How I Would Learn to be a Data Analyst
Summary
TLDRIn this video, Luke, a data analyst, shares his insights on the pathway to becoming a data analyst, focusing on an iterative two-step learning process: acquiring skills and applying them. He emphasizes the importance of technical skills, such as Excel and SQL, and suggests starting with these before moving on to analytical skills, domain knowledge, and soft skills. Luke recommends using online courses like Coursera for learning and projects for practical application, highlighting the value of showcasing these skills to potential employers.
Takeaways
- π Start with a learning process that involves both acquiring knowledge and applying it immediately to solidify the skill.
- π Focus on four main areas for a data analyst: technical skills, soft skills, analytical skills, and domain knowledge.
- π§ Technical skills are recommended to start with as they are tangible and can be built upon with other skills like writing or communication.
- π Excel and SQL are identified as the most important technical skills for entry-level data analysts, based on job posting analysis.
- π οΈ Use tools like Coursera for learning and also for showcasing projects that can be added to a resume for job applications.
- π Begin with a broad overview of tools to understand which ones align with your interests and passions.
- π The Google Data Analytics Certificate is suggested for gaining a general understanding of popular tools and their applications.
- π Incorporate analytical skills like problem-solving and critical thinking into projects to apply and improve these abilities.
- π’ Domain knowledge is beneficial, and applying data analysis skills within your current industry can lead to success.
- π£οΈ Soft skills like communication are important and can be showcased through social media or other platforms.
- π Iterate over learning by mastering one skill, applying it, and then moving on to the next to continuously grow.
Q & A
What is the main focus of Luke's channel?
-Luke's channel focuses on tech and skills for data science.
What is Luke's suggested pathway for becoming a data analyst if starting over?
-Luke suggests an iterative two-step approach of learning and then using the skills, focusing on technical skills, soft skills, analytical skills, and domain knowledge.
Why is applying skills immediately after learning them important according to Luke?
-Applying skills immediately helps in retaining the knowledge and provides tangible experience to showcase to potential employers.
What does Luke consider as the four general areas a data analyst should focus on?
-The four general areas are technical skills, soft skills, analytical skills, and domain knowledge.
How does Luke recommend using newly acquired skills?
-Luke recommends using new skills through coursework projects, portfolio projects, work projects, and teaching others.
What is the role of online courses and certificates in Luke's learning process?
-Online courses and certificates are useful for the initial learning phase, and the projects associated with them can be showcased as experience.
What is the recommended starting point for learning technical skills as a data analyst according to Luke?
-Luke recommends starting with Excel and SQL as they are the most important and commonly required skills for entry-level data analysts.
What does Luke suggest for someone who wants to master a technical skill?
-Luke suggests focusing on either Excel or SQL first, as they are the most popular tools for data analysts and can increase the chances of getting hired.
How does Luke incorporate analytical skills while learning technical skills?
-Luke incorporates analytical skills by applying them in projects that require problem-solving, critical thinking, research, and math skills.
What is the significance of domain knowledge in becoming a data analyst?
-Domain knowledge is important as it allows one to apply newly learned data analytical skills in a specific field or industry, making the individual more valuable.
How can soft skills be developed and showcased while learning technical skills?
-Soft skills can be developed and showcased through social media, writing posts or tutorials, sharing code on GitHub, or creating content on platforms like YouTube, Instagram, or TikTok.
Outlines
π Journey to Data Analyst Mastery
Luke, a data analyst, introduces his channel focusing on tech and skills for data science. He shares his pathway to becoming a data analyst, emphasizing the importance of both learning and applying skills. Luke outlines his iterative two-step learning process, which includes not only acquiring new skills but also using them in projects to retain knowledge and build a portfolio. He stresses the value of showcasing projects to potential employers as proof of experience. Luke also highlights the role of online courses like Coursera in facilitating this learning process, particularly through their specializations and projects.
