Unexpected Surprises in Analytical Projects w/ Piyanka Jain at Aryng (Episode 48) #DataTalk

Unexpected Surprises in Analytical Projects w/ Piyanka Jain at Aryng (Episode 48) #DataTalk


– Talk, a show where we
talk to data science leaders from around the world. We’re super excited to
talk with Piyanka Jain. She’s the COE and president of Aryng, which is a management consulting company focused on analytics for business impact. Some of her clients
include IBM, Google, SAP, Apple, and a lot of other
top data and tech companies. Today, we’re talking
about unexpected surprises in analytical projects, and
we are just super excited and blessed to have
Piyanka as our guest today. Piyanka, how are you doing? – I’m good, and I’m
pleased, and it’s an honor to be here with you, and
I’m looking forward to this. It’s really fun. – That is gonna be a great conversation, and this is sponsored by
NyQuil, because I’ve been sick all week, and you can
probably hear it in my voice. (Piyanka laughs) I apologize for any crackling you hear. But for those that are listening
to the podcast, you gotta make sure and check out
Piyanka’s business website. Again, the company’s name is Aryng, which is spelled A-R-Y-N-G. So make sure to Google
that after today’s episode. So, Piyanka, we always
like to start these shows getting to know you and
kind of your journey into being a scientist, in school. Interested to know, like, where do you, where did that drive come from? What was your journey like? – Yeah, happy to share. By the way, you were tuning out for me, so I don’t know if it’s everybody else is also experiencing, but we’ll roll. I think I got the question that basically, how did I start out? – Okay, cool. Yes. – So, yeah, you know, my
background is in quant and math. I’m more of a math person I
think, but it turned out to be that I happened to, two of my master’s, both the theses involved
AI and applied statistics, and I was, I’m always,
I was always interested in solving big problems,
so with my first master’s, I was solving a problem
of radioactive spillage, and the second problem, I
was solving some problem with artificial intelligence
in a routing table. So, it was good, fun problem,
but I didn’t, at that point, I’m completely going to date myself, but at that point, there
wasn’t necessarily a thing called analytics, but it
was always interesting. Math was always interesting for me, and quantitative methods,
and solving big problems. And after I graduated, I was
working as a software engineer, you know, developer,
coder, and so on, so forth, but at some point, I
realized that’s not me, and then I founded a
company on the other end of the spectrum in advertising. In between, I was working
for Google, pre-IPO. – Wow. – So all sorts of things, and
not really finding myself, you know, not really, like, me, and then, I was just
thinking about my next gig after, after my company
Out of the Box Media, and I was thinking about my next gig, and I saw this job. I was just browsing, and
I saw this job description for a senior analyst
at Adobe on Craigslist, and I looked at the job,
and I was like, this is me! (Mike laughs) And so, I, I made my resume
in the kind of format that I teach people how to do it now, as I’ve helped people transition, ’cause I had to sort
of transition as well. I mean, it wasn’t a, I wasn’t
an analyst to begin with, and I became an analyst. And I went from my job,
and I was hired on spot. I was, like, I was given an
offer I couldn’t believe. – Oh, really? – And I was like, whoa, I
am, truly, I was on fire. I really, like, people
could see the passion I had, and, you know, what it is, but,
you know that getting a job and then solving problems
is two different things. So my quantitative methods
didn’t really prepare me for the reality of, reality of solving, you know, big problems with data, because, you know, academics, simulation, you can run a hundred simulations. This is very different then when you come to the real world of analytics. So I had to learn a lot
through this whole process of, you know, how do you
actually begin to drive impact using data, using logic,
using, you know, simple math, using more complex
models, whatever it takes? How do you begin to drive impact? So that’s kind of my
journey, and so excited that I found my passion. – Yeah, that is so
cool, especially hearing about your different paths that you took and you’re like, I’m not happy
here, I’m not happy here, and then stumbling upon, of
all things, Craigslist listing. (Piyanka laughs) That is so funny. That is so awesome. And so when you read that job description, you were like, this is it. This is, it’s exactly what
I’m looking for, but you were like, I don’t necessarily
have like, the resume for it? What was that? – Yeah, I didn’t have the resume for it, and I made the resume
exactly like I would teach. I have a book called Acing Your
Analytics Career Transition for your readers, if they’re
interested, and in that book, I have an entire, I
have a five step method of how do you translation,
and then that’s exactly what I did, pretty much,
and one of the chapters is on that eight-second resume. How do you make an eight-second resume? – Oh. – And at that point, I
didn’t know what I was doing. I just intuitively made
my eight-second resume, because I had to pretty much
show that I had what it took for that job, although I didn’t, like, if you looked at me, what I was doing, like as you said, I was
going here and then here and then here.
