If you say right now that being a hands-on mother and being the best career woman in the field of data science is unachievable, our guest, Kate Strachnyi, would completely disagree as she shows you what she’s been doing so far.
In this episode of SuperDataScience Podcast, learn more about the field of data visualization, her career, and her wide social media influence!
About Kate Strachnyi
Kate Strachnyi is the Program Manager of Data Analytics for Deloitte Advisory based in New York City. Kate’s also the author of Journey to Data Scientist, Instructor of Tableau Visual Best Practices: Go from Good to GREAT! at Udemy, and the host of Humans of Data Science (HoDS) on my Story by Data YouTube channel.
Kate is surely unstoppable. When she wanted to be the best mom and the best data scientist without compromising one, she made it happen. I promise you, you’ll be listening to her share her stories with a gaping mouth as you wonder how she manages to smoothly juggle everything.
Her career went from finance to risk management to consulting. Kate Strachnyi has gained a lot of experience and knowledge in these fields that contributed on how she is now one of the Data Science influencers. A bulk of it would then be attributed to her passion of sharing knowledge to people through writing and producing visual content (i.e. videos)
For those who aren’t aware, Kate has been actively posting content on her LinkedIn, the StorybyData site, and the StorybyData YouTube Channel. She writes articles and hosts the Humans of Data Science (HoDS) Project. Aside from these platforms, she’s also published her first book and about to release two more which are all related to data science. She does all of these because sharing information has a two-way benefit for you and your readers. Kate says she understands the topic better and she’s able to teach others too.
Through these online engagements with people from the field, she was able to take note also of some issues faced by entrants and emerging professionals. The #1 thing that worries them is getting their first job. So, Kate and I discuss tips and pieces of advice for you. Just remember to build your network, put yourself out there and you don’t have to be the master of everything to excel in the field of data science.
At the end of the podcast, we touch on the topic of data visualization. Discover the difference of visualization for data mining and visualization for data presentation. Know also what visualization tools she’s using, how important still is the role of humans in the future of automation, and how she’s integrating interactivity in data visualization.
I really hope you enjoy this episode with Kate Strachnyi. You can catch her also on next year’s DataScienceGO Conference as a speaker.
In this episode you will learn:
- Being a mother and a career woman in the field of data science.
- How does it feel to be a known social media influencer in data science?
- Kate talks about her first book Journey to Data Scientist.
- Data Science is not for everybody.
- What’s her advice to finally land your first role/job as a data scientist?
- Exploration vs. Presentation: Roles of Data Visualization
- Incorporating interactivity in data visualization.
- Future of Data Visualization
- Kate talks about her the greatest takeaways from her first book and the two upcoming books.
Items mentioned in this podcast:
- StorybyData’s Humans of Data Science (HoDS) Podcast YouTube Channel
- Journey to Data Scientist: Interviews with More Than Twenty Amazing Data Scientists by Kate Strachnyi
- GARP: Global Association of Risk Professionals
- Tableau Course by Kate Strachnyi
Kirill Eremenko: This is episode number 205 with data science influencer and author, Kate Strachnyi.
Kirill Eremenko: Welcome to the Super Data Science podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring inspiring people and ideas to help you build your successful career in data science.
Kirill Eremenko: Thanks for being here today and let's make the complex, simple.
Kirill Eremenko: Welcome to the Super Data Science podcast, ladies and gentlemen. Super excited to have you back here today. And we've got a very exciting guest joining us for today's episode, Kate Strachnyi. Kate is a data science influencer and a data science author. So what will we be talking about in this episode? Well, you will find out all about Kate's journey from working at a bagel store to becoming a data science influencer with over 30,000 followers worldwide, and publishing books, and creating video podcasts, and massively contributing to the data science community.
Kirill Eremenko: We'll also talk about connecting the dots and looking back at how this journey unraveled and how Kate's career took her to different places and how she ended up working at Deloitte and rolling out data science tools company wide. You'll learn about her work with Tableau, creating self-serve analytics tools, and creating a culture of data science within organizations. Also, we'll talk about the three books; one, which you can already get, which is called Journey to Data Scientist, and two new books that Kate is already working on the [inaudible 00:01:58] data science leaders and mothers of data science. So you'll learn all of that and much, much more.
Kirill Eremenko: Can't wait for you to check this episode out, it's gonna be a great and fun ride, so buckle up and, here we go. Without further ado, I bring to you Kate Strachnyi, data science influencer and author.
Kirill Eremenko: Welcome ladies and gentlemen to the Super Data Science podcast. Very excited to have you on this show today. Today we have a super exciting guest with us, Kate Strachnyi. How are you going, Kate, today?
Kate Strachnyi: Doing excellent, thank you so much for having me here.
Kirill Eremenko: Very excited to have you, because you have your own podcast, is that correct?
Kate Strachnyi: Yes, it's a video interview podcast called Humans of Data Science, which is hosted on Youtube under the Story by Data channel.
Kirill Eremenko: That's so awesome. So how does it feel to be on the other side now? Being the guest?
Kate Strachnyi: It feels funny, I get to talk about myself now. So I'm excited.
Kirill Eremenko: That's so cool, that's so cool. And I gotta come on your show at some point as well, to do the opposite, that you'll be introducing me.
Kate Strachnyi: Yes.