π οΈ Building a Technical Skill Roadmap
Luke details his approach to mastering technical skills as a data analyst, starting with the most sought-after tools such as Excel and SQL, identified through his own data analytics project. He suggests beginning with a broad overview of tools through programs like the Google Data Analytics Certificate, followed by deep dives into specific tools. Luke recommends learning BI tools and programming languages based on personal interest and industry needs. He also discusses the importance of applying analytical skills, such as problem-solving and critical thinking, through projects, using his food nutrition calculator project as an example. Additionally, he touches on the significance of domain knowledge and soft skills, such as communication, in a data analyst's journey.
π Leveraging Soft Skills in the Digital Age
In the final paragraph, Luke addresses the shift to remote work and digital communication due to the pandemic, viewing it as an opportunity to showcase soft skills. He shares his personal experience of creating a YouTube series to improve his communication skills while learning Tableau. Luke encourages the use of social media platforms for demonstrating soft skills like writing, coding, and creating content, which can also serve as a portfolio for employers. He concludes by reiterating the importance of starting small, iterating, and building on skills to advance in the field of data analysis.
Mindmap
Keywords
π‘Data Analyst
π‘Technical Skills
π‘Soft Skills
π‘Analytical Skills
π‘Domain Knowledge
π‘Iterative Process
π‘Coursera
π‘Portfolio Projects
π‘BI Tools
π‘SQL
π‘Excel
Highlights
Luke, a data analyst, shares his pathway for becoming a data analyst if starting over, including skills and learning process.
An iterative two-step approach to learning is recommended: learning and then using the skill.
Four general areas to focus on as a data analyst: technical skills, soft skills, analytical skills, and domain knowledge.
The importance of applying skills immediately after learning to retain them effectively.
Using skills in coursework, portfolio projects, work projects, and teaching others to showcase experience to employers.
Online courses and certificates are great for learning but require practical experience for resume enhancement.
Coursera is highlighted for hosting courses and projects that help in learning and showcasing skills.
Starting with technical skills is recommended for their tangibility and motivation factors.
Excel and SQL are identified as the most important skills for entry-level data analysts based on job posting analysis.
BI tools like Tableau and Power BI, and programming languages like Python or R follow in importance.
The Google Data Analytics Certificate is suggested for an overview of popular tools and a capstone project.
Mastering Excel or SQL is crucial for increasing the probability of getting hired as an entry-level data analyst.
Incorporating analytical skills like problem-solving and critical thinking while learning technical skills.
Basic math, including algebra, probability, and statistics, is sufficient for most data analyst roles.
Applying domain knowledge from one's current industry or field while learning new data analytical skills.
Soft skills such as communication are important and can be showcased through social media and other platforms.
A roadmap for learning to become a data analyst, emphasizing starting small and iterating with each new skill.
The video concludes with advice to start with one technical skill and build a project, then iterate with additional skills.
Transcripts
what up done nerds i'm luke a data
analyst and my channel is all about tech
and skills for data science and in this
video today i wanted to cover my pathway
for becoming a data analyst if i had to
start over again and for this i'm not
going to be only sharing the skills that
i recommend learning but also my process
for learning different skills which i've
applied and refined over my time in
school learning engineering to my time
in the navy learning how to drive a
nuclear-powered submarine and then more
recently to learning all the different
skills of a data analyst in order to
continue to gress further in my job this
process has also been refined by my
interactions from others that have not
only gotten jobs as data analysts but
also hired others for these roles as
well my journey was filled with a lot of
wasted time and effort and so i'm hoping
that this video helps save you effort
and also time and learning the skills
you need to know for your job so let's
break into my recipe for learning
anything and it's an iterative two-step
approach that i recommend taking that
can be applied to anything that you
really want to learn the process
consists of learning it and then using
it so let's expand further into what you
should be learning as a data analyst and
i feel that there are four general areas
that you should be focusing on that
consist of technical skills soft skills
analytical skills and domain knowledge
don't worry we'll go into all these
general topic areas in a bit but we need
to move to that next step of actually
applying it immediately after learning
it and using it is it true that if you
don't use it you lose it
is that a serious question which in this
case is quite literally true because the
tools that i've learned in the past and
haven't applied i haven't been able to
retain them so i feel like this is a
really key important aspect in order to
retain that skill and you can use these
skills in a variety of different ways
such as coursework projects on coursera
