The Artists of Data Science

On this episode of The Artists of Data Science, we get a chance to hear from Khuyen Tran, a student of data science that is currently in pursuit of breaking into the field. She gives insight into how she prioritizes her tasks every day and strategies she uses to take notes and read books. This episode gives our listeners a fresh perspective on how to approach the data science field, and some very interesting soft skills that you can implement to step up your game! Khuyen is definitely someone I believe will bring lots of value into the data science field. WHAT YOU WILL LEARN [3:23] Ways to boost your efficiency and learning rate [9:34] What inspired Khuyen to begin writing her posts on data science [11:42] How to initiate projects in data science [26:43] Reading books the right way QUOTES [4:43] “…maximize important tasks over the urgent but not important tasks…” [11:25] “…the best way to learn anything is not from taking notes, but from… using it.” [24:15] “…learn to love whatever you are doing and you will start to do it really well.” FIND KHUYEN ONLINE LinkedIn: https://www.linkedin.com/in/khuyen-tran-1401/ Medium: https://medium.com/@khuyentran1476 Twitter: https://twitter.com/KhuyenTran16 Website: http://mathdatasimplified.com/ SHOW NOTES [00:01:17] Introduction for our guest [00:02:28] How did you get interested in Data science and machine learning. What kind of drew you to the field? [00:03:02] Khuyen talks to us about her struggle to dedicate time for Data science, and shares some of the struggles and strategies that she's used to enable yourself to boost your learning rate and accomplish more. [00:04:11] Khuyen talks about how she uses the Eisenhower decision matrix to manage her priorities [00:06:11] How to manage the distactions that could derail you while you're studying [00:07:17] How to cultivate the right mindset for studying [00:09:23] Khuyen talks to us about some of the projects she's done and how posting her work on Towards Data Science has helped her [00:10:55] Khuyen shares her tips for taking notes while studying [00:11:32] How to come up with ideas for your projects [00:12:46] How do you find the right Data? How do you organize your thoughts? How do you structure your project? How do you overcome these challenges? [00:13:51] Tips for networking with experts in the field [00:14:41] Some tips on how to identfy and use the right resources [00:16:49] What's your data and analysis discovery process like? [00:18:18] How to answer questions you don't know the answer to during an interview [00:21:51] What's the one thing you want people to learn from your story? [00:22:16] The lightning roundSpecial Guest: Khuyen Tran.

On this episode of The Artists of Data Science, we get a chance to hear from Alistair Croll, a well-established entrepreneur, analyst, and author. He's the author of Lean Analytics, when we co-wrote with Benjamin Yoskovitz. He's also one of the founders of Coradiant, Year One Labs, and the Strata confersence. He shares some excellent tips one how to ask the right questions when working with data, essentials of business communication, and the need to be obsessed as an entrepreneur. WHAT YOU WILL LEARN: [28:28] How to be an intrepreneur [13:39] Incorporate philosophy with data [19:11] Why you need to be obsessed as an entrepreneur QUOTES [14:22] …”as an early stage company, your focus is your biggest currency.” [22:10] …”crises have a way of accelerating the inevitable.” [46:04] “...you got to first seek to engage and entertain and then you have the ability to inform people.” [51:38] …”find a way to capture attention that you can turn into profitable demand better than the competition.” WHERE TO FIND ALISTAIR ONLINE: Twitter:https://twitter.com/acroll LinkedIn:https://www.linkedin.com/in/alistaircroll/ Website: http://solveforinteresting.com/ SHOW NOTES [00:01:37] Introduction for our guest today [00:03:20] Alistair talks about his early work with Coradiant [00:05:47] What do you think the next two to five years is going to look like for businesses leveraging data and analytics? [00:07:55] Why A.I. will need a therapist [00:08:26] In this new vision of the future then what's really going to separate, like the great data scientists from just the merely good ones? [00:11:03] The importance of privacy and GDPR for data scientists [00:13:56] The concept of "one metric that matters" and how that's going to manifest in terms ofmeasuring privacy [00:15:00] Why Zoom DOES NOT deserve to be the videoconferencing platform in the world [00:17:30] Do you have any advice or tips for anyone who's been toying with the idea of entrepreneurship? [00:19:22] Why we need to instill leaps of faith in people who want to be founders [00:21:06] In terms of data science, entrepreneurship in this COVID/post-COVID area. What do you see as some problems with tackling that in enterprising data? Scientists can can identify and then get into