Kirill Eremenko: That's so cool. So Kate, you are a ... not just to have you on the podcast, you are a major, well in my view, a major influencer in the space of data science. You have a book that you've published, another two that you're working on, they're gonna be published very soon, and I had the honor of reading one of them and give you my comments on that, which I really enjoyed, really liked. The Disrupters I believe it was called, Disrupters of Data Science. And you have a ... you're actually influencing people, you have almost 30,000 follower on LinkedIn, people who are listening to what you are saying, listening to your tips, advice in the space of data science, what's happening in the field. So very, very excited about this podcast and can't wait to see where this will take us in today's conversation.
Kirill Eremenko: How are you feeling about today's session?
Kate Strachnyi: I'm excited. I've been looking forward to this, so thank you.
Kirill Eremenko: Awesome.
Kirill Eremenko: To start us off, how would you describe who you are and what you do to somebody you meet for the first time?
Kate Strachnyi: I usually tell them I make pretty pictures with complicated data. I think that goes well with most people, but I usually tell them I use some kind of tool to take complex data from different data sources, and try to help business leaders make decisions quickly. That's how I describe it.
Kirill Eremenko: Okay, and that's probably a very succinct description of what data visualization is all about. Which company do you work for at the moment?
Kate Strachnyi: I'm currently at Deloitte & Touche, and I've been there for a little over seven years now.
Kirill Eremenko: In New York, right?
Kate Strachnyi: In New York, but I'm currently based from home, so I don't actually have to go to any office. I'm just sitting in my bedroom as I speak.
Kirill Eremenko: That's awesome ... that's awesome. And looking after your two wonderful kids, you said; 2 and 4 years old, right?
Kate Strachnyi: Yes, I have two girls, age 2 and one that just turned 4 last weekend.
Kirill Eremenko: That's so cool. I wanted to also say, did you know that I worked at Deloitte as well?
Kate Strachnyi: No, I actually did not know that.
Kirill Eremenko: Well, yeah. I worked at Deloitte, but not in New York, in Australia, in Brisbon. I only lasted two years, I really felt after two years that I've learned what I wanted to learn, and it's time for me to move on. I admire, very much, people who do many years of consulting. I have a friend who did ten years at Deloitte. Also, actually, a lot of final years were in New York.
Kirill Eremenko: Tell us about your journey. Like how did you get into Deloitte, how did you get into this space of consulting and data science?
Kate Strachnyi: Yeah, sure. So it actually started out ... I started my career after graduating with a finance degree. I started working for an organization called GARP, so it's the Global Association of Risk Professionals, which basically tried to promote desk practices and risk management mainly for financial services institutions. My job was to use LinkedIn or something to try and find chief risk officers and then pitch our training services to them, in which I met a chief risk officer of Bank Leumi, and as I was pitching him, we actually got to talking and we both relationship, and a few weeks after that, he told me he was looking to hire somebody. My previous career was actually in risk management.
Kate Strachnyi: So I went from GARP, I started working for this bank, and I had a blog, I guess I still have it I'm just not active, called riskarticles.com, which some recruiter from Deloitte actually picked it up and said, "Wow, you must really love risk management that you just have this blog and you continue to write there." They called me for an interview, so at that point, I had no idea that I would ever actually get to work for Deloitte. I had, I guess not low self-esteem, but I just never thought of myself as working as one of the big four consulting companies. I ended up getting the job and working in risk management, so I can see what you mean that you only left two years, because working in client service can be difficult. It does require a lot of travel, and usually, nights, weekends, every night and every weekend, at least for me, was the case.
Kate Strachnyi: What happened when I had my first child, was I couldn't imagine continuing on with this type of schedule and not being able to see my daughter, so I asked them if there was another alternative, or I was also looking outside of the firm to find something that I can work remotely, even one or two days a week, to get more balance. And what happened was they needed a role for their CIO program, so this Chief Information Officer program, and they said it was going to be a role for an inside strategy manager, which at that point, I had no clue what it meant, but they said it'll keep me home for most of the month. I think I had to come in once a month to the city, and I'm like "Whatever it is, I will do it."
Kate Strachnyi: Lucky for me, it was analyzing data using Tableau. They basically gave me their whole SalesFirst database, they said here is Tableau, which was the first time I ever seen it, and they said go ahead and give me insights. That was my first introduction and, I guess when I fell in love with data visualization. That's kind of the journey and I think I've lasted so long, because for the past four years or so, I've been working remotely in an internal role, so my client is Deloitte versus the actual clients, like the banks that I worked for that were more demanding in terms of being on location and then working longer hours.
Kirill Eremenko: Well, what a random journey, you started out in one area, risk professionals and finding clients, being in the sales side of things, and then instead of you pitching to that risk officer in Bank Leumi, it happened the other way around and he pitched to you ... or they pitched to you to get you into the bank, then through your blog you got into Deloitte. Through a blog you kept, somebody from Deloitte randomly saw it and they got in to Deloitte, and there, through having children and looking for a more laid-back role, you discovered data visualization.
Kate Strachnyi: Yes.
Kirill Eremenko: How crazy is that?
Kate Strachnyi: It is crazy. And it actually helped me further down the line, because the CIO program is meant for ... it's kind of a confrontational thing, so they want new faces in every year or two, so after a year and a half of being that program, it was time for me to go back to client work, which at that point, I think I was already having my second kid, and I'm like "No, wait." So what happened there was, I started looking for another internal role and it's a group that sits closely with the chief executive officer of Deloitte's advisory side. They needed a person, so I signed up to go to that group, and lucky for me, they were using a lot of spreadsheets to track all their data, and they didn't really have any reporting, or dash boarding, or any kind of insights into what's happening with their data.
