Transcripts
1. Introduction to Top Tier Tech Resumés: Transitioning your
career is scary. It was the scariest thing
I've done in recent time. And honestly, I could've
used a lot of guidance. That's why I'm here. My name is yesterday. I
am a data professional. I work as a scientist
for machine learning, have experience as a
Machine Learning Engineer. And I teach data
science on Skillshare. I have taught data science in Fortune 500
companies like Shell, the UK government,
and in universities. I now work with machine
learning and data every day. And this transition
out of oil and gas into something with data
has made me very happy. I know that a lot of people are looking for jobs and tech. And I want to make this possible by taking your
experience that you already have as an applied scientist or as someone that
worked in academia. And translate that
into something that will get your jobs
in data professions. Whether it's data
engineering, data analytics, data science,
business analytics, or all the other jobs. I want you to go away
from this class and have a CV or resume and
get a job with that.
2. Class Project: Welcome to class, welcome
to the other side. In this class, we'll have
a look at how we can translate your
specific experience that is outside of data science, outside of the data professions. And really get that
into something that will hopefully
get you a job in tech. For this class project. I found it a little
bit difficult because I'm aware that you are translating your personal
experience into something data. And I think this can be a
bit uncomfortable to share. A little bit too much to
share sometimes. Yeah. But I want you to do a project. I want you to
finish this course. And also, if you do a project, this means this gets shared with more students and
recommend it to others. If you do a project, essentially what I
want you to do is take one example
of an experience that you have and use the methods that I
teach in this class. What we'll go through a
specific way to write bullet points for your CV and translated for
data professionals. And take what you had before, how you kind of describe
your job before. And then share
with the class how you describe it after
just one bullet point, put it in a little
picture so you can upload it to our class Products
section over here. That would be amazing. Then also others can get
some inspiration of what you did and how you
translated your experience. If you want feedback on
your class projects, you can write that
in the comments, then other students
can have a look. If they find some other
way to translate it. If you don't want feedback,
that's totally okay. But yeah, share your project, share your kind of translation, how you got something
that you did before that wasn't data specific and translated into
something that data professionals will
absolutely understand. Without, Let's have a look
what a good CV looks like.
3. What makes a good CV?: One of the questions we
have to answer here is, what is a good CV? And this is a bit difficult
to answer because there are three entities that we'll
take a look at your CV. The first one is going to
be an automated system. Most companies use these. It's an ATS or applicant
tracking system. This one is checking for keywords and they
are very annoying, but they used everywhere. And it's really important for
you to, they'll match this. There are websites
online that essentially used this ATS system that check for you how your CV or resume is evaluated
according to ATS. And I highly recommend
that you use these. Once you've set up your CV. Then it goes to recruit. Someone in HR is
having a look at your resume as having a look
if the ATS is doing well, did a good job, and
is going to evaluate you based on the job description
that you applied for. So here it is good. Hit the relevant keywords. And oftentimes also match keywords that are actually
in the job description. So if you are working with
the Microsoft Office Suite, so Excel Word PowerPoint, and they are asking
for XL specifically. You shouldn't just use
MS Office in there. You should also use
excellent there. And this is just an example. There are so many different, many different softwares that are very specific and recruiters don't always know that this is part of that software suit. What Microsoft, it's easy. But how about Python? We're saying we can work with
the Python psi pi stack. That is a word
that you can know, like not even all
data scientists know what that is
supposed to do. But a lot of people
will say Yeah, but this encompasses
NumPy Pandas and often scikit-learn
and of course I pi. But if they're specifically
asking for pandas experience, then you should write
that you'll have pandas experience in your CV. The recruiter will also
look for your experience. Obviously that the numbers
match up and everything. Here, it's important that you
match your local standards. For example, when I was still applying for
jobs in Germany, Germany needs you to have this is changing but often still needs you to have a
photo on your CV. If you apply to English speaking countries
like in the US or in the UK, this will actually put you on the discard pile right away. In the US, specifically, if you attach a
photo of yourself, you will often land
on the discount pile simply because this will avoid will accompany
that way you can avoid a discrimination
lawsuit if they went ahead with your application. It's a bit silly, but look
at your local standards. So if I'm giving you some advice and your local standard is
something different, then definitely take the
local standard because you have to get past the
recruiters first. And then it goes often goes
to a technical person, someone in the company that
actually does the job, usually a little
bit more senior. Having a look at your CV and checking if your CV make sense. If it's a good fit technically, if your formal positions
and if your skills that he lists do make sense in the position that
they actually need. So essentially what
you're doing is writing a CV for three people, which is incredibly hard. Which is also why you have to apply to so many
jobs today and it sucks, but we'll do our best
to get this around. So a good CV is hitting
those three points. It makes sense to a
technical person. It makes sense to
recruiter that is mostly looking at keywords and
at the formalities. And at times prestige,
all those things. And it goes through the
applicant tracking system. And our next class, we will have a look at how you can make a
nice savy and templates.