an opportunity? [00:22:29]So you've been writing a lot about innovation at Tilt to the Windmill. How should be incumbents think about innovation? [00:23:02]A deep dive into various models of innovation [00:26:38] An excellent and thorough discussion on intrapreneur [00:30:37] Some great advice for one man data science teams who are on an intrapreneurial journey [00:33:50] The stages of growth intrapreneur developing data products within their organization will face and how to overcome challenges in those stages [00:36:50] We get into music science and its intersection with data science [00:43:13] What's your go to music? [00:43:54] The important soft-skills that a data scientist needs for success [00:47:11] What are some key takeaways from your book - Propose, Prepare, Present - that you think a data scientist should apply when communicating with non-technical audiences? [00:49:27] Let's talk a bit about being evil. You say start-ups should be more evil, that sounds terrible. What are you thinking? What are you trying to communicate with that? [00:53:23] What's the one thing you want people to learn from your story? [00:54:24] What's it mean to solve for interesting? [00:56:00] Jumping into a quick lightning round: What would be the number one book, other fiction or non-fiction or both that you'd recommend our audience read and your most impactful takeaway from it? [00:57:19] If we could somehow get a magical telephone that allowed you to contact 18 year old Allistair. What would you tell him? [00:58:45] What it means to cultivate a personality [01:00:07] What's something you've done at one of your ventures that's been just evil enough? [01:02:57] What's the best advice you've ever received? [01:04:58] What motivates you? [01:06:10]So what song do you currently have on repeat? [01:06:34] How could people connect with you? Where can they find you?Special Guest: Alistair Croll.

On this episode of The Artists of Data Science, we get a chance to hear from Deborah Berebichez, a physicist, data scientist, and TV host. Her passion for learning and teaching has led her to become a voice for women and minorities in STEM. She gives insight into how she broke into the data science field, how to cultivate the right mindset to succeed, and the importance of diversity and inclusion in tech. Deborah shares with us how she grew up in a conservative environment, and the obstacles that she had to overcome to become the first Mexican woman to graduate with a physics PhD from Stanford University. WHAT YOU WILL LEARN [17:11] What value Deborah believes data science will bring within the next few years [20:43] Deborah's role model for being curious and inquisitive [27:42] Actionable tips for cultivating the habit of critical thinking [40:07] Advice on how to be the hero when you feel like a failure [51:47] Advice for women that want to break into tech QUOTES [19:57] "…I think the most amazing things that are going to happen [due to data science] are giving transparency to industries and to communities of people that otherwise in the past have remained quite invisible" [24:19] "I am a very strong supporter of making people learn and educat[ing] others in the basics of science so that we can become empowered citizens and know more about the world." [24:50] "…Critical thinking to me is about questioning authority…[it] allows us to to gain the proficiency in being able to discard lies from the truth." [28:12] "…Make sure that you recognized the biases that you have about the world and what you want to be truth so that you don't blind yourself to the actual results of a data analysis" [40:59] "…The people who end up succeeding in life are not the ones for whom things come easily. They are the ones for for whom obstacles are just something to transcend and the ones that get up every time that they experience a failure in their lives and they keep going." FIND DEBORAH BEREBICHEZ ONLINE LinkedIn: https://www.linkedin.com/in/berebichez/ YouTube: https://www.youtube.com/channel/UCT58Xn45TFrXGIEqRcT_yTg Instagram: https://www.instagram.com/debbiebere/ Twitter: https://twitter.com/debbiebere SHOW NOTES [00:03:44] The path into data science [00:07:59] Dr. Berebichez talks about how she got involved with Metis and the work she's doing there. [00:09:36] What data science will look like in 2-5 years [00:11:05] The need for different skillsets in data science, from translators to engineers. [00:12:12] How to be a great data scientist [00:14:30] What do you think would be the scariest application or the scariest abuse or misuse of data science machine learning in the next two to five years? [00:16:46] What ways do you think Data science will have the biggest positive impact on society in the next two to five years? [00:20:34] Dr. Berebichez talks about a historical figure that means a lot to her: Tycho Brahe [00:24:38] Critical thinking and the data scientist [00:27:33] Actionable tips to become a better