Kate Strachnyi: So me with my knowledge of Tableau at that point, I was able to just take all their data and quickly throw some charts together, and at that point, the CEO saw it and he's like, "I want this everywhere." So we started rolling out Tableau, pretty widely in the firm, because it was just so much easier for the executives to see what's happening behind the scenes.
Kirill Eremenko: Wow, that's so cool. Tableau must love you. You must be one of their best advocates.
Kate Strachnyi: Yes, we had some calls with them. I think they're happy with how things are going.
Kirill Eremenko: 'cause Deloitte's such a massive company to get them on board with a tool like that. That's a big win for Tableau. I guess win-win, Tableau's a great tool.
Kate Strachnyi: Yes, it really is.
Kirill Eremenko: Awesome, awesome, it's very exciting to hear. How did you get into the space of ... I can understand that journey, like lots of random coincidences and, actually reminded me of what Steve Jobs said at his Stanford speech, one of the three points he mentioned was connecting the dots, that looking forward impossible to connect the dots. Why did you go to, for instance, go to Bank Leumi or why did you start the blog? How that's gonna pay out in your life? But looking back, it's so easy to connect the dots, we just did that, right? We just looked at how everything worked out to get to where you are.
Kirill Eremenko: But my question was, that part, we thank you for describing it, very exciting journey, but what I wanted to see was, how did you get into the space of sharing your expertise and journey in data science, and becoming an influencer in the space of data science on LinkedIn and on Twitter, and generally social media? Even writing books. What gave you that push?
Kate Strachnyi: Well I think intrinsically, that's just how I am. When I was still in that risk management space, the reason behind starting that blog was, one, because I was given something to do right, they said read this regulation and see if it applies to the bank and let's do some risk assessment, so I started googling, obviously, when somebody tells you what to do, you google, right? To see if there's an easier way to understand something, and I realized that there wasn't, so I started trying to fill that gap with my blog to try to read it, and then understand it, and then I guess it was pretty useful to other people, because I had several thousands of subscribers.
Kate Strachnyi: I think for the data side of things, it's very similar because when I learned something, I loved to just teach others right away. It helps me understand it better, and then, especially if I create videos, then if I forget how to do it, I can always go back. But the whole being active on LinkedIn thing started, I think, at the end of last year, when I set my goals for the year, and I shared them on LinkedIn. I shared a pictures of myself writing my goals by the fireplace, it was also nice. I got really positive on that post, that people were also interested in setting goals, and I think that positive affirmation I received, kind of kept me going. The more I posted, the more positive feedback I got, and it just pushed me further and further.
Kate Strachnyi: I still don't think of myself as an influencer. I just like to say that I think out loud on LinkedIn, because none of my posts are ... I don't sit there thinking, "What should I post today?" There could be a week that I just don't post anything, and there could be a day where I post eight times just because things come to mind and I post them as I come up.
Kirill Eremenko: Yeah, it's very ... very interesting way of thinking about it. But alright, I can agree with that. Then writing books, is that how you write books as well? Just thinking out loud?
Kate Strachnyi: No, well the first book that, Journey to Data Scientist, came out when I was thinking to become more focused on data. When I get my hands on something, I like to go all in. So I decided that I would like to talk to actual data scientists from various countries, and levels, and ethnicities, just to get an idea of what did they do? Did they like their job? How did they get their job? Just because I was curious, and I was interviewing people, I decided that, maybe this will be interesting for others because I did see high interest from aspiring data scientists or people who just want to do this for a living. So I that this could help others, and that's when I started making it into an actual book. Plus I love writing, and it's just so much fun to publish your own book. It's kind of like having another little baby go out into the world.
Kirill Eremenko: Yeah, and so, how many people have you interviewed so far?
Kate Strachnyi: For the book, it was over 20. So I think it was close to 25 people for that book, it was just, like I said, people from random industries, random spaces, just to hear about their typical day, what did they like, what did they not like, what challenges did they face in becoming a data scientist, and I did receive plenty feedback on the fact that this book has helped others even make a decision. Whether or not they want to go into this space, because I know they call it the sexiest role of the 21st century, I think the book uncovers some of the not sexy parts of the role. That you're cleaning data for 80% of the time, and just, sometimes you work for days on something, then the decision maker says that they no longer need it, so little nuances that people might not hear about that help them make that decision.
Kirill Eremenko: I totally agree. I think that's very important for people to realize. It might be very tempting to jump into data science, but indeed, it's not ... I believe anybody can be a data scientist, but data scientist, regardless of background, but it really is not for everybody. Just based on personal preference; somebody might not be that into the field, or into the whole ideology of getting insights from data, and all of the different areas of expertise that come with it. That as you said, that cleaning data is probably an integral part of any kind of data science work that you do, and the whole data science project life cycle, and things like that.
Kirill Eremenko: What is the ... what's been the feedback you've been receiving? Do you have a ratio? You know 90%, 9 to 1 that people do believe data science is for them, 1 out of 10 believe it's not for them, or something like that.
Kate Strachnyi: Most of the people I talked to are really into data science, so I would say 90% of my network now on LinkedIn and Twitter is made up people who are just so passionate about the data science space for one reason or another. I think the issue they mostly run into is getting their first job. So I think that's the one thing discouraging people right, is they're taking those online courses or actual courses in universities, but then they're having issues bridging that gap between getting some experience to get your first job, and then the jobs requiring you to get some experience before they hire you.