portfolio projects for your resume work
projects for your job and then also
through teaching others now there's an
added benefit of this second step and
that's that because you've created
something with your new skills you now
have something to showcase to an
employer as experience so when you're
searching for a job you can now display
this item that you created for employers
to see i've rambled about this before
but i think that online courses and
certificates are great for this first
step in the process of learning things
but if you don't have experience to
showcase on a resume on how you use
these skills that you learned an
employer is not going to risk hiring you
and all of this relates directly to the
sponsor of this video coursera coursera
does a great job of hosting courses so
that way you can learn the skills in
that first step of the process but also
in that second step of using something
it then goes and has projects available
for its specializations and certificates
and this is great because you can not
only display your certificate or
specialization on a resume you can also
showcase those projects as experience
for employers to see so getting back to
my learning process so once you have
learned a skill and then used it it's
then time to iterate back and learn a
new skill so where do you actually start
and what skill should you focus on first
for learning a skill my preference is to
start with those technical skills and
then also incorporate those other skills
such as analytical or domain knowledge
while you're learning a technical skill
so why do i say start on a technical
skill first so one i feel like they're
more tangible and they're easier to set
goals that you can actually accomplish
such as you can write out what functions
you want to learn for excel and then
learn it i also find that technical
skills are funner to learn and i have
higher motivation levels when actually
setting out to accomplish that and two
they allow you to apply other skills
while actually focusing on that
technical skill for example say you're
learning a technical skill such as like
r you could also write a blog post about
it and this would showcase and build on
your soft skill of writing so
technically you're not only focusing on
technical skills but you're also trying
to incorporate those other skills as
well alright so let's jump into my
technical skill roadmap so i recently
did a data analytics project where i
went through and scraped job posting
data from linkedin i was able to find
the most important skills for
entry-level data analysts based on how
many times a skilled appeared in a job
posting so my insights from this project
were this that excel and sql are the
most important skills to learn of a data
analyst as they comprise almost half of
all job postings following in popularity
are the bi tools of tableau and power bi
and then also the programming tools such
as python or r so from this my
recommended roadmap is this first i
recommend getting a brief overview of
all the different tools i think this is
going to help with later on identifying
tools that you want to focus on based on
what your passion and interest is in i
like the google data analytics
certificate because it teaches you a lot
about the popular tools of sql
spreadsheets are in tableau and then
going back to my recommendation on how
to learn it not only teaches you about
these skills but then you also implement
these skills in a capstone project for
the certificate now this first step in
the process is all about breadth not
depths and the google certificate is
perfect for this because you're not
going to be a master of any of these
skills once you complete it but you will
have a general overview of these tools
and you also have an introduction to
other skills as well such as soft skills
and domain knowledge next it's time to
get into a mastering skill and for this
we need to focus on either excel or sql
i recommend these two most popular tool
of data analysts because from a
probabilistic standpoint if you have
these two skills on your resume i feel
like you're more likely to get hired for
an entry level data analyst job now
regarding whether to use excel or sql
first i really leave that up to you if
you're looking for recommendations for
resources to learn these type of skills
check out this recent video i did where
i went over some top courses to learn
the skills of a data analyst next after
mastering both excel and sql it's time
to get into mastering other tools such
as bi tools and programming languages
once again when selecting one of these
tools i'd go off what your passion is
select one that you have an interest for
and you really want to dive into and
learn and apply other skills with i
think it's important to understand that
you don't have to master every single
one of these skills here in order to
land your first job as a data analyst my
first job i landed with only the skills
of excel but i continued to progress my
skills and because of that i began
and because of that i continued to
advance in my career as a data analyst
being able to level up and get different
opportunities based on the skills i was
learning so that's my roadmap for
technical skills but what about those
other areas of analytical skills domain
knowledge and soft skills and what do i
mean by these skills and how do i
incorporate them while learning those
technical skills let's break it down
first up is analytical skills and by
this i mean things like problem solving
critical thinking research and then math
skills so i get a lot of questions
around this math skill whether more
in-depth training or studies is needed
