] Djimila’s work with Mindsenses Global [32:42] Advice on how to become an entrepreneur QUOTES [33:17] “...your journey is going to be lonely. So you have to have a lot of resilience to be able to sustain yourself and you grow your business…” [34:02] “I believe that if you want to do it, if you really, really want to and you believe in it...you will succeed no matter what…” [35:28] “…you have to be able to adapt to a changing environment.” FIND DJAMILA ONLINE LinkedIn: https://www.linkedin.com/in/dr-djamila-amimer-142662137/ Twitter: https://twitter.com/mind_senses Website: https://mindsenses.co.uk/ SHOW NOTES [00:01:21] Introduction for our guest today [00:03:15] Talk to us a bit about how you got involved with the field of artificial intelligence, what drew you to the field? [00:03:48] Where do you see the field of A.I. headed to the next two to five years? What do you think is going to be the next wave of A.I.? [00:05:09] A historical tour through the three waves of A.I. [00:07:07] What do you think separates the great Data scientists from the good ones? [00:08:26] What do you think are going to be some of the biggest concerns that a Data scientist will face in the next two to five years? [00:10:13] Narrow AI, General AI, and the future of AI [00:16:43] The ethical concerns Data scientists will face as AI evolves [00:21:19] How can AI be used to help us fight this Covid-19 pandemic? [00:24:57] Do you think that we could use AI and machine learning to identify or at least predict the next pandemic? [00:25:30] Which one of your research works do you think is most relevant to our current times and can you maybe make the connection for us? [00:27:02] A deep diver into the work that Dr. Amimer does at Mind Sense Global [00:32:28] Tips for anyone who is thinking of becoming an entrepreneur [00:33:44] How to cultivate an entrepreneurial mindset [00:35:32] Data science entrepreneurship opportunities in the COVID world [00:38:05] The soft skills you need to standout [00:41:13] How can a student with nothing but a laptop and an Internet connection to use AI for good? [00:44:22] Advice for women in STEM [00:46:11] What can the Data community do to foster the inclusion of women in STEM? [00:48:27] What's the one thing you want people to learn from your story? [00:49:12] The lightning roundSpecial Guest: Djamila Amimer, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Lisa Shiller, a mathematician and data scientist who loves dancing, cooking and adventure. She's most passionate about using her skills to make a positive impact, improve people's well-being, create sustainable abundance and decrease our carbon footprint by spreading awareness of sustainability. Lisa shares with us her work at FoodMaestro, the importance of sustainability, interesting findings from her COVID-19 related project, and advice for women in tech. Lisa provides great advice for data scientists on how to impact the culture of their organizations and the importance of being authentic. It was a great pleasure interviewing her! WHAT YOU'LL LEARN [6:01] What is sustainability? [19:52] Lisa’s COVID-19 project in Mexico [28:19] Challenges in cultivating a data science culture in an organization [32:41] Important soft skills every data scientist needs [38:51] Advice for women in tech QUOTES [8:38] “...it's all about taking the data that we have, interpreting it and allowing just like everyday people to have access to information to make smarter, healthier decisions.” [31:22] “I think it's important to... work with other people that are also who they are authentically.” [36:57] “I don't know everything right now, but I will figure it out. And that's totally OK.” FIND LISA ONLINE LinkedIn: https://www.linkedin.com/in/lisa-shiller-a7471551/ Instagram: https://www.instagram.com/lisashiller/ Twitter: https://twitter.com/lisa_shiller Facebook: https://www.facebook.com/lshiller Website: https://www.lisashiller.com/ SHOW NOTES [00:01:44] Introduction for our guest [00:02:58] Lisa’s path into Data science. What sparked her interest? Where did she start? And how did she get to where she is today? [00:04:08] Talk to us about the work you're doing at FoodMaestro. How are you applying data science to help deliver a better food experience? [00:05:48] What sustainability means in terms of the work Lisa does [00:07:15] How will Data science will impact clinical health, wellness, and sustainability even in the next two to five years? [00:08:48] In what ways do you feel we can leverage data science to help reduce our carbon footprint and promote sustainability? [00:09:45] In what ways do you think Data science will have a big impact or at least the biggest positive impact on people's food choices in the next two to five years? [00:12:06] Lisa talks to us about the project she worked on, where she used math and data science to predict COVID-19 in the state of Guanajuato, Mexico. [00:14:09] Lisa explains what the SEIR model from epidemiology is [00:15:37] Lisa talks to us about the importance of having good or strong assumptions when undertaking a project? [00:19:44] Lisa shares what she found to be the most interesting or important finding that she got from this project? [00:21:54] Lisa defines what herd immunity is [00:22:54] How do you view data science? Do you view it as an art or as a science? [00:24:08] How does the creative process manifests itself in mathematics and Data science? [00:25:28] What do you think are the essentials to lay the foundation on which to build a data science team in your organization? [00:28:02] Tips for the first data scientist in the organization. [00:29:45] What is it that you look for in a Data science candidate? [00:32:14] What are some of these soft skills that candidates are missing that are really in a separate from their competition? [00:34:30] How to communicate with non-technical audiences [00:35:32] How to communicate when you don’t know the answer [00:38:33] Words of encouragement for our women in the audience who are breaking in to or currently in tech. [00:40:44] Can you talk to us about how you grappled with imposter syndrome and how you overcame that? [00:43:03] What can the Data community as a whole do to foster inclusion of women in Data science and AI? [00:44:52] What's the one thing you want people to learn from your story? [00:45:39] The lightning roundSpecial Guest: Lisa Shiller.