Kate Strachnyi: I think that's the biggest gap I'm seeing, but most of the people, I would say, is probably 9 out of 10 that feel very optimistically about the role.
Kirill Eremenko: Interesting. So what would your advice be to people like that, who are listening to this podcast? Who are having trouble to bridge that gap and get their first role in data science?
Kate Strachnyi: Well, I would advise something that always Data Science.
Kate Strachnyi: Well, I would advise something that's always worked for me is just networking. That's how it got me first role. So, I graduated college in 2009 and that's when the financial crisis was hitting the United States. Especially the banking sector, which was my dream job at the time with my finance degree was to go work for a bank. The banks who were under a hiring freeze, and they were laying people off. So, it was not a great time to be on the market. And, I definitely remember being in that boat where I'm graduating, and ... I mean, I had a job at a Bagel store, right? I needed a real job. So, I was desperately looking and applying on sites which to me felt like ... And, I think likely was the case, that it was going into some black hole that was never actually looked by a person. So, I keep submitting my resumes and writing those cover letters, and nothing seemed to work.
Kate Strachnyi: So, I started networking. I used to go to these risks conferences, that's where I think your data science conferences is just awesome. It gets people in the room and actually networking, and meeting people, and face to face conversations, which is how you can actually build the relationships. And, that ... I met someone there who worked at GARB, and that's actually how I got the job.
Kate Strachnyi: We just started talking, and obviously you can't just shake everyone's hand and say, "Hi, I'm looking for a job. Hey, I'm looking for a job." You can have something to say, and show your value in a different way. Then somehow weave into the conversation, when they ask you what are you doing. Have them ask you first, and then you can bring up the fact that you're actively searching or something. Then tell them exactly what you want to do, because I think it's a turnoff when somebody messages you and says, "I want to work in data." You are like, "What do you want to do with data?" The more specific you can get, I think the closer you actually get to finding your role is first defining it.
Kirill Eremenko: True, true. Well, we'll get to that in a second. I just wanted to also say, it's really cool that you brought that up, the networking part. And, to add to that, I would even go as far as what you did. Start a blog in what you're doing, in what you're passionate about. Like, data science, but what kind of data science, what type of data science. Share your journey, share what you're learning on the blog. Then when you do go networking instead of just saying I'm looking for a job, you can instead share a business card with a link to your blog, or you can tell people as your logo. You can show them your blog on your phone or something like that. And like get them, show them that you're passionate about this space. Then they will want to hire you, rather than you saying that you're looking for a job.
Kate Strachnyi: Yes. I think it's definitely difficult when you've been looking for a job for eight months to not come off as, "Yes, I'll take it" desperate. But, I think the thing that is important, and I definitely agree with the starting a blog, and being active on social media to get your blog out there. People, when I tell them start a blog, they say something like, "Oh, I'm not a great writer." I don't think you have to be a great writer. I think people have their own unique way of writing, just because you don't fit into the mold of the person that you think is the perfect writer. I don't think of myself as a great writer, but I enjoy writing, so I do. And, if people don't like it then, well, too bad.
Kirill Eremenko: Or you can always record videos, or audio, podcasts. There's just all sorts of media. You can share images, right? You can go on the same Tableau public, and just post your visualizations there.
Kate Strachnyi: Yes, absolutely.
Kirill Eremenko: Okay. So, now let's continue that conversation. You said, before saying to somebody I want to work in data is not good enough. You've got to know what area of data you want to specialize in. Tell us a bit about that. Like, what are some main areas of data jobs that you identify where people can build their professions, and work?
Kate Strachnyi: Sure. I think it helps if you walk through the data science process, and then think about which area you like to work in, or that you're good at, or which one excites you. Which in my case is towards the end of the process, is the data visualization. But, if you're into coding and data cleaning, then you can start looking for specific roles that require those responsibilities. So, I think looking at the data science process, but in terms of data related roles, there are quite a number of them. I mean, there's data engineering, there's net flow data scientists, there's programmers.
Kate Strachnyi: So, I think it's working as a data scientist, you're no longer expected to be the unicorn where you're a master of all trades, and you're coding in every language in your profession in absolutely everything. Because, now it's more common to work as a team in a data science, in an environment that's a data scientist. And so, thinking through the data science process, and then which segment of the team you'd like to be. Once you define that, just focusing your efforts on building that skill set, along with just getting an understanding of the rest of the process, because I do think understanding it is still important. So, you just know how the up and downstream work actually impacts each other. But, you no longer have to be like an expert in everything. So, just picking that niche I think really helps.
Kirill Eremenko: Okay, interesting. Would you say in order to progress in your data science career, like become a senior data scientist, maybe a data science manager, would you say in that case you do need to know the whole spectrum of things? Or can you still just be specialized in one specific area like data cleaning or visualization?
Kate Strachnyi: I think as a manager you will ... One you have to know how to manage people effectively which is a thing in itself. You have to understand the domain industry, the business. I don't think you have to be a master in all those areas, but like I said before, I think you do have to have a very good understanding of all of the other pieces. So, if you've never seen code before, I don't think you could be a data scientist. I think you have to understand what's possible in different programs, and you have to be the leader of the team. So, you don't have to have that understanding.
Kirill Eremenko: Okay, got you. All right. Well, that's a great overview. Before we jump into the technical side of ... Or, more or less technical side of things on this podcast, while we're on the topic of networking, I wanted to say to our listeners that we got some exciting news. Like, Kate you're joining us for this ScienceGO 2019 as a speaker.