critical thinker [00:29:33] Why are humans so bad at appreciating or conceptualizing probabilities? [00:31:09] Why is it important that we cultivate this intuition for what probability represents? [00:33:53] Is data science an art or science? [00:36:16] How does the creative process tend to manifest itself in Data science? [00:38:00] For people out there who are trying to break into data science and maybe they feel like they don't belong or they don't know enough or they aren't smart enough. Do you have any words of encouragement for them? [00:39:54] So in those moments where we feel like we're failing or failures, we want to give up because it's hard upskilling and learning so much to get into Data science. What can we do to feel like a hero? [00:41:48] Breaking into data science when you're coming from a non-technical background [00:44:06] What would you say would be like the biggest myth that people tend to hold in their heads about breaking into Data science? And would you mind debunking that for us? [00:45:49] The story of Rupesh [00:49:59] The importance of progress over perfection [00:51:32] Debbie shares her experience being a woman in tech and provides the women in our audience some advice and encouragement. [00:53:30] What could the Data community and men in the Data community do to foster inclusion of women in Data science and AI? [00:55:39] What's the one thing you want people to learn from your story? [00:56:24] The lightning roundSpecial Guest: Deborah Berebichez, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Srivatsan Srinivasan, a data scientist who has nearly two decades of applying his intense passion for building data driven products. He's a strong leader who effectively motivates, mentors, and directs others, and has served as a trusted advisor to senior level executives. He gives insight into how he broke into the data science field, the importance of focusing on business outcomes,, and some important soft skills. Srivatsan shares with us his tips on how to navigate crazy job descriptions, as well as his methods for communicating with executives. This episode contains actionable advice from someone who has been working with data since the beginning! WHAT YOU WILL LEARN [10:26] What it means to be a good leader in data science [11:45] How to productionize a model [15:01] Concept Drift [17:54] How to navigate difficult job descriptions [20:33] Tips on communicating with executives QUOTES [9:09] "I think more and more data scientists today are technology focused. They need to use technology to just solve a problem…they should focus more on business outcomes." [10:26] "…a good leader in data science…should be ready to embrace failure" [12:21] "…start with modularizing your code, see where are your common functions that you can use" FIND SRIVATSAN ONLINE LinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/ YouTube: https://www.youtube.com/c/AIEngineeringLife SHOW NOTES [00:01:17] Introduction of our guest today [00:02:58] Let's talk a little bit about how you first heard of data science and what drew you to the field and maybe touch on some of the challenges you faced while breaking into the field. [00:05:13] You've been so generous with your knowledge and sharing your knowledge, creating some really well crafted content for LinkedIn and YouTube. And I'm wondering what's the inspiration behind that? [00:06:35] Where do you see the field headed in the next two to five years? [00:08:41] In this vision of the future, what's going to separate the great data scientists from the ones that are just merely good? [00:10:08] What does it mean to be a good leader in data science? And how can an individual contributor embody the characteristics of a good leader without necessarily having the title? [00:11:30] What are some challenges that a a notebook data scientist can face when it comes time to productionize a model? And do you have any tips for how to overcome those hurdles? [00:12:43] Some actionable tips that you can use today for moving outside of notebooks [00:13:32] What are some things that we should be keeping track of once we have deployed our model into production? [00:14:44] A discussion of concept drift and data drift [00:17:08] Do you have any advice or insight for people that are breaking into the field and they see these job postings that look like they want the abilities of an entire team rolled up into one person and then they they just become scared of applying. Do you have any tips or advice for them? [00:19:12] What are some soft skills that candidates are missing that are really going to separate them from their competition? [00:20:23] And do you have any tips for a data scientist who might find themselves having to present to a non-technical audience or perhaps a room full of executives? [00:21:16] What's the one thing you want people to learn from your story? [00:22:03] The lightning roundSpecial Guest: Srivatsan Srinivasan.