4. Where to find Resume Templates: Now that we know who
we're writing the CV for, we can choose our
formatting appropriately so it gets best passed in an
applicant tracking system. It still looks nice for
the recruiter and it can contain all the
necessary information for the technical person. So I have a couple of
recommendations here. Take something that is
fairly simple actually. So recently there was this trend of having
these really beautiful, visually appealing CVEs that have like skills and
little button boxes, or have even like little
points scales next to, next to you the skills and put
everything in two columns, sometimes even three
column layout. And I highly recommend
against those. I recommend you take
a single column. Nice CV, often written just in Google Docs or Word
or something like that. And really stick to the simple. So even the applicant tracking
systems that aren't that good can pass your CV
as good as possible. Because then you send them this PDF and you have two columns, so it's next to each other. It doesn't know that
the column is there, so it keeps reading and methods everything out
that you have in there. And then you add kick to the
discard pile despite having all the skills and all the experience that
is perfect for this job. So stick to a single layout. And let's have a look
where we can get templates because you don't have to
write everything from scratch. You can use help. So the first one is right here. This is Google Docs. You can simply use one
of these templates. Change some of the
colors so you like it, change the font so you like it. Don't get too artsy, don't get too fancy. Keep it professional,
but these are good. These are excellent. I have my CV from one
of these templates. So the second one
is going to become, now Canva gives you a
lot of flexibility, which is one of its downfalls. It's more for the artsy people. But you can find some
good CV template, resume templates in
here that are pretty, that are often a little bit easier to use for
a lot of people. And you can get a real
good CV out of this. So choose one of these and then translate
your experience. Maybe you have it
on LinkedIn into something that is appropriate
for a data professional. And this is what
we'll get into now. Now that we know
the form factors, we have to think about how to translate your experience into something that a data
professional and the recruiters in the field
can actually understand.
5. Structuring your data CV: Let's talk about the
structure of our CD. This one's a little
bit interesting because it varies locally. So if you have
information that is from professionals local
to where you are, like whether that's the US or India or somewhere
here in Europe. There are slight
differences in having a CV, so definitely be aware of this. And then then we can go ahead. The local information is usually better than
what I have here. I'm going to talk
about general ideas. So when someone has some local information
that is more important, like I said in Germany, a lot of times you have
to attach photograph, which is absolutely
not okay in the US. And a structure here usually is that you have executive summary
in the beginning. Now this is a little
bit controversial. I've heard a lot of people
that don't do this, and a lot of companies
that don't like this, recruiters either
love or hate them. But like an executive summary is a great place for you to say, this is who I am and this is why my experience is
relevant to this, especially if you're
someone that is changing careers into a data profession. This is a great place
to kind of give a little bit of
context for your CV. Now of course, your cover letter can give a lot more contexts, but a lot of times the
cover letter is skipped. And people look
at your CV first, so they have an idea of what your skills are and then
go to the cover letter. So giving a little
bit of context, oftentimes a really good idea. But once again,
listen to your local, local information
because that is what counts where you are. They don't do
executive summaries and don't add one because this will probably put you on the
discard file, discard pile. When recruit actually
has a look at it. Then you have your professional experience
and your education. And here it depends
which one you did last. So if you're already a seasoned professional with a lot of jobs under your belt or
a few jobs under your belt, this should come first
and then education, if you're fresh
out of university, education usually comes first. The order changes depending on how much and what kind
of experience you have. Within these. You want to have it
chronologically ordered, but descending the
newest 1 first, like whatever you're
doing right now or what you've just finished has to be all the
way on the top, and then you add whatever below. Here's the thing though. Not all your experience is
relevant to this position. Especially if you think
about that you are changing into a data
proficient profession. Not every job and every
degree you hold is going to be relevant
to a data job. You can make them
sound like that. Definitely, you can
change them in that. And that is something we go into Definitely because this is
one of the easiest fixes. I see an a lot of CVs. But just be aware that sometimes you don't
have to add all your jobs, especially if their
internships or if the smaller side gigs. You don't have to add them all. The most important part is, and wherever I was in
the US, in the UK, in Germany, I've never
seen a CV over two pages, unless you're an academia
where they get really long. But usually leave your CV even if you're a seasoned
professional, like a senior, whatever you did, usually
you want to cut it down to two pages and only leave the most relevant
information in there. They can always go to your LinkedIn profile later
and have looked at it. Ask you more questions, especially if they're asking questions while you
have gaps in there. It's like, Oh, I
had a job there, but I didn't find that this was so relevant
to this position. That is completely okay. But sell yourself with
the most relevant stuff. I do that myself like I don't
add all the jobs I have. If I have degrees
that aren't relevant, maybe I mentioned
them but I don't like ad but it didn't
those degrees. It's just the line. And also in the next section
that is really important, the product experience only
add the most interesting, most relevant stuff in there. So this is really a whole section that
will go into as well, but your product experience
is extremely valuable. This can be different parts. So if you did my other courses here where
we do build products, you can include
those other courses like on Coursera that
have certificates. You can add those. And of course, open source
contributions as well. Let's go more into detail what you can do in each section, in a separate lecture
for all of them.