– Yeah, yeah. – I didn’t quite have it, but I had it. And so I had to tell that story in a, in the most fluent and graceful manner, and so I developed this kind of format. I call our RFQ format, but,
you know, your listeners and audience can go in
and look at this book. It’s on Amazon, Acing Your
Analytics Career Transition, so. That helped me; it can help you. – Awesome. We’ll make sure to put a link
to that book on our website, and for those listening in,
the URL’s ex.pn/datatalk56, and that’s the URL that will
provide a link to that book. It sounds fascinating,
especially for those of us who have been all over the
place in different careers, and you’re trying to make a transition. ‘Cause sometimes I think,
Piyanka, recruiters might look at the resume
and go like, well, this person’s all over the place. I’m not even gonna talk to them. – Exactly. (Mike laughs) – But you’ve somehow,
you’ve somehow managed to be able to paint a nice picture to show that you would be a nice fit. – Yeah, I think what happens
for most of us is that we tend to identify ourselves
with where we’ve been, right. We have the momentum. We’ve been doing this,
we’ve been doing that, and when we have been doing this and that, we have an incoherent
story, and incoherent story really is a put-off, because it basically, like, eight second,
recruiters and hiring managers take about eight seconds
or less to read your resume and say ah, no pile, because
you’re just all over the place. So, the idea is to tell the
story about where you wanna go, where you wanna be, while
collecting relevant information from your background to show that you have demonstrated
experience doing it. So it’s not really rocket science, and there’s no dishonesty. You’re not like, doing
anything crazy here, but it’s just about
painting a coherent story of where you wanna be, what
you’re passionate about, what inspires you, and kind
of, you know, going from there. – That’s beautiful. I’m gonna check out your book. (Mike laughs) I’m definitely gonna
check it out and buy it. That sounds fascinating. I love your approach to telling a story of passion for your career. So what then led you from your dream job, where you’re like, yes, I love this, to then starting Aryng, your
own company, on analytics? – That was a journey, too, right? So I was at Adobe for
three and a half years. I learned a lot about analytics
as applied to advertising, marketing, marketing
operations, somewhat product, but I basically like the
whole customer experience, and I really, really learned a lot. I had some really great, amazing mentors and people around me, and
also, like, great clients, internal clients who
challenged and said, you know, why should I accept this and so on. So I had kind of started
developing sort of, like, when I joined it, I
was just, I think I was, I just had, I was doing,
I had one product, and I was reporting back on one product. By the time I left, the
entire like, Friday, 3:30 p.m. was my email was going
out for the entire set of relationship marketing campaigns, because I was able to kind
of synthesize a lot of data into meaningful stuff for folks. I think that was what it was. And I was effective in
solving problems using, using analytics. So, but I was still learning,
and then I joined PayPal, and I learned a lot about
analytics as applied to product, to fraud, to, you know, the
entire customer segments, customer operations, CS,
and so, kind of became a little bit more complete
in my understanding of how do you apply analytics
in all these different places. And meanwhile, I was
speaking at conferences, like I was keynoting
Predictive Analytics World and many other, like,
business conferences, and I was just seeing that the gap where people taught
analytics and data science is just, it’s rocket
science, and they were, they were over-solving it and
jumping too wide, too far, and really, like, it’s not rocket science. And so that’s where I
started thinking about, how do I make people,
how do I empower people with this know-how of how to
make decisions using data? It’s really not rocket
science, and it can be taught. And I started kind of
putting all these frameworks that I had in my head on piece of paper. I started reading, and I
started thinking about, how do I empower people? And so, out of that came
my framework called, it’s an intuitive, very
intuitive, most people who are doing analytics would be
following something like that. It’s called BADIR, which is a five-step, it’s an acronym for these five
steps: business questions, analysis goal, laying out
hypothesis, doing analysis plan, sorry, data collection,
insights, and recommendations. So it’s a five-step method. That’s then my first book,
Behind Every Good Decision. It’s also on Amazon. It’s published by Amicom,
and your local bookstore. So Behind Every Good Decision