On this episode of The Artists of Data Science, we get a chance to hear from Brandon Quach, a data scientist who has a PhD in bioengineering, and has worked on threat analysis for security and business ecosystems. He's currently a principal data scientist and manager, leading the charge to modernize the customer experience by applying machine learning to customer support. Brandon shares his perspective on how data scientists should approach problems, the importance of passing on knowledge, how to be a leader in the data workspace, and the appropriate mindset to develop when faced with difficult problems. Speaking with him was an honor, and this episode has something for everyone to take away from. WHAT YOU'LL LEARN [11:50] Brandon discusses automation and whether or not we will be able to automate human judgement [18:01] What qualities do you need to become an intrapreneur in your organization [22:19] A unique way to approach leadership in your organization [30:08] Why great thinkers abhor being told what to do [37:37] How important is agile and scrum methodology in data science [46:13] The mindset you need to accept the monsters in your life QUOTES [22:37] “...trust, to me, comes from your ability to not be scared of the results that come out of your work or anything that you do.” [27:25] …”If I received good advice and….good guidance, then I feel it's sort of my job, my duty, to pass it on to the next generation” [30:08] “Great thinkers like to figure things out and come to a point that they believe in the solution.” [35:33] “I want people to look back long after I've gone and say...that decision that was made early on that nobody had appreciated...turned out to be really critical down the road…” [53:33] “...successful data scientists can think through any kind of problem surrounding data science, not just the core problem.” [57:05] “You should learn how to think through code. How can you learn how to think through code?. Well, either you have a built in imagination... and/or you have gone through a lot of iterations of code and you can understand the process...” FIND BRANDON ONLINE Twitter: https://twitter.com/databrandon Linkedin: https://www.linkedin.com/in/bquach/ Website: https://databrandon.com/ SHOW NOTES [00:01:16] The introduction for our guest today [00:03:52] Brandon's journey from academia to industry [00:06:13] What were some of the the struggles and challenges he faced during your journey? [00:09:48] Things are never as simple as they seem in data science [00:11:41] The future of data science [00:12:06] The automation of data science workflows [00:13:58] The automation of human judgment and human creativity in problem solving [00:15:45] What separates the great data scientists from the good ones [00:17:01] Why a lot of data scientists tend to have PhDs [00:18:01] What is an intrapreneur? [00:21:56] A leadership philosophy for data science [00:27:40] Great advice for data scientists new to the career [00:29:34] Why you should never tell a data scientist what to do [00:32:25] Autonomy and mastery lead to purpose for data scientists [00:33:42] The mindset of future judgement [00:37:25] Agile and scrum in data science [00:42:34] Grit, Mindset, and Drive for data scientists [00:43:55] Dealing with data science stakeholders and handling machine learning setbacks [00:47:25] Imposter syndrome in data science [00:50:31] Soft skills for data scientists [00:51:56] Brandon talks about some interesting interview questions he asks to assess a candidates thought process [00:54:54] How to deepen your intuition and knowledge of data science [00:58:08] What's the one thing you want people to learn from your story? [00:58:56] The lightning roundSpecial Guest: Brandon Quach, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Kyle