6. Executive Summary: Starting out with the
executive summary. Executive summary is
really a few sentences about what you
want to do and how your experience that you have
from positions degrees is going to influence your well, your suitability for this job. Really what you want to do
is write a few sentences. How everything you've done relates to the drop
description that you read. Here, it's really
important that you related to the job description and to the job that you
are applying for. And it should be fairly short. Like advice I heard is
keep it under three lines. Really have it skimmable,
like a crispy, nice short paragraph
where you write, I'm applying for this job. You don't write I'm
applying for this job, but I am looking for this in data science
because I liked doing this. And this is my
experience that is super relevant for it
as a geoscientist, which is my background
all the way in the back. I was always
formulating it in a way that my experience with real-world data is
influencing how I am great data scientist or a great machine
learning engineer. Knowing inversion from physics is great for a
machine-learning position. Really help recruiters and technical people
get context for, for the position
because they don't always know what place you, especially if it's your
first position or where you're changing into if
your data profession, you really want to help
people understand how your experience really
relates to this job. You may be the odd one out, but you can make it easy
for a recruiter to say, Okay, I'm going to put
you forward because I think there's something in
here that we can work with. And you'd be surprised. There are so many people that
have experienced in some of the applied fields like
biology, ecology, geology, all those ODEs that really, really give you relevant, relevant experience with working with real-world data,
with messy data, and a lot of data scientists
and machine learning people love this experience. So don't sell yourself short. You can definitely
find a position. And this executive summary, if it is used wherever you're
applying, applying for, is really a great way to get your first foot
in the door and say, This is why I'm great for this. This is the context
for my experience. So let's go on to the professional experience now and have a look how
we can reform it. A couple of experiences
for a date profession.
7. Professional Experience Section: In this section for your
professional experience, you put the jobs that you help that are relevant
for this job. Usually that should
be your last job, then whatever is
relevant before that. And here, what I like to do is have a little won't
have all the details. So what position where you
how long from when to when? Include a month and the year. So people can deduce
how long you were there for and then
also the company. That is my heading. And then under that, I put bullet points. And there's a really
nice formula how to make these bullet points really
stand out to recruiters. So the way this works is you start out with
an action word like produce or developed,
or really led. Things that are powerful
action words that are like, oh, you are someone
that is doing things. Really be, be creative there and try to mix
them up as well. Don't be like produced, It's produced that developed
this, developed that. Try to be a little
bit creative with it. So you use a different password
for each bullet point, and then you want to go, action words are
developed a thing. Developed open-source
software x. Then to solve, why did
thing x to solve y? And then you want
to say achieving that developed
open-source software x to solve the needs of the stakeholders and
achieving whatever that is. And here, the achievement, like for me, it's really hard. I come from academia. So it can be really, really difficult to translate your experience into
something tangible, into something
where you're like, Yes, I achieve this thing. But if you can try to put
a number, is really good. Achieving if you saved
money, that is fantastic. If you sold more, that is great. But also like since I
came from academia, something measurable is
that many publications. So if I, if I worked in a certain postdoc
or something like that, I can really go
into detail with. I analyzed the data
of that to do this. And this resulted in
five publications. That is fantastic. So this is really
putting it into context, how productive you were,
what you achieved, and what you did,
for which reason. Here, you can use a
couple of tricks. These tricks on disingenuous, these tricks are legit, but you have to translate your experience
into something that both fruits and technical,
personal will understand. Keep jargon to a minimum. But translate your experience into a data analysis framework. If you work with Excel,