is the name of the book. And so, BADIR is the framework
that is, that is what I take now to my clients, my
corporate consulting clients, that we solve big problems
in an accelerated manner because we have that, and
the whole beauty of BADIR is that it’s just not about data science. It’s also about decision
science, because at the end of the day, it’s not, you
and I are making decisions. Machines are not making decisions, right? And so, it’s about how,
it’s about understanding, who is it that needs this
information from the data, and what actions they are ready to take, and what do they need by when? What are their constraints? What are the constraints of the data and the environment we are in? Putting it all together and
then driving towards actionable insights, which can be like,
you know, so, this, this framework basically incorporates
the entire data science, decision science, and it’s
very sound algorithmically as well as it’s very sound
in the decision science. If you follow that framework,
or some such framework, you are going to make sure that the analytics you
do will be effective. Like, it’ll, it’s not gonna sit on shelf, because I speak at, speaking
at Predictive Analytics World and other such, you know,
data science conferences, the biggest problem that data
scientists will tell you today is that, I do really good
work, and nobody cares. Like, my biggest recommendation, this is the greatest
model I’ve built, like, best accuracy, and it
sits on shelf somewhere. What happened? And what happened was
that you miss the shot. You missed the whole decision
science aspect of it. You didn’t engage with the
stakeholders at the right time. You just thought that, oh,
I’ll build the best model, and then I’ll try to push
it down somebody’s throat. It doesn’t work that way. (Mike laughs) Right? And I’ve done it, I’ve
done it, I’ve done it, and that’s why I’ve learned, right? Like I was, when I started
at Adobe, I wanted to solve every problem with the most
complex algorithm I could find, because that was my training. I mean, I learned non-linear regression before I learned linear regression, right? So this is our training. But that doesn’t work very well, because if people don’t understand you, and you don’t understand them,
they’re not gonna take action on whatever insights you have, right? So, anyways, long story
short, BADIR is the framework that I would encourage anybody who’s looking to do analytics. If they’re a data scientist and
they’re not being effective, find, get a hold of
this book and get a hold of that framework, and,
you know, start practicing the decision science aspect of it, and if you’re looking
to get into analytics, make sure that you are learning
analytics in a structured, structured manner, so
you can drive impact. – Yeah, that is, that
framework sounds awesome, asking the right questions,
helping to guide the person along, and also to get leadership
and stakeholders involved. Can you talk a little bit
about, ’cause you said that, and I thought this was really interesting, that the, so many data
scientists are coming to you and saying, I’m solving these problems, I’m coming up with these solutions, but then I’m not getting
it across to leadership, and it just sits there. So, can you speak to that data scientist who’s in that position right now? – Yeah. – They’re discovering some
really fascinating insights. – Yeah. – They’re sharing it as best
they can with leadership, but it’s not being interpreted correctly, and it’s just sitting there. What advice would you have
for that data scientist? – Yeah, the short advice I have is, follow the BADIR framework. So, frame. So, the first thing is
influence starts early. You think influence starts
after you have some really amazing, ruby-like insights,
and then you’re thinking, oh, I gotta go in and do the
stage performance, voila, and people will get excited. It doesn’t work that
way in the real world, because everybody has their own context. The person who you’re presenting
to has their own problem set and their own context,
and you need to kind of have an understanding of their
context, their problem set. If you’re, you know,
you’re presenting to the, if you have CMO as your
client, the CMO is responsible to CEO for some, some metrics or some, he’s held accountable for
something; what is that? What are those things
that he’s responsible for? What it is that he’s
thinking day in, day out? What is keeping him up at night? You need to understand those
thing before you think about, oh, I can, I have a beautiful solution, but a beautiful solution to what? What if you have, you
know, so it’s understanding what problem is it that
you’re trying to solve. And so that means the biggest part, the front part of BADIR,
business question and laying out the hypothesis and plan,