Polich, a computer scientist turned data skeptic. He has a wide array of interests and skills in A.I, machine learning, and statistics. These skills have made him a sought after consultant in the data science field. He is also the host of the very popular data podcast, Data Skeptic, which discusses topics related to data science all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. In this episode, Kyle defines what a data skeptic is, and also goes on to give advice on how to communicate effectively with leaders and executives as a data scientist. Kyle brings a very unique perspective related to all things data, along with actionable advice! WHAT YOU WILL LEARN [00:11:49] Probabilistic data structures [00:15:19] How probabilitistic data structures will change the future [18:55] Is data science more of an art or science? [23:36] Advice for data scientists trapped in a perfectionist mindset [30:43] Important soft skills that you need to succeed [39:40] How to communicate your ideas with executives QUOTES [11:43] "…greatness is achieved by a commitment to your craft and pursuing it." [16:42] "The greatest trick the devil ever pulled was convincing the world he didn't exist. That's what good data science does to me." [24:42] …"being able to fall down but get up fast is important." FIND KYLE ONLINE LinkedIn:https://www.linkedin.com/in/kyle-polich-5047193/ Twitter:https://twitter.com/DataSkeptic Podcast:https://dataskeptic.com/ SHOW NOTES [00:03:01] How Kyle got into data science [00:05:20] What the heck is a data skeptic? [00:07:47] What do you think the next big thing in data science is going to be the next, say, two to five years. [00:11:04] How to be a great data scientist [00:11:49] Kyle gives us a primer on probabilistic data structures [00:15:19] How do you see probabilistic data structures impacting society in the next two to five years? [00:17:19] Data skeptic mission [00:18:39] Kyle answers the question - how do you view data science? Do you think it's more of the art or more science? [00:21:09] We talk about principles and methodologies as it related to art and science [00:21:52] Kyle shares his thoughts on the creative process in data science [00:23:22] Kyle shares his thoughts on being a perfectionist when you're working on a project [00:25:28] Do you have any tips for people who are coming from a non-technical background and they're coming up to these technical concepts face to face for the first time? [00:26:42] We talk about the importance of being a lifelong learner and Kyle shares some advice for aspiring data scientists out there who feel like they haven't learned enough yet to even consider breaking into the field. [00:28:47] What is your advice for data scientists who they feel like they've learned enough, and just don't even need to learn anything else to be successful? [00:30:27] We talk about the soft-skils that candidates should pick-up, and Kyle shares advice for people who are in their first data science roles. [00:31:03] Some insight into the purpose of your resume and how you should leverage that for your job search [00:34:17] We talk about the pursuit of certificates versus the achievement of self-directed learning projects [00:36:18] Tips on finding the right type of project to add to your portfolio [00:39:13] For those people a little further along in their career, Kyle shares tips on how to effectively communicate with executives [00:42:16] We talk about our shared love for Bill Murray [00:43:06] How you should respond when you come across job postings that look like they want the skills of an entire team rolled up into one person. [00:46:22] What's the one thing you want people to learn from your story? [00:47:19] The lightning round. Special Guest: Kyle Polich.