that is fantastic. Did you do any of the formulas
that they have equations, however, they're cold
and your your version. That is good, that is already experience in data processing. Did you do any wrangling
of data where you got data per email or over
some stream or wherever. That is great because you were essentially doing data
pipelining and data cleaning. If you extract the data from databases and then changed
it into different formats, help load data into software. This is commonly
referred to as ETL, Extract Load, extract
transform load. And really those are
terms that you should try to somehow get into
your description if you did them here. Now it can be a little
bit difficult because you don't really know what
these people expect. But if you have any
analysis experience doing any analysis and
specialized software or an Excel, that is fantastic, right? That you don't even
necessarily have to write the software name
because no one's coming. No. But you can say in
specialized software, analyze data to achieve this in software x and achieving that. And then. Also, if you did any
visualizations, that's fantastic. Visualizations are
stakeholder communication. You can also talk
about, first of all, your data visualization
because this is a great skill. You can also talk about stakeholder communication
because this is an important skill that especially data scientists
that come out of boot camps that are
fairly junior don't have, they don't have this experience of communicating
difficult results, difficult data to
stakeholders that come from all kinds of
different areas. So those can be
technical as well. Those can be C-suite. If, if you can translate your
experience in a job into, into this, this wording that recruiters understand that
will change everything. Because suddenly your job
working as a biologist is a data science job because essentially
you already did this. If you worked with your specialized software
to analyze the data, then right at that way. But also if you, well, if you had to get
your microscope data in a weird format and
change that into CSVs. You can load it into XL or however you did
your data analysis. This is already really valuable. Even if in that job you didn't
use Python and Pandas and NumPy and only learned that in your free time with
your products. That doesn't mean
that this job is entirely irrelevant because it's still taught you how to deal with this messy real life data. And a lot of ways taught you communication with
relevant stakeholders, like your lab had, or with professors, or
with your managers. This, this experience isn't
for not a lot of ways, you still have to translate it. And like I said, keep jargon to a minimum. If you did certain lab cultures, I don't think it's that relevant to put what
kind of cultures, but it is relevant to put the analysis that
you did with them. So don't don't go into too much detail
because this will have most people checkout
that aren't in the field. This is better for
if you have a really relevant I'm job in biology
then of course include this. If this is going into a
data science position in biology and biology
experience different story. But if you're going into like a general data science position, then keeping a little
bit lighter on those deep details and
translate your experience more into something that
these recruiters and technical people on the data science side
of thing Understand. Next we'll have a
look at education, which is fairly similar. But still we have to
translate that as well.
8. Education Section: Let's talk about the
education section. The thing with education is
that it's highly individual. So even if you think people
know what you learned in a certain degree because you spend a big chunk
of your life in it. So it's second nature to you to know what is in this degree. And you think everyone knows, especially if you're from
some prestigious university, you think it is implied
about your null. But it really helps to tell people what
you learned there. So what I like to do, especially if education
is my last experience, tell people what you learned. So I did my PhD as the last thing when I had to write a CV
for that position. So I was like, I
did this analysis. I apply these kinds of softwares and I rode Python packages. And I had this number of publications out of
this one book chapter. And I was really productive. So it was really helping people see the kind of
impact that I had. Especially with PhDs. People usually have no idea what you're actually
doing an a PhD. And the thing is also, it's very variable over
different, different countries. Every country has
different prerequisites for you to get a PhD.