that’s the, that’s the, that’s the most important
part of framing the problem, figuring out who’s wanting what answers, is it actionable, who are
the critical stakeholders, who’s gonna sponsor it, who’s gonna take, you know, take action on it? All of that needs to be
figured out, and also, what hypothesis do they have? Because hypotheses is the fastest ways. I call it the detective route. If you wanna find, you know,
treasure in Pacific Ocean, there are two approaches. You can go start swimming
and say, oh, wow, beautiful, nice water, whatever else, and swim for a long time
before you’re gonna find some treasures, or you can
say, where are the most, best possible sites or
highest probability sites where the ship must have
sunk, and all of those things. Find your best spots and then
go down diving into it, right? That’s the second, second
approach is the detective route, and this approach that most
data scientists need to have is first, understand the problem. What is it, which treasure is it? Are you looking for these shiny whales? Are you looking for this
algae which solves cancer? Are you looking for these rubies which got sunk in a 1952 wreck, right? Like, what are you looking for? Let’s find that first,
figure that out first, and then lay out your hypotheses. Where are the highest probability areas where you can find a solution, and hypothesis comes from stakeholders, the same person who has
asked you the problem, and everybody else in the room,
let’s put our heads together and figure out, where are the places where we can get the best solutions? So hypotheses you’ve been planning. And then let all of us,
let’s all of us come together and agree to a plan. This is how I’m gonna look at my metrics. This is how I’m gonna prove
or disprove my hypothesis. All of that needs to come together. Once you have a solid plan,
you’re most likely to succeed. And then of course,
there’s the whole aspect of touching base, making
sure early insights, and presenting right, right? So all of those things
need to happen right for you to be effective
as a data scientist. – I love that answer, Piyanka,
and I think that it’ll be very, very helpful, especially
for the young data scientist, ’cause I think that that is
something that is sometimes only learned once you’re in the business. – Yes. – Because you’re doing all this research. You’re doing all this analysis. You’re sitting, you know, at
your desk with your computer, finding all these insights,
but then you realize, well, there’s all this other
stuff that has to happen along the way to get recognized, to get buy-in, to actually
move forward, right? So there’s all this like, pre-homework that has to be done to make something actually actionable and
have success with it. Can you talk a little bit
about, you know, today’s topic is all about unexpected
surprises in analytics, and I’m wondering if you
can maybe share some, some case studies or some times
where you’ve actually worked on some projects, coming in
with a hypothesis, and all of the sudden, the data’s
telling you a different story? – Yeah. Yeah, it happens a lot. In fact, the more it happens,
the highest is your impact, because then it’s given that
the business was working this way, and, you know,
the hypothesis had come in, but what you found was
completely this way, and that means the business
has to shift significantly, and that means there’s the
biggest opportunity there. So, it’s actually a great
blessing to get these amazing, you know, like 180 degree turns. And I can think of many
cases, but one example that comes to my mind is, I was, this is, this is some time back. I was within PayPal, and, and, I was contacted by the head
of, so basically, this is like a CEO-level project which
basically said, our customer, our c-sat, customer
satisfaction is going down, and we don’t know what’s going on, and we are really looking
really hard at these metrics. Their average speed of answer,
ASA, average hold time, these are the standard metrics
for any customer service. How quickly are you answering the phone? How much is the hold time? We wanted to make sure
that the customer service, but the c-sats were still going down. So what’s going on, and I happened to, I was looked at at that
point as a SWAT team, so anywhere there’s a problem
that cannot be solved, I would be parachuted in with my team, and they’re saying, solve this. – Give it it Piyanka, give it to Piyanka. (Mike laughs) – Right? And it could be a good
thing, but it was also always a bad things. I was within crossfires of
really high-power discussion, and you’re like, what am I doing here? Somebody get me out of here. But anyways, so, but I learned a lot. So I was, I went to Omaha operations team, which was based out of Omaha, Nebraska, and I tried to understand, like, again, using the BADIR, BADIR