On this episode of The Artists of Data Science, we get a chance to hear from Scott Taylor, also known as the "Data Whisperer." He has spread the gospel of digital transformation through public speaking engagements, blogs, videos, white papers, podcasts, puppet shows, cartoons and all forms of verbal and written communications. He has also helped organizations, such as Microsoft and Nielsen, comb through and organize their data for meaningful use. Scott shares his "eight 'ates of master data", a set of rules to engage with master data in a meaningful way. He also goes over his tips for communicating with executives, along with important soft skills that are being overlooked by data scientists. Scott is very articulate, and his passion for data and teaching are definitely evident in this episode! WHAT YOU WILL LEARN [12:57] The eight 'ates of master data management [17:04] Data science communication with executives [21:45] Legacy data systems QUOTES [3:37] "It's not all about building. Sometimes it's about making sure things are structured and organized the right way." [7:11] "Hardware comes and goes. Software comes and goes. Data always remains." [16:11] "Data, to have value, has got to be in motion." [20:36] "If you're a data scientist, you are the business….and it's impossible for you to learn too much about your own business." [27:08] "…you've got to bring people from "I have no idea what you're talking about" to "how can we live without this?" and that comes from telling a good story." WHERE TO FIND SCOTT ONLINE LinkedIn: https://www.linkedin.com/in/scottmztaylor/ Twitter: https://twitter.com/stdatawhisperer Website: https://www.metametaconsulting.com/ SHOW NOTES [00:01:20] The introduction for our guest today [00:02:54] Scott talk to us a bit about his professional journey, how he got involved in the data world. And what drew him to this field? [00:04:40] Scott talks to us about some of the early gigs he had in the data space. [00:05:54] Where do you see kind of the field of big data and digital transformation? What's this landscape going to look like in two to five years? [00:07:41] Scott talks about how the stakes are changing and how data management is unavoidable [00:08:32] Scott goes more in-depth as to how the stakes are changing and how he's seen it play out across enterprise organizations. [00:09:56] In this vision of the future where the stakes are changing, what do you think is going to separate the great data professionals from the merely good ones? [00:11:25] Scott takes us through what he calls the "eight 'Ates" of data: Relate, Aggregrate, Validate, Integrate, Interoperate, Evaluate, Communicate, Circulate [00:16:29] Scott breaks down how to effectively communicate with executives and what they care about - hint: not necessarily what you care about as a data scientist [00:18:27] Scott shares some tips for data scientists coming into organizations with legacy organizations and how to navigate that landscape [00:21:11] What are the similarities or differences in the challenges a legacy system organization faces versus a tech startup? And how can one navigate these waves? [00:23:40] So what would you say is the biggest data blunder in the last year or two? He describes the system of hotel keys and how it relates to master data, very interesting! [00:25:12] So what about some data wonders? He describes an everyday application of a wonder: the checkout counter at a grocery store. [00:26:41] More insight on communicating with stakeholders and executives [00:27:56] What are some of the soft skills that that candidates are missing that are really going to separate them from the competition? [00:29:29] There's a lot of people out there who are trying to break into the data space and maybe they feel like they don't belong there or know enough for they aren't smart enough. Do you have any words of encouragement for them? [00:31:20] Scott does a deep dive into his passion for data and how you can cultivate it in yourself [00:33:02] What's the one thing you want people to learn from your story? [00:34:21] The lightning roundSpecial Guest: Scott Taylor.

On this episode of The Artists of Data Science, we get a chance to hear from Brandeis Marshall, a computer scientist that is excellent at breaking down difficult concepts into easily digestible pieces. She is passionate about educating people on data, as well as understanding the impact data has on race, gender, and socio-economic disparities. She is the CEO of DataEdx, a company which focuses on making data science accessible to all professionals. She shares her perspective on how data impacts communities, how to promote diversity and inclusion in the data science space, and the importance of documenting your process. It was an absolute pleasure to hear her perspective, and I believe her message will help broaden the data science field. WHAT YOU WILL LEARN [8:29] How data impacts marginalized communities [13:29] From Brandeis's perspective, what separates great data scientist from good ones [14:48] Understanding how data is packaged, and ways to break it down into bite-size portions [19:30] The impact of live tweeting on social movements [30:09] Discussing inclusiveness in the data workspace [39:46] How to be gritty and break away from negative thoughts QUOTES [7:57] "I'm trying to do my best to be… that beacon to talk about data in sizeable, understandable nuggets, because it's not just a science thing. It is our everyday life." [11:45] "…if you stay within your own lane in your own expertise, only talking to people who have your particular background, you're losing the whole