Kate Strachnyi: Yes. I'm so excited about that.
Kirill Eremenko: I'm so excited. We really wanted to have you on in the ScienceGO 2018, but obviously you've got small children, and it's a bit difficult right now. But, really hope that I will see you in ScienceGO 2019. Very pumped about that. Do you already know what you're going to be speaking about? I know it's like a year away, but any ideas?
Kate Strachnyi: Probably something related to data visualization, best practices. Or maybe, the fact that being product gnostic is a powerful differentiator at this point. So, not being married to a tool like Tableau or Click, and being able to deliver to the client whatever they need, in whichever format they need it. Or maybe, something about one of the books I'm writing. The possibilities are endless.
Kirill Eremenko: That's awesome. Well, very much looking forward to seeing you there, and actually is it Goclick, you just recently posted a Linkedin about trying ClickView, I think it was for the first time. How did that go for you?
Kate Strachnyi: Yeah. I tried Click, and Power BI. So, as you know, I'm usually using Tableau, and I've become quite proficient in it. But, I decided, I actually didn't decide, it happened when I was asked to do a training for a company for Tableau. And, a few days prior to the training date, to which I agreed to do the training. They said, by the way, the client can no longer use Tableau they're now going to use Click.
Kate Strachnyi: I've never seen Click in my life. So, I had to get up to speed. And, I think that's what makes being a product gnostic powerful. It's just being able to pick something up. And, once you learn one tool, it gets easier to pick up another tool. Just learning where everything is. That was my little wake up call. But, to get my feedback on the actual tools, I think they're all special in their own ways.
Kate Strachnyi: I'm still a lot more comfortable with Tableau, just because I know where everything is. But, one differentiator, I actually spoke to one of the leaders at the Click company, and he said that there's this powerful engine that they have that helps with preparing the data, and blending all the data sources in clicks, and that's something I think that Tableau was working on right now. Maybe they've addressed that with the Tableau prep, which is that new feature that came out, which I haven't had time to play with. But, that was the only big differentiator I saw so far, was that engine that's able to massage the data sources inside of it.
Kirill Eremenko: Okay. Very interesting. Let's talk a bit more about Tableau then. This is a tool that you discovered at Deloitte, how long ago did you discover Tableau?
Kate Strachnyi: About four years ago. Maybe a little more than that.
Kirill Eremenko: And, how long did it take you to get up to speed with Tableau, where you can actually provide insightful visualizations and create business value?
Kate Strachnyi: Funny story about that, when I just joined that CIO program, and they gave me the data, they also put me into Deloitte Technology Nerve Center. So, it's basically this like-
Kirill Eremenko: Nerge or nerve?
Kate Strachnyi: Nerve, like the [inaudible 00:29:12] system. Not with the D, not the nerd. That would have been funnier. Yeah, nerve center. And, they gave me a project which I thought was meant to train me, because they said, "Spend some time in the nerve center and get up to speed." So, they gave me this really cool project and I started working through it. Then I thought I had all the time in the world, because I assumed there was a training data set. Then they told me that, "No, this is actually going to be, whatever you're building will be used for every single onboarding, hiring, training across the United States."
Kirill Eremenko: Wow.
Kate Strachnyi: Oh, shoot. So, I have to take this seriously. So, I had to get up to speed pretty quickly. I think it took me like a week and a half of just learning the ropes, and the formatting, and everything. So, it was really, really easy to pick up, and there are plenty of sources on Tableau's community sites. So, I would say probably week and a half to two weeks to really pick it up. Then maybe a few more months to get on top of all of the very custom formatting options. Like, the color palettes, just getting it to look crisp and pretty.
Kirill Eremenko: Wow, that's what I love about Tableau. For our listeners out there, you heard that right. One to two weeks, that's how long it takes you to get to Tableau, I would say like intermediate level where you can actually build stuff that businesses will use. That's really, really cool tool. Really powerful. Then one more, one or two more months to get to a highly proficient level. That's, incredible. Sometime some tools, I don't know, like let's say, C++ coding and object oriented programming. That'll take you a good three to maybe six months to actually learn, and master, and create apps, and stuff like that.
Kirill Eremenko: With the way technology's progressed, tools like Tableau, and Click, and Power BI, and others in that space. It's all drag and drop. And, you can really harness the power of visualization so quickly. By the way, Kate in terms of visualization, I personally identified two types of visualization. Visualization for data mining, where you grab the data and then you look at it, and used a visualization tool rather than to produce visuals, you use it to extract insights for yourself for better understanding the data, to actually see the data.
Kirill Eremenko: The second type would be the visualization proper, where you create visualizations so that you can share them with people, present them and so on. Would you agree with that differentiation of the two types of visualization or what would your categorization be?
Kate Strachnyi: Yes, I definitely agree. So, I call it ... One is exploration, and the other one is presentation. So, I'm aware I'm exploring the data, or even giving somebody else the ability to explore the data with filters, or parameters. That's easily sage for different members of my team. Instead of like the leaders, they would get the presentation, or view of it where it's published, and they can just print it to pdf, which is what they mostly want to do unfortunately. Because, that's just what they're used to.
Kate Strachnyi: They're like, "Can you put this into Power point? I'm like, "Oh my goodness, that defeats the whole purpose." I mean, you can actually click on things, and drill down, and hover. But, that's the case with, I would say probably a little less than half of the audiences. They want to see a static version of it, which is the presentation. Then some actually explore, and drill down, and see all the details, which is the exploration.