framework which was in my head at that point, but basically,
figuring out really what the question is. What’s going on really in the field? Talk to agents who are
picking up the phone. I talked to them and sad,
okay, why do you think customer is unsatisfied,
and so on, and so forth? Based on that, we generated hypotheses. We put down in our analysis. We collected all the
data, the relevant data. So another thing about BADIR
framework is it basically helps you think through the problem so you’re not boiling the ocean. You’re just looking at
the relevant data sets. So we looked at really, and
that accelerates your analysis, because you’re, you know,
more number of data sets you analyze, the more
time it’s going to take, and, you know, all of that,
signal to noise ratio, versus if you’re just
looking at a small data set based on the hypotheses. So that’s what we did,
and we looked at the data coming from the surveys and all that. And what we found was
ASA and AHD and those two or three metrics, operational metrics, were not even correlated to c-sat. What was correlated to
c-sat was whether they’re, which we then found the
term called FSR, first call resolution, and how many
times a customer had to call, and whether the customer
perceived the agent as friendly or not, right? Like, this was not expected at all. What they were expected was,
so what had happened was, initially, when the average hold time, average speed to answer, AHA and AST, when they used to be rather
long, they were key metrics that were driving customer satisfaction. But when that became, that
those were under control and people were, it became table stakes. If I call customer operations,
I’m expecting my call to be answered within so much time. – Yeah. – When those metrics kind
of came under, like a, within a tolerable amount and they were, then the other metrics became important. The business had moved, but people, people’s dashboard had not. So they were still in their dashboard. The green and red was
still showing ASH and AST, whereas c-sat was going down. And once we found that,
the agents were trained, the metrics were changed, FCR and whether the customer
had to call again. All of those things changed,
and that was a big hullabaloo about like, we have to change
our ways of looking at things, and I was like, this is
what we are, you know, because of following BADIR
and because of involving the stakeholders early on, those were involved early on, they knew what we were doing. We were not moving forward. I did not move forward until
they say your plan looks good. I was not moving anywhere. I’m like, okay, do you guys agree that this is how we’re gonna approach it? Because this is the conversation
I’m having with these high-value stakeholders early
on, before I’ve even had, have any insights, right? And so they’re saying,
yeah, this is looking good. They felt heard. When the insights were ready,
they were ready to act on it, because they came along
with us on that journey. So it was a very, very successful project. Things completely changed, and c-sat improved from there on, so. Big surprises, but to
date, those are the metrics that are still being used by
not only just CS for PayPal but for many other companies. – Wow. I just love how you,
from the very beginning, were bringing them along
with you on the journey, sharing with them your thoughts, getting buy-in, so that they
were with you on this process, so that when you did share
this surprising data, they were, like, surprised
with you, but like, oh, we can act on this. – Yes. – And it wasn’t self-righteous. – And they had confidence on it. I mean, the biggest thing is that people, the only reason people
don’t accept your answer is when they don’t have confidence in it. But because of this
process that we followed, and I mean, of course,
one thing was that I had built up significant credibility
within the organization, but the other thing was I
was following this process where they were coming along with me. They bought the process. They bought, like, yeah, this
sounds like a sound approach. And they realized that how focused we were to solving that c-sat. So we were all on the same team. We are gonna move that
c-sat, and so that’s kind of, you know, when you’re driving,
when you have big border, and you’re all pushing from one direction, you’re more likely to move
it, whereas, you know, people pushing it from
different directions. – Piyanka, have you
ever worked on a project where, when you, you know,
you were sharing, you know, going through your process,
bringing them along with you, and then the insights you
shared were just not believed? Like, the leader was like, well, this doesn’t make sense to me? Have you ever encountered that? – Yeah, very, very early on. Very, very early on, before