story… and with data, there's always a story" [29:34] "…I want…other people to know that they can talk about their particular ethnicities, content in a research space, in the tech space, and still be successful." FIND BRANDEIS ONLINE Twitter: https://twitter.com/csdoctorsister LinkedIn: https://www.linkedin.com/in/brandeis-marshall/ Website: https://www.brandeismarshall.com/ DataedX: https://www.dataedx.com/ SHOW NOTES [00:01:50] Introduction for our guest today [00:04:51] Brandeis talks to us about how she heard of data science. What drew her to the field and some of the struggles and challenges she faced as she were breaking into the field [00:07:21] Break data in sizeable, understandable nuggets. [00:08:21] So where do you see the field headed in the next two to five years? [00:09:12] How do we shift the conversation so that all people are included in the data conversation? [00:10:39] What could data scientists start doing today so that two to five years in the future they understand the need for diversity of data and they're cognizant of it. What are some things that they could start doing today? [00:11:03] Data scientists need to get out of their comfort zone [00:13:12] How to be a great data scientist [00:14:27] What is data competency [00:16:38] What's the mission for your new startup, DataEdX? [00:19:30] Live tweeting, social movements, and data science [00:22:28] The technical aspects of the Black twitter project [00:27:31] Project Ideas for Data Scientists [00:29:04] If there is any impact that you want your work in this space to have on society as a whole? [00:30:08] The unfortunate effects marginalization in the data workspace [00:33:30] Diversity in data science [00:36:34] Dispelling the myth of "it's all about technical skills" and questioning the "move fast" ideology in tech. [00:39:46] Grit and being determined to seeing your goals through even in the face of challenges. [00:43:05] What's the one thing you want people to learn from your story. [00:43:23] The lightning roundSpecial Guest: Brandeis Marshall, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Eric Weber, a lifelong learner, mathematician, and data scientist. He has cultivated a passion for sharing his work and experience with others to help them become excited about data science, as well as educating executives on all aspects of data science. He gives insight into his perspective of learning, how to be a leader in the data science field, and important skills that data scientists need to develop. Eric shares with us what drew him to the field, and his transition from academia to the business side of data science. This episode highlights the journey and success of someone who has seen the field develop from the beginning, and has continuously improved over time. I think there is a lot to learn from this conversation! WHAT YOU WILL LEARN [4:43] How to transition from academia to industry [11:40] How to become a great data scientist [20:59] How to communicate effectively with your team [24:07] The art in science [34:52] What soft skills you need [41:15] What you should do about data science job descriptions QUOTES [6:35] "…my journey was all about figuring out two things. One, how to work with data at scale. And two, what does it mean to actually do data science in a business context. And those two things are really, really important…" [12:17] "You don't need to build an incredibly powerful model for every situation, but you need to know what's going to allow the business to thrive in a productive way." [19:48] …"getting by is not a long term solution to delivering value for a business, because what you're doing right now to get by is probably going to be automated in a few years…" [23:50] "You're not always gonna be the expert in the room. And if you are, you're probably in the wrong room." FIND ERIC ONLINE LinkedIn: https://www.linkedin.com/in/eric-weber-060397b7/ Twitter: https://twitter.com/edweber1 [00:01:12] Introduction for our guest today [00:04:17] How Eric broke into data science [00:06:20] The challenges of transitioning from academia to industry [00:08:21] Where do you see the field headed in the next two to five years [00:09:16] Eric talks about the age of the specialist, and how its become the norm recently. He also talks about how this is now a "prove it" time for data science teams [00:11:32] How to be a great data scientist [00:12:54] Eric goes into detail about the need to deliver business value versus scientific value [00:14:01] Data scientists are lifelong learners [00:16:00] Why data science tends to be a more highly compensated field [00:16:17] What's your advice to aspiring data scientists who feel like they have not learned enough to start applying for jobs? [00:18:44] Why you never stop learning as a data scientist [00:20:47] Don't be afraid to not know something [00:22:09] The importance of finding teams where asking questions and being open is is valued [00:23:59] The art of data science [00:25:20] Curiosity and creativity in data science [00:30:10] How to be a great leader in data science [00:33:15] We talk about the book by Liz Wiseman called Multipliers [00:34:36] The soft skills you need to succeed [00:38:48] How could data scientists develop their business acumen and product sense? [00:41:15] Don't be discouraged by these job descriptions [00:43:28] Going from notebooks to productionizing models [00:45:51] Why do we even build models in the first place? Mainly for two reasons, find out here. [00:47:09] What's the one thing you want people to learn from your story? [00:48:04] The lightning roundSpecial Guest: Eric Weber.