On this episode of The Artists of Data Science, we get a chance to hear from Brenda Hali, a marketing guru turned data scientist who is passionate about using data to understand causation and to promote company growth. She gives insight into how she broke into the data science field, how marketing and data science are related in some ways, and the struggles she faced when breaking into tech. Brenda shares with us her transition from marketing into data science, along with the importance of having the representation of women and other minorities in the tech industry. This episode really shows why diversity and inclusion in tech is so important, and how we can all play a role to help others break into the field. WHAT YOU'LL LEARN [6:56] What marketers can learn from data scientists [11:07] Steps to take when beginning a new project [17:33] How to communicate effectively with your team in the post-COVID world [20:56] Advice for women and minorities that want to enter into data science QUOTES [15:02] “...you need to have communication with your team, and that communication needs to be in one place” [15:47] “...experiment fast and let things go…” [23:52] “Be careful with who you listen to, and be careful when those voices are close to you.” FIND BRENDA ONLINE LinkedIn: https://www.linkedin.com/in/brenda-hali Instagram: https://www.instagram.com/datanauti/ Twitter: https://twitter.com/brendahali Medium: https://medium.com/@brendahalih SHOW NOTES [00:01:31] Introduction for our guest today [00:02:19] Let's talk a little bit about how you first heard of data science and what drew you to the field. [00:06:16] As someone who is a marketer turned data scientist, what would you say that the data scientist and the marketer can learn from each other? [00:08:46] How do you see data science impacting marketing and what could the data scientists and the marketer do to best serve each other in this vision of the future that you have? [00:10:49] What are some of the first things that you do when taking on a new project? And what are some of the steps you take to kind of keep you on track while going through and navigating the ambiguity of a data science project? [00:12:51] You wrote on a "Starting Guide to Excel at Teamwork." I was wondering if you could talk to us a bit about the importance of teamwork for data scientists. Do you mind sharing the key points from that post with our audience? [00:17:17] How do you think teamwork will change or be affected in this post-Covid world? What can we do to start being better team members when we're actually not going to be for a while at least some people aren't going to be in the same room, in the same office as their colleagues. [00:20:38] Do you have any advice or words of encouragement for the women in our audience who are breaking into tech or who are currently in the tech space. [00:24:11] What can the Data community do to foster the inclusion of women in Data science and A.I? [00:29:37] What's the one thing you want people to learn from your story? [00:31:39] How universities, probably will change their business model. [00:32:27] What is your Data science superpower? [00:33:03] What's an academic topic outside of Data science that you think Data scientists should spend some time researching on? [00:33:13] What is the number one book, fiction, non-fiction or both that you would recommend our audience read. And what was your most impactful takeaway from it? [00:34:09] What's the biggest blunder of bias you've seen or heard of with an algorithm? [00:34:55] If we can somehow get a magic telephone that allowed you to contact 20 year old Brenda, what would you tell her? [00:35:43] What's the best advice you have ever received? [00:36:23] What motivates you? [00:38:08] What song do you have on repeat? [00:38:21] How can people connect with you? Where can they find you?Special Guest: Brenda Hali.