I’d understood the process, and if you, your audience,
if any of you data scientists out there, if you’re experiencing
it, that just tells you, you are not engaging the
stakeholders early on. The only way your stakeholders, your stakeholders want answer, and they wanted answer yesterday. So they are really and primed
to look for answers from you. So, if they’re not buying your answers, fundamentally, what’s
going on is you are not finding alignment with them, and if you’re not finding
alignment with them, that means your early part
of B&A, business question, analysis plan, all of that
analysis plan needs to be locked and loaded and agreed
upon by everybody and saying, yeah, this is how we’re gonna look at it. If you do that, you’re
not going to face that, face that at all. Sometimes, what happens is, you know, you start with three
stakeholders, and then, you know, the VP of marketing left,
another marketing came, and this is a three-month-old project. Another person came in, so
they joined a little late. You, you as a data scientist
or a, on the analytic side, you have to make sure that
the new person who’s coming in also comes along with you, and
that they get an opportunity to provide input, because
unless they feel heard, they will not hear you. It’s as simple as that. So they need to feel heard. All the stakeholders need to come along, and they will, there often is a situation where the stakeholders get added later on. Make sure to have a
separate meeting with them. Bring them along. Take their feedback. Incorporate it, so that,
you know, when you, when you present, people
are ready and primed. If you, if you haven’t heard them, they’re not gonna hear you. (Piyanka laughs) It’s as simple as that. So you need to make sure
that you’ve heard people, you have presented with
them, after hearing them, hearing all of them, you’ve
presented a cohesive plan, and everybody agrees with
that, with that plan. – For those listening to
the podcast, if you’d like to learn more about Piyanka’s
books, this framework, you can always go and go
to her website at Aryng, which is spelled A-R-Y-N-G,
and just Google that, or you can go to the Experian
blog at ex.pn/datatalk56 where we’ll have links to
her books and the framework and her website. Before we go, Piyanka, I’m
just loving all these stories that you’re sharing about
how to approach analytics, and I think this is like,
super helpful, especially for the person who’s just
starting out in data science, or the person who’s just
struggling with getting heard, because that can be
very, very frustrating. – Yeah. – Where you work so hard, right? The frustration.
– Yeah. – You just wanna leave that company, because you’re not being listened to. – Yeah, yeah. Unfortunately, you take
yourself along with you. (both laughing) You leave the company, but
you’re taking yourself along with you, and you have the
same situation all over again. So, so, the problem doesn’t lie outside. The problem, changing your way. I wanted to say one last
thing to the, to the folks who are looking to transition
their career to analytics. You know, the data scientist,
machine learning, AI, it’s just such a glitzy world out there, and everybody wants to be getting on it. It’s not for everybody. If it is your passion,
please, please find right ways to get into it, but
it’s not for everybody. So, go take an aptitude test. We have one on aryng.com. There’s an analytics aptitude test. It’s a pretty cohesive, simple test. We can send the link to
you as well, Michael. But go get a test. Make sure this is you. If you take the test on aryng.com, you will, A-R-Y-N-G,
aryng.com, you will actually hear back from us and we’ll
tell you whether you’re, this is right for you or not. Make sure you understand
that this is for you, and once you understand this is for you, you love all these puzzles
and you like solving, seeing patterns and
solving complex problems, you’re a problem solver,
then make sure to, again, another thing
which happens to people who are looking to
transition is the tools. They go, oh, I should learn
Tableau and Python and R, and it’s just, it’s not about the tool. It’s about problem solving. It’s understanding the
framework of how to solve it. And so find, find a training,
find ways in which you can understand or learn analytics
as applied to business. It’s different than
academic, you know, academic, I come from academia, so
it’s different from academia. And it’s different from, almost always different
from statistics as well. I mean, it is, you apply statistics, but like, really, learn
analytics as applied to solving problem and do a real project, not like you being able
to optimize ranking. Yes, you can, you have optimized
the data science aspect of it, and maybe you have
understood feature optimization, but that’s, that’s not gonna