On this episode of The Artists of Data Science, we get a chance to hear from Chhavi Arora, one of the rising stars in the data science industry! She gives insight into how she broke into the field, the hurdles she had to overcome in the job search, and how she answered commonly asked questions during an interview. Chhavi shares with us what got her interested in data science in the first place, along with the biggest self-limiting fear that she had to overcome in order to begin her journey into data science. If you are interested in becoming a data scientist but don’t know where to start, then this episode can answer many of your questions! WHAT YOU'LL LEARN [9:23] The mindset you need to adopt during the job search process [11:04] How Chhavi overcame her biggest self-limiting belief [14:58] How to get a leg-up on your competition when applying for jobs [18:05] Commonly asked questions during interviews, and how to answer them [24:55] How to prepare questions for the interviewees, and why it’s crucial QUOTES [6:39] “...every project you do as a data scientist needs to be something that you have interest in so that you know what questions you are looking for and you will eventually find answers to your work.” [12:46] “...every little weakness that you think you have can become a positive thing if you spin the story right.” [17:16] “...the most important thing is to never, never stop being passionate about data science...because the learning never stops.” FIND CHHAVI ONLINE LinkedIn: https://www.linkedin.com/in/chhavi-arora/ SHOW NOTES [00:01:23] Introduction for our guest today [00:03:14] Chhavi talks to us about her experience at the NGO and how that got her interested in data science and and machine learning. [00:05:07] Chhavi tells us more about how she went about building out her projects. And how she comes up with ideas for her projects. She talks about how he creates independent projects based on what she finds interesting. [00:09:23] How important is having the right mindset during the job search? She talks about the importance of the growth mindset and how it carried her through the ups and downs [00:11:04] So what would you say was kind of the biggest self limiting belief that you had to overcome when you were in the job search? [00:12:14] How did you address resumé gaps during the interview process? It's all a matter of perspective - it's only a negative if you let it be negative. Chaavi gives some great tips [00:14:42] We get into what the job search process was like for Chhavi and she walks us through her process for applying for jobs and then getting interviews or whatnot. Listen to find out why it's not enough to send a resume and just hope that somebody would call you back. [00:16:06] ow many interviews did you go on before landing your current role? [00:17:02] Do you have any words of advice or encouragement for those rising stars out there who are now in the same position that you once were? [00:18:05] Jumping into a mock interview where Chhavi will answer commonly asked interview questions. Starting with: Tell Me About Yourself [00:19:42] Can you describe a time when you had to deal with competing priorities and with competing deadlines? And how did you handle that? [00:21:10] What's the most difficult type of person to deal with and how do you deal with them? [00:23:08] Walk me through your discovery process when you're starting a new project. [00:24:20] We talk a bit about the STAR format for answering interview questions [00:24:55] What's the process for coming up with questions to ask during the interview? [00:27:11]Let's say it comes time to talk about a technical question and the interviewer is asking you about some technical topic. How do you handle that type of question? [00:28:49] What's the one thing you want people to learn from your story? [00:29:24] Let's jump into a quick lightning round here. Python or R? [00:29:29] All right. Where do you see yourself in five years? [00:30:01] If you can go back in time to have a conversation with 18 year old Chhavi, what would it be? What would you tell her? [00:30:28] So how about your favorite book, fiction or non-fiction or both of you, if you'd like, and your biggest takeaway from them? [00:31:23] How people can connect with Chhavi, and also tips on ineffective ways to connect with anyone on LinkedIn.Special Guest: Chhavi Arora.