If you're in the US. It's so, so, so
different from Germany, it's so different from the UK, which is again so
different from Denmark, where I did my PhD. Really tell people what you did. Did you get teaching
experience? Great. Put that if, especially if
it's relevant to that job, if you did presentations,
here's the thing. A lot of people, when they hire
junior, junior folk, they don't know that you have extensive
presentation experience. And especially if
you're a PhD who was presenting work at
workshops and conferences. This is actually really
valuable because people that go the more normal route, the, the path well trodden. You don't really get the chance to present that much
until you are really in a job where you have to check out what kind of
experience you're half. And there are fantastic
videos on how to translate. Well, how to translate
your experience, especially in education, into something that people in the
industry will understand. Because like I said, most people have no
clue what a masters, especially research
masters and a PhD and tail help people
put bullet points, use the same kind of method that I described the professional
experience section. Here's an action word, thing x, using why. Achieving that formula
is really, really good. You can also put a
couple of courses, but the titles of the courses
should be descriptive. And again, keep it on the
light side with jargon. Really, if you did
presentations, That's fantastic. Teaching experience
is really great because that means you
can communicate and you're not just some lofty
scientists in the ivory tower. So really think how you can make others
understand what she did, even if they have a good idea. Try to step it up a notch and make it really easy for
recruited to see this and say, Wow, I had no idea, this is really, really good. And this is individual to
the kind of job we have. If you did analysis
during your research, if your data collection
as well, this is great. And most people who have
no idea that you do, like the cliche is that you're
some person just reading, reading, reading, and then
writing about something. That's it. That's the impression that
people have of university. So, yeah, sitting in
lectures obviously. But the higher you
get in university, the more wrong this
assumption is. Yeah, I know I'm
repeating myself here, but help people
understand what you did. And use this formula
with the action word to get bullet points
that people understand. And again, it's the best if you only add
relevant experience. But if you don't have
that much experience, especially if education is
the last thing that you did. It's fair to kind of expand on your PhD, bachelors, masters. So people can see
you what kind of experience you gain that
if you did field work, all those interesting things. So that's the
application section. This one will get smaller. Usually the longer you are
in an industry position, the more positions you
have had in the industry, people tend to reduce
their education section. I think in my last CV
that I applied with, I actually put my bachelor's and my master's into
one bullet point, essentially just
saying I have those, then not that relevant here. Look at my professional
experience and yeah. It's all a little bit fluid. Depending on your experience, really play around with it, whatever makes sense,
and send it to someone that doesn't know
what you did in your PhD, which is probably almost
everyone that you know, like a family member, a friend outside
and see if they can understand better what
you did in your PhD. If they have questions, The recruiter will have
questions as well. So take those as feedback and explain better
what you did and really get a nice feedback loop
going And that way and improve it so people
can understand how, how you experience relates to this data
professional right here. In our next section, we'll have a look at
products which is going to be extremely valuable and a little bit different from the usual savy advice that you see on more
corporate positions. But it's extremely important. So don't skip the next one. We'll have a look at projects.
9. Project Experience Section: This section is about projects. This is one of my
favorite parts of a CV. I've probably hired
the most on this one. This was the biggest decision
because a lot of people, like a degree can
mean a lot of things. Position can mean
a lot of things. But if you have a project, you have something
very tangible, something that people can see, often provide a link and really understand what
you have done already. What do you have
experience with, maybe how your code
quality is as well? So I like to do is
look at my past, what I've done, whether
it's research projects or hackathons or contributions
to open source. And dig through what
you've done already. Even if it's at another
job and it was open. This is project experience. So if people can see it somewhere or if you can talk about it and
it's not under NDA, this is great because this
is tangible experience and gives context for your
knowledge and what you've done. So oftentimes I like to
have a look at some, some experience with coding, some software experience,
whether that contribution to an open-source project or a small project that
you did yourself. This can sometimes even be finishing project
for the course, but I wouldn't not
always recommend this. I actually have a
YouTube video on this of projects that I
recommend not doing. So have a look for that. But essentially, if it's
something original, you found some data that
is really interesting, or even some data from yourself. You analyze that data, you make like a
nice visualization. Or you presented somewhere. Maybe it's even
during a hackathon, then this should definitely be under your
project experience. There I usually put
the project name, put the year where it happened, and then write a little
sentence about what this is and how this relates to being a
data professional. And this is really, for me, this is the most
interesting part in a CV. It's not always standard way. A lot of people also
have looked at status. So did you work at Google? That is probably really,
really impressive. But knowing how you work, providing links to your GitHub
or maybe to your Kaggle. Where you have one, something where you have achieved something
where you have published something that is
really, really valuable. For a lot of people can be
difficult finding projects. I've heard from a lot of people that they haven't done projects. Oftentimes that's not true. Most people have some kind
of project experience. Now, one question is whether
you can talk about it. If it's for work. Check if there's like a
non-disclosure agreement. But usually you have some thing that you've
done for something. So students often
have class project. When you get to a point where you have
more project experience, those probably fall out first
because they're too simple. But yeah, when you've
done something, when you've
participated in events, there's usually some kind
of project experience that you can put into a nice
little package for that. So go through your
past and be open with what might count as some, some products that
came out of it that is now existing and that you can show to people
are explained to people. Not everything has a link
in the Internet, I'm aware. But if it's a little
checkbox that you worked on, if it's a data
scraping project or any of those kinds of things where you just
did something for fun. Is that an automation project like you amazing at
using Apple's shortcuts. I know this is a
little bit funny, but essentially if you
get some kind of data, which is sensor data on your iPhone and you have some
really cool shortcuts that are running on that and automatically
triggering other things. This could be a small project until you have bigger ones
that you can show to people. But this kind of
automation shows that you can build pipelines out of, out of real-world data source. Now, this is fairly
simple, I'm aware. But especially for
junior positions, we don't have to have these huge projects
that would be worth millions because then you don't have to apply for a
junior position anymore. Go through your experience. And when I say be creative, I'm not saying be
creative with the truth, but I'm saying be
creative with what you count as a
project because I'm sure there is a project
that you have that you can put on your
CV and probably more. So really go through,
have a look. Is there any code and
the visualization, even if it's an Excel, that's okay for the beginning. And put that there. In our next class, we'll have a short discussion about skills that are
really important, that you might still have to
do a little bit of learning, especially if you're coming from one of the more
applied sciences, but which are fairly easy to learn if you have
a good basis already.