get you into analytics. So if you can’t have enough,
I mean, we have such program. We have an entire program
and career transition. But if you find such
program where basically, applied analytics, real-life example, real-time client work,
that is where you’ll say, oh, this is me, I want to do this, versus, oh, my God,
this is so overwhelming. This whole predictive
analytics thing is killing me. It’s better to find out now. – That’s right, that’s right. – Than going in later
and saying, oh, my God, I’m so unsuccessful, I don’t like this. I spent three years taking
all sorts of courses on Coursera, and I don’t like this. You don’t want to end up in that position, so find out if it’s right
for you, take the test, make sure to choose the right program, and once you start doing
it hands-on, you know whether this is for you or not. – Great advice. For those listening to the podcast, again, the website is A-R-Y-N-G.com. That’s Piyanka’s business
website where you can take the test, learn more about how
to become a data scientist. And one last question,
Piyanka, before you go. You know, one of the
common questions we get in our data science
community is from people who are, you know, they’re
excited about, you know, they read all about data science and AI in the news all the time, and they’re always asking,
how do I get started? Where do I start? And for those that are taking your quiz and realize that yes, I
like to solve problems, I feel like I’d be a good fit, what would you say is
a good place to start to kind of, kind of get the wheels going, to help them start their
careers in data science? – Well, a great place to start with us. If you score well in that test, I will be your direct mentor. So, yeah, I have limited
time with the number of people I can spend and directly mentor, but if you score well, you
will be assigned to me, and I will walk you through this process. We’ll teach you business
analytics, predictive analytics, A/B testing, all with problems, like just examples I’m giving you. Same from all of our clients. We give you examples after
examples and exercises and capstone cases, and then
at the end of the training, you actually work on a client
project, real, real client, one of our clients, and
project on that, and, and by the time you’re done
with that, you’re pretty solid. Then when you go to an interview, and we of course prepare
you for the interview. We have that Acing Your
Analytics Career Transition, resume building, targeting
your job and all of that, but when you come and sit in
front of the hiring manager, you know how to solve
a problem using data, ’cause you’ve done it already. And many times in the class, then when the real
project with your client. So, you know, I would say, come join us. We’ll take care of you. And I would love to be your mentor. I love this transition from people taking, taking people from A to
Z, where they come in and they don’t know much about analytics. They are unsure about
themselves, and by the time I get my call from folks
saying, I just got hired! I got this job! I’m like, it’s so exhilarating. I can’t tell, you Mike. It’s like, most satisfying for me. I mean, I do consulting,
analytics consulting is our main thing, but like,
this, this individual stuff that I still mentor
people, I really enjoy it. I help people. I love doing this empowering of folks and getting them to their
dream jobs, you know? – And that is awesome. Piyanka, it’s been awesome
having you as our guest. You can tell just from
her passion and enthusiasm for data science, not only as an analyst, as a brilliant data scientist,
but also as a mentor, someone who likes to empower people. So if you are someone who’s
interested in getting involved in the data science field,
make sure to reach out to her. Go to the website, aryng.com. Again, A-R-Y-N-G.com. Check out the resources there. Take the quiz, and who knows? You might end up being mentored by Piyanka, which would be amazing. And it’s just so cool to see, Piyanka, how you’re giving back to
the data science community, because of your passion
and love for data science and just helping people
not make the mistakes that you made early on, and being able to succeed
as a data scientist. So, thank you for all your contributions to the data scientist community. Thank you for inspiring so many people, just in today’s chat,
and for those listening, again, follow her on LinkedIn. Connect with her, and also
connect with her business. And again, if you’d like
to go learn more about her, you can go to the Experian
blog where we’ll have a full transcription of
today’s episode and the video, et cetera, and the URL is
simply ex.pn/datatalk56. Piyanka, thank you so much for your time. – Thank you. – And have a wonderful week. – You too.

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