prior to taking any courses or prior to
diving into the field of data analytics
part of my life as a data analyst i was
fortunate enough to be exposed to a lot
of different math subjects so everything
from algebra to more advanced
mathematics like calculus and
differential equations
because of this and now being my role of
data analyst i can say that the most of
the math that i've applied to my job has
been pretty basic math and has focused
on algebra probability and statistics i
don't think subjects like calculus and
discrete math are necessary especially
for entry-level data analyst roles and
the good news is that for most secondary
schools like high schools in the united
states you're exposed to subjects such
as algebra and also other subjects like
probability and statistics so based on
this i wouldn't necessarily worry that
you don't have the math skills to get
started instead if you don't know
something in math you can then learn it
or apply it in a project as you're going
along so getting back to how to apply
analytical skills in a project
when i was learning excel one of my
portfolio projects that i was working on
in school was building a food nutrition
calculator this was a spreadsheet that
could tell you what to eat in order to
be healthy this project not only
required learning excel it also applied
probability and statistics in
determining what foods to recommend
along with basic algebra in calculating
macronutrient values of food this
project was not only great for teaching
me the technical skill of excel but also
testing my analytical skills in solving
this problem of building this calculator
interesting enough this project got
brought up multiple times in different
job interviews i were in specifically by
interviewees that were interested in
physical fitness and well-being and it
was really great because it allowed me
to connect on a similar interest with
the interviewee next up is domain
knowledge and this is knowledge of a
specific discipline or field so for
example i recently asked you all what
fields you were transitioning from to
become a data analyst and the results
range from students and business and
engineering to those working in an
industry such as education and health
care from what i found you don't have to
actually switch industries or domains in
order to become a data analyst in fact
what i found is those that have the most
success in becoming data analysts apply
those newly learned data analytical
skills in the current domain or industry
that they're in as an example of this in
my first role i was working in the
procurement industry working only with
the skill of excel at the time i was
looking to improve my bi tools and an
opportunity came up to build a solution
using power bi as i had a general
understanding of this field of
procurement i was able to apply these
newly learned skills of power bi in my
role to build this dashboard but also i
was able to actually go more in depth
and learn even more about this field of
procurement so for those that are
working in an industry or maybe going to
school to learn a certain subject i
highly encourage you to take a similar
approach and dive into a tool while also
diving deeper into that domain so you
can apply those skills in a relevant
project last up is soft skills and this
relates to how you work and also
interact with other people with the
current pandemic this shifted the way
that we're interacting with each other
and instead of doing the normal
face-to-face interactions we've actually
shifted this quite differently to using
alternate forms of communication i
actually think this is a positive in
that you can actually showcase these
alternate forms of communication in your
portfolio and in the projects that you
do so what do i mean by this well when i
was learning tableau i decided to make a
youtube series documenting my learnings
these videos were not only improving my
tableau skills but also a way for me to
improve my soft skills of communication
where i was getting first hand feedback
on my presentation skills now i'm not
saying that you have to make youtube
videos per se
instead what i'm saying is that you can
use social media in order to showcase
those soft skills that you have such as
writing posts or tutorials on medium
sharing your code or processes on github
or making short form content on
instagram or tick tock all these not
only have the benefit of working on
those technical and soft skills they
also are able to be used and showcase
your experience for employers to see how
you interact with others all right so
that's my roadmap on how i've learned to
become a data analyst remember this is
not a comprehensive plan so you don't
need to learn every single skill that i
showed here today
instead i'd start small right so start
with that one technical still and add in
another skill maybe analytical or soft
skill that you want to work on and build
a project from there iterate remember
for me one skill of excel was good
enough to get my first job as an analyst
and i feel the same can apply to you as
well as always if you got value out of
this video smash that like button and
with that
[Music]
[Music]
new video from ken on how to start in
data science yes
every year i like to refresh my advice
about how i feel about learning data
science
the data domain is changing
[Music]
learn more about data science in the
upcoming year thank you so much for
watching and good luck on your data
science journey
wait a second we need a video like this
but for data analysts
what i've done nerds i'm luke a dad
analyst and my channel
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