10. [Work Session] Craft actionable Resumé bullet points: It can be difficult to be specific and find
specific examples. So in this small lecture that I'm actually
recording after, but I wanted to add this to make it really usable for you. I want to talk about translating my old Cv into something new, using these action words
actually created a little, little e-book that you can pick up in the
resources section, then pick out my
friend action words. And C also links to
more action words and some ways to differently do this kind of CV
and resume work. And those lists have up to in 200 words and I picked out my favorite ones in
different sections. I basically split this
into four sections. So general
responsibilities, then, your technical and data
experience, and of course, leadership and management and
stakeholder communication. And let's see how we can translate some of these
skills into that format of action skill thing
using why achieving zed in our general
responsibilities. If we don't want to make our CV too technical or too focused
on different things. Here we can talk about how we
expanded the documentation, for example, for my
teaching skills. So I expanded the documentation
of teaching material. This guilt right
there where with Jupiter to accelerate on
onboarding of new teachers. So now you can see how this
is very clear what I did. Of course, there's a little bit overlap
with communication here. But yeah, really see what, where you put everything
and you don't have it's not that key account
to be quite honest. So what else did I do during my time as a post
grad and as a PhD, I did organize the journal club, for example, the journal club. What we can go for years
to disseminate knowledge and facilitate
cross disciplinary. You could also say
across department. I'll think that is a
little bit more broad, a little bit more like
talking about oh, yeah, I got all these different
departments involved. Then of course, what
did you achieve? So I don't know, in this journal club, I think a lot of people
learned and networked. And I would put that down actually because this
is quite tangible. Why do you do a journal club? Well, people learn and
people can network. So this is how you can take some of them are general things. And when we want to go technical as simple
one right here, that you can take a refactoring. So if you have worked
with code before, that is a really good example
how you can translate this. So you can say reflected existing code phase using
pytest, like eight example. So those are two
Python libraries that are good for code quality. One is for testing, the
other one is for linting. So making sure everything
looks nice and is in a format that works. And what do you achieve well? Test coverage, hopefully,
and consistent code quality. So this is a really good one because this shows
that you are hands-on. Then you can also talk
about how you install things. If you did that. So installed updates,
plug-ins for Excel using the Homebrew
package manager for example, if you use that or whatever
you're using in your company. And what did you achieve? Well, this case,
I think with X0, oftentimes you install
these plugins to get some kind of
functionality out of it. So it runs faster, runs better. And I'm specifically
using Excel here too, because most people
forget that a lot of work with axle
is also data work. Really see what you
did that if you worked with some equations, formulas, if you did
some data cleaning, shows that you did some teamwork enter
through work together, and that you have technical
skills that pertain to data cleaning and data
wrangling in Excel. But this knowledge is
transferable, right? So this is kind of telling people what you did if you just, just ride used Excel. No one really knows what you did if you write a
sentence like this. Oh yeah, I installed updates and plugins to do this using this. That really gives you
the same keyword. Another keyword if
that is necessary. But also these keywords. Data cleaning. You can also do data wrangling here if that is
in adopting description, really expanding on
these bullet points is great because otherwise you just have these two words right here. I'm fairly certain
I have some of these like I could
have really expanded on these and made my job
search much easier by maybe reducing some of
the jobs I have in here. Yeah, give people an
idea of what you're doing and then what we can do. Let's have a look
at some leadership. Something good is always to
have hiring and experience. So hire new staff. Evaluating the CDS
if you didn't. And cross-checking
project experience, which is something that I did
in my last last drawback, actually resulting in two new June your highest. So this shows that you have
leadership experience, that you have hiring experience, and that you are more on the
SR side of the spectrum. Other ways. You supervised anyone
trained anyone, that is great right here. If you cultivate it. The the experience for people so cultivated like
a coding, get together. This was something really
nice that we had at Harriet right here. At the ELP. Cultivated a Python Meetup to share, share common problems. Daily work with Python
and associated packages. Together we solve
multiple problems, solving, um, multiple
sticking points and problems. Which is really nice
if you are the kind of person that gets
people together. I mean, this is replaceable. If you don't have that
Python experience in a job, maybe you have that Python
experience outside. You attended a PI data meet up. Or if you have some, some internal like lunch and