Kirill Eremenko: Is there anything you're doing to address that's statistic. That 40% or 45% of the audience there, they want static visualizations. Is there anything that you're doing, and that people like we can do as a community to help educate people on interactivity? Like in essence, we're talking about self serve analytics here, right? That once you give people interactive visualizations, you empower them to do their own analytics. But, if they then go take a step back, or like 10 steps back, and actually print it out, and just look at the static version, it defeats the purpose. What do you think we can do as a community to help progress that to move the envelope in that space?
Kate Strachnyi: I think it is an education thing. I think people are intimidated by new tools or new whatever, right? So, I think just letting them do it while you're watching or something. Because, once you start opening up a webpage and clicking through stuff, some people believe it or not, they still get intimidated, because they don't know. They click into a Tableau map once and it zooms in. They're like, "Okay, now the data's all broken." They get freaked out, if you can't unzoom, or it definitely is an education demonstration type of thing where you can show them that, you can get to your data quicker and you can actually drill down, and show them those capabilities. I think over time we'll get closer and closer, because in the beginning everybody was against it. So, now the 40% I think is a bit better statistic now.
Kirill Eremenko: Well hopefully that will just better, and better with time. And, probably the next thing I wanted to ask you, what are your thoughts on the future of visualization? Do you think that with time visualization is going to maintain it's a necessity, like people are going to still be using visualization? Or is it going to become more automated and machines are going to be doing it?
Kate Strachnyi: I do think we're moving towards the machines are doing it, but I think it will always be necessary to have some human interaction with it, to do a sanity check or make sure ... You know, the computer can show you whatever the data is showing you, but I think you still need a human and you probably will need for quite some time to know if it makes sense. Especially relating it to the business. But, I agree. I think tools like Tableau might evolve or maybe a new tool will come in where you upload data, and it will just give you a dashboard.
Kate Strachnyi: I think there are some tools that can already get close to that. They can give you a newspaper article based on a data set. I forgot what the name of that tool was, but somebody demoed it for me once. Where you upload your data and then it spits out like a Power point presentation, or a news article, or a pdf of a summary, or built like an annual report for a company based on the financials that you've uploaded. It will just write like, the profits from last border have gone down by 2%, and category XYZ went up. And, they just can give you an SA form of your data analysis, plus the pictures which is getting easier and easier for the computers to build.
Kirill Eremenko: Okay. Well, it would be very interesting. You need obviously ... Well, it kind of even to me it feels like you need a human to double check. Are those all those insights correct? Right? Like, it still feels that a machine would easily make a mistake somewhere there, don't you think?
Kate Strachnyi: Yes, I agree.
Kirill Eremenko: Alright. So, next thing that I would like to ask you Kate, is about your Humans of Data Science. And, more also about your whole podcast, and books, and how you go about these things. So, to start off, as Humans of Data Science, you've interviewed quite a lot of people. And, I think you mentioned it was close to 40 people or so. What are some of the most eccentric things that you've learned from your guests on the show?
Kate Strachnyi: Yes. So, at this point we are I think a bit over 40 interviews, with another 300 or so that are currently on the list that are pending to be interviewed. So, I need to make more of these videos every week. But, every interview that I've had so far has been really special and unique in its own way. I find that in my attempt to get to know the human side of data science, I actually end up learning a lot from these individuals, because they've all come from different backgrounds, different industries, and different countries in some cases. And isn’t in different countries in some cases, and the way they openly share their experiences, and provide advice to the incoming aspiring data scientists, or even the existing data scientists is really inspiring. I think a few that have stood out for me was there was a Faveo last quiz, that I not sure if you had him on your podcast yet. I know you speaking at your conference in the next few weeks.
Kirill Eremenko: Yeah, and his episode was just released like a couple of weeks ago as well.
Kate Strachnyi: Okay. Yes, I have to watch that. The funny thing, there was that we actually had him sing the national anthem for Venezuela where the country that he is originally from. So, I thought that was really fun, and it's always interesting when you get to see the personal side of the people that you're usually interacting with, in one way or another on social media. You might like each other's posts, or comment, or even get to the point where you're messaging. But I think seeing them almost in person, on camera is to getting you that much closer to getting to know each other.
Kirill Eremenko: Yeah, definitely. Faveo is a really, really cool guy. He didn't say anything on the SuperDataScience podcast, but I'll definitely bring it up when we're chatting with him. He sang on your podcast, that's such a cool thing. Okay, and how about in the space of like data science. What is the most memorable comments, experience or tip that you heard from one of your guests?
Kate Strachnyi: Oh my God, they're so many that come to mind. Well, I think the one thing that came across from everyone is that, they're all really, really excited to continue learning in the space. And, that they're super passionate about data, and about learning, and about continuing to advance their skillsets in terms of actual data science content. We try not to get too deep into the technical side of things, not to lose the audience. We give them just enough to tease out some interest in either tool, like software, or algorithm program, or some specific project that they're working on.
Kate Strachnyi: J.T. Kostman I remember had, had a few interesting things to talk about. I think he was talking about AI, and how it impacts our future. They're all just really interesting. If people have time to check them out, definitely do that it's storybydata.com. We have a link there for all the Humans of Data Science videos that were completed.
Kirill Eremenko: Awesome. Also, I would recommend, especially like there's quite a bit of overlap between our shows. SuperDataScience podcast and Humans of Data Science. So, if there's somebody you like on SuperDataScience, then there's a chance you'll find them on Humans of Data Science, and you can actually watch another interview from there, and vice versa. So, yeah, this is a great resource. We should collaborate more Kate, this is awesome.