learns something like that. These are really great if you have this kind
of experience. Yeah, I'll go into
what you did in your job and really suss
out some of the things. I hope this is some
kind of breadth and on different
skills that you see. So these are more technical, these are more general, and these are more in
the leadership area. And yeah, don't forget
that X is some basic data, data experience, but
you can translate it to make people understand that you already have some
data experience. And yeah, if you, if you worked in the field, then you know where
the datas from. So for example, formalize the data acquisition pipeline. In the field. Pulling two teams needs. Data processing needs. Achieving higher
overall data quality. When you are at any end of
this data processing spectrum, look into it if you can
find some of these things. If you are in the field, if you are working with people to standardize how
data is reported, you can make this into a data CV and give
yourself a leg up. Make yourself more
interesting to recruit us rather
than just saying, oh yeah, I did some field work because no one really knows
what field word means. Make people understand
what you do. I hope these examples help. I'm putting some of these
examples into that e-book. So definitely check it out. It's free, it's in the
resources section. And yeah, I hope this is
as applied as you need it. So onto the next lesson.
11. Filling the Skill Gap: Let's talk about some skills for data professions that
are very important. These can be skilled
that you don't have yet, that you still have to learn. In addition to having experienced
with real-world data. You can, if you want, you can skip this part
because each, well, if you're reading
job descriptions, those are obviously yours, your source of truth. This is only an idea of some of the skills
that can be really, really important that I've found in a lot of
job descriptions. The first one is Python, libraries in Python that are used in data science
and data analytics. So this is pandas, numpy, and often times
Matplotlib or seaborne, and some basic machine
learning experience using scikit-learn. Having those on your
CV is quite valuable. Building a small project
that kind of showcases these technologies can be really good and it's great
having on your CV. You can also mention these
technologies on your CV. And that way you can hit those keywords for the ATS
and for the recruiter. Building a small
end-to-end product is a good idea to
hit those skills. And of course, if you are applying for a more software
development heavy job, It's great to have experience contributing to open
source software because those are oftentimes tested and well-documented and
people can find it and have a look at your
contribution. Here. It's really great if you, if you expand on your
Python knowledge, either through a project
or through a contribution. It really depends
what is the focus on the position that
you're looking for? Then some jobs are looking
for our experience. I haven't applied
for any of those. But if this is a job requirement in a lot of
jobs that you're going for. This is obviously a language
that you should learn. And especially the
especially jobs that are smaller in a lot of
ways that don't need that. Heavy TensorFlow or PyTorch in bigger machine learning
systems are as great. So the next one that is more generally really
important is SQL or SQL, which is language to get
data out of databases. So this can be a little bit confusing because
there are courses with certificates from in
Oracle and Microsoft and a lot of other places
for database administration. This is little bit
different because there are the systems that
store databases. And there is the witches
often like Postgres, postgres SQL or MySQL and all those different
technologies. And there is the
querying language, SQL, which is to get
data out of them. This one is fairly universal and this one for
the administration, which is more on
this is probably more on the side of
someone that actually is the database administrator. You want to learn the language, the language to get data out
of systems in businesses. If you're a data
scientist that is applying to a classical
corporate job, this is really important
because in most companies, you have a SQL
database somewhere and retrieving that data to be able to do your analysis
on that data. Whichever system
you use after that, whether it's Python,
whether it's, you have to get
the data somehow. Knowing SQL is really,
really important here. I'm not sure if it
was a requirement in my jobs per se because I was more on the
machine learning side. But regardless, I
have it on my CV because it shows that you know how to get your
data from a certain place. And it's so important. That is a skill that a lot of
the people that come out of the sciences don't usually have because our data is
coming from somewhere else, not out of a database. So having a specified course
that teachers use SQL is valuable and
definitely something that you should
consider investing in. I personally like
the free courses on cattle to get you started. They have to, but there
are also courses here on Skillshare and you can also
get certificates and app. But like I said, there are, there is a lot of
confusion whether it's SQL course or it's a database
administration course. So be aware before you
pay a lot of money for these certificates because you might end up in
the wrong course. Then you can basically expand around what you find in
different positions, the data professionals growing. So that is the data
engineering part, which is kind of building
pipelines between data. So if you have
experience with Hadoop, Spark, if you have experience
with darker or airflow, these kinds of things that
help you make computation possible and also data pipelines possible that is
really valuable there. Then you have data analyst
position where Excel Tableau, those technologies
are quite valuable. Then you have data
science positions where oftentimes you go more into depth on then also
seaborne and stats models. So hypothesis testing packages that you want to be able to use and all the way
to machine learning engineers that
have to be able to use also darker oftentimes. But TensorFlow,
PyTorch really have a look what kind of position most aligns with
what you wanted to do, and then learn those skills. Each part of these professions
has different skill sets. And it's only recently starting to kind of
separate a little bit. I've had a lot of positions
where data scientists and machine learning engineer was used as the same
word essentially. And it's really not, those are different professions. There was a different goals, but one is paid less
than the other. Sometimes it's described in the wrong way just to
get someone for cheaper. Be aware of that. But essentially see what kind of position you want to work in and then acquire
those skills. And of course, this
list isn't complete, but it gives you an
idea of what you might look out for
and really scan those job descriptions
so you can learn the skills that you
need to get that job. Because I'm aware we don't have all the skills necessary
and the data professional. But this is the next step after translating
your experience. And especially if you're
applying for a junior position, you don't necessarily
need all those jobs. You might already get lucky with your experience
and they might say, You don't know SQL, that's okay. We're going to accept that, put you in a cause for this. Because SQL is really easy
to learn quite frankly, especially if you already
know programming. It's very, very
easy to understand. But it's a skill that a
lot of people asked for. So you already having that skill because you
already took a course for it. Maybe did a week
of work with it, have a project with it. Then you're already one step up. And then you already
have, in addition, you have your really
awesome applied knowledge with real-world data. That is how you upskill, get those high-value
skills and add those on your CV in form of projects, experience, courses,
certificates. And that part is a little bit less
translating and more filling the gaps because
of course, there are gaps. You haven't done
this job before. But most of data science, you can learn for free. A lot of the skills are
here on Skillshare as well. Look for those courses and see what people are asking for
in these job descriptions. And try to up-skill. You don't need to
hit everything. But if you have looked at ten different job
listings and you see what is equal
in all of them, then you know, that is one
of the high-value skills. Get that skill. And you'll see v will land
much higher on that pile. And you're much more
likely to get a job, especially after
you translate it, your other sections
into something that a recruiter will understand and the data professional will understand and get
you in that job. So that's our entire receive. Keep it under two pages. We'll get right into
the conclusion.
12. Conclusion: That's how the class, we went through all
the sections in a CV. How to translate your
specific experience into something that will
hopefully get your job. Remember, getting a
job is really hard. It's hard work. Don't let anyone else tell you. I I have a pretty good CV. I'm sorry to say it like that, but I find my CV
pretty impressive. And most people mural that. And still I wrote hundreds
of applications because sometimes my CV just crashed
and burned in the ATS. Sometimes I didn't hit the right keywords
with a recruiter. Writing a lot of
applications sucks. But it doesn't mean that your CV or your skills are bad and it doesn't
diminish your worth. As a person. Probably amazing. And really be aware that
you are an odd one out. So it might take you
on an application more because someone has taken a little bit of a gamble on you. But if you translate
your skills, like we discussed
in this course, if you fill the gaps with the
skills that we discussed, then I'm sure that you can
transition your career from whatever you're doing now into a data profession, into tech, and find an amazing career that is oftentimes
really inclusive, super interesting,
keeps your learning and keeps you engaged
throughout quite some time. So I hope this class
helped you translate your experience into something data
professionals understand. And please leave a review. And if you want that, other people find this,
add your project. I know this can be
weird with your own CV. So that's why we have the class project section
in the beginning was some suggestions to not share all your personal
data in the Internet. And with that, thank you
for taking the class. Think about checking out my other classes that
are about data science. Data science projects. With that, find me
on social media. I write a lot about data
science and machine learning. And again, thank you so much
to making it to the end. And good luck on your
learning journey and good luck with your career.
Jesper Dramsch, PhD, Scientist for Machine Learning