Kate Strachnyi: Yes.
Kirill Eremenko: Okay. Then the other thing I wanted to ask you is, books, right? You are a brave warrior in my eyes. I've written one book, and I feel like never again, never say never. But, I feel it's such a demanding task. You on the other hand, you've just finished one book which was released recently, a Journey to Data Scientists and the right away, before you know it, before I could even blink Kate is already writing, not one but two books. And, you're writing Disruptors in Data and you're also writing ... May I mention these third book? Because, I don't know if-
Kate Strachnyi: Yes, of course.
Kirill Eremenko: Okay, you're writing Mother of Data Science, is that correct? Is that the tittle?
Kate Strachnyi: Yes, that's exactly it. That's the working title. We might change it, but at this point ... so, the third one you just mentioned, I'm co-authoring with Kristen [Kerr00:04:15].
Kirill Eremenko: Kristen. I still have to have her on the podcast. That's definitely a guest who I'm looking forward to talking to.
Kate Strachnyi: Yes, what you said.
Kirill Eremenko: But, like exciting, right? Tell us how, why, where do you get the inspiration, courage? And, we'll dive a bit more into what each book is about. But, let's start off there, why did you continue to write to, to go onto two more books, after your first one?
Kate Strachnyi: Sure, I think it's hard for me to contain my excitement about the data science space, and I get so many ideas every single day about the potential, what I could do, and write this blog post, and this, and this, and it just ... I happened to just do whatever comes to mind. And, then I ... I was talking to Rico about this. It was, forget the way he called it, but it was like commit yourself first. Oh, Reckless Commitment.
Kirill Eremenko: Reckless Commitment, yeah.
Kate Strachnyi: That's what it was. Yes, you commit yourself first and then you follow through. So, I came to the sense-
Kirill Eremenko: That's his trademark, because that's what his show was on our podcast, and the way I think about it now is the saying, "If you want to take the island, burn the boats." Right? You have to put yourself in a position where there's no turning back, and then you're much more likely to accomplish a goal.
Kate Strachnyi: Absolutely. And, I carry that with me, and all aspects of my life. I mean, I love to share my goals publicly, so when I announced that like, yesterday I think I announced I'm doing a thousand pushups in the month of October, or I'm running the marathon, or here's like I'm running 20 miles. Once I announce it, I feel like I no longer have a choice in the matter and it must get done.
Kate Strachnyi: That brings me to the books. When I get excited about a topic, I always tried to make it public in order to give myself that sense of commitment. Then I end up scrambling for time to follow through. But, the good thing with books is you ... Especially if you're self published, like I do, you set your own deadlines and you can move things a week or two if you had to. So, it's a bit easier to manage.
Kate Strachnyi: But, to answer your question, why am I doing it? I just, I love getting to know people. And, I feel like the people I get to know, and I have these conversations, I want to share it with other people, because there's just so much value and insights that I learned, that I just love sharing it with the rest of the world. And, a book is just a great way to do that.
Kirill Eremenko: Sharing is caring.
Kate Strachnyi: Yes.
Kirill Eremenko: You are definitely caring for the people that are also aspiring to learn data science just like you. Speaking of books, I wanted to ask you, sometimes when I read a book I try to take away as much as possible. But, given the amount of books out there and modern information, sometimes I've, even just looking back and trying to remember a book that I read. I try to even remember, okay, what's the one biggest takeaway I got from that book? And, if I can remember that, that's a radio success. If, I can remember more that's even better.
Kirill Eremenko: And, the case of your three books, would you mind sharing with us, what would you say for each one of the three books would be the biggest takeaway that a person can learn, and remember for the rest of their lives? If there was one takeaway for each one of your books? What would you want it to be?
Kate Strachnyi: Sure. Starting with the one that's already published, Journey to Data Scientist. The takeaway there is ... So, I interviewed over 20 people, and asked them one question that I had in common for all the individuals. Was, how did you get started in this space? And, basically everyone came from very different backgrounds, and had very different journeys into this data science role, but they all share a few things in common. Like, they're really curious about the field, and it just takes this inquisitiveness that I can drive their whole career.
Kate Strachnyi: But, the big takeaway there is, it doesn't matter where you are now, you can still get to where you want to be and there's no clear cut path, because as the book highlights, everyone has taken a different path. Some have started as working at a retail store or just, some have actually gone down the ... I guess the more direct path of getting their PHD in this space, and then going to work as a data scientist. But, that's really the takeaway is that, you can do it from whichever point you start, you just have to put in the work.
Kirill Eremenko: Awesome. Okay, so that's Journey to Data Scientist, by Kate Strachnyi. What's the takeaway from Disruptors?
Kate Strachnyi: So, the disruptors is really highlighting the data science leaders, and it attempts to show the world, the power of data. And, there is a big underlying discussion about the data ethics and data science ethics, or just how data is used across different industries, and how it impacts people. And, the fact that algorithms might ignore or the individual as it probably should not, because it really impacts individuals and just being cautious with how your data's used. In educating the common public about how their data is used and all of that. All this stuff that goes with it. There are definitely a lot more stories in there, but I think that's just one common thread that pulls through all the 10 conversations that I've had for that book.
Kirill Eremenko: I can see. Very interesting, and from what I read, because I read the sample chapters for your book. I felt submerged when I was reading it, but I think it's very well structured in terms of how you put your narration with the comments. So, there's a little shameless plug for your book, anybody considering learning from some top data science in the world, this is a great place to start.
Kate Strachnyi: Thank you.
Kirill Eremenko: No, thank you. And, the third book, which is yet to come, Mothers of Data Science. What inspired you to do that, and what would it be that one takeaway from there?
Kate Strachnyi: Okay. What inspired me, this actually came about probably a week and a half ago that I decided to write this book, and I pinged Kristin on Facebook and I said, "Hey, do you want to write a book with me? Mothers of Data Scientists." She was like, "Sure, yeah, let's do it."
Kirill Eremenko: And, Kristin also a mother?
Kate Strachnyi: Kristin is a mother, yes. She also has two kids. They are similar in age to my kids. I think a bit younger. Yeah, so we decided that we're going to write this book to inspire women to get into this field. The reason we went with mothers is just there's this added level of complexity. I think motherhood brings to pretty much any profession or anything you want to do ever, because you have to factor in those extra steps of the responsibilities of motherhood.
Kate Strachnyi: The message for that book is that, a woman should step forward into this career. It's a great career. I benefited greatly from the flexibility that it has provided me. I think we spoke about this earlier in the podcast where, when I decided that I needed to work from home, that's actually when I started working with data, and that's what enabled me to continue with my flexible work style till now.
Kirill Eremenko: Yup, and very inspiring. I think this'll be a very inspiring book for, as you said, not just mothers, but women in general in the space of data. And, that's what we need to enhance this diversity situation, to improve the diversity situation. Remove that gap for more people to be able to get into this amazing profession just like you did. Very, very inspiring. What are the timelines on your books? I know you already self published the first one, what are the second and third ones coming on?
Kate Strachnyi: The second one should come out probably next month. There's a ... I'm working with an editor right now that's going to take a final review, once I've received approval for each chapter from the data science leaders. So, it's getting very, very close and I'm at the point where I can't wait to publish it, but at the same time I want to make it perfect, like I don't want to publish it. So, it's that final race to the finish line.
Kate Strachnyi: This year, and surprisingly the third book is also slated to come out sometime this year. Kristin and I are pushing full speed ahead. We're interviewing amazing mothers just this week. So, starting tomorrow, I think we have four or five interviews in one day. Yes, we cannot wait. We're both really excited.
Kirill Eremenko: That's so cool. So, some people can get these books for their colleagues, and friends as Christmas presents, then.
Kate Strachnyi: That's the plan. That's actually what we spoke about.
Kirill Eremenko: Oh, awesome. That's so cool. And, even if somebody is listening to this after Christmas, you can get it as a new year's resolution. That's also good. Good gift.
Kate Strachnyi: Or, Mother's Day present.
Kirill Eremenko: Yeah, that's the best. It's the best one, awesome. Okay. Well, thanks. Thanks so much Kate. We're slowly getting slowly got into the end of the podcast, it's been a great, great fun chat. Before we wrap up, can you tell us where our listeners can get in touch with you, and follow you, and get more insights about your career, and amazing podcast, and books, and other things that you get into?
Kate Strachnyi: Sure. Well, I'm ... It's pretty much easier to contact me on Linkedin. I'm pretty active there, but I'm happy to share my email which is, [email protected] The website or blog that I'm going to spend a lot more time writing on is also called storybydata.com. And, on twitter you can find me at, StorybyData. So, pretty much all things StorybyData is where you can find me.
Kirill Eremenko: Awesome. Thank you so much, and I would encourage anybody listening if you have an interesting story of how you got into data science and how your career has been progressing, reach out to Kate, and yes, she's got a list of 300 people. She's yet to interview, but why not give it a shot, and maybe you will be featured on her podcast or maybe her seventh book. Once you get there. Awesome, awesome.
Kirill Eremenko: Okay, well thanks so much Kate. Very, very exciting times. We will share all the links in the show notes, and I personally can't wait, and very much look forward to meeting you finally in person at a DataScienceGO in 2019, next year.
Kate Strachnyi: Yes, I'm really looking forward to that as well. And, thank you for having me on the show.
Kirill Eremenko: Awesome. Well, have a good day. Bye.
Kate Strachnyi: Thanks, bye.
Kirill Eremenko: So, there you have it ladies and gentlemen, that was Kate Strachnyi, data science influencer, and author. I hope you enjoyed this episode as much as I did. And, to recap, I'm going to list the books that Kate has published or is working on currently. So, you can look out for them. The one that's currently already published is called, Journey to Data Scientists and you can already pick it up on Amazon. And, the two books that she's working on at the moment are the Disruptors Data Science Leaders, and Mothers of Data Science.
Kirill Eremenko: Another thing that Kate asked me to mention is that, she has a Tableau course on Udemy. So, if you'd like to check that out, it's called Tableau Visual Best Practices from Good to Great. I will include a link in the show notes. So, if you are a fan of Kate's work, and if you would like to learn from her directly, then you can check out that course. Tableau Visual Best Practice from Good to Great, on Udemy. Also look out for more content coming from Kate. Make sure to follow her on Linkedin, and in other social media, and see what she will be up to in the near future.
Kirill Eremenko: You can get all the show notes with the links, with the items discussed on the show. Plus the transcript for this episode at www,superdatascience.com/205. That's superdatascience.com/205, so make sure to go there and click on Kate's Linkedin, and follow all of her so you can stay up to date. On that note, thank you so much for being here today. I really appreciate you spending this hour us, and I look forward to seeing you back here next time. Until then, happy analyzing.