How I Would Learn to be a Data Analyst

Summary

In 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

• 📚 Start with a learning process that involves both acquiring knowledge and applying it immediately to solidify the skill.
• 🔍 Focus on four main areas for a data analyst: technical skills, soft skills, analytical skills, and domain knowledge.
• 🔧 Technical skills are recommended to start with as they are tangible and can be built upon with other skills like writing or communication.
• 📈 Excel and SQL are identified as the most important technical skills for entry-level data analysts, based on job posting analysis.
• 🛠️ Use tools like Coursera for learning and also for showcasing projects that can be added to a resume for job applications.
• 📊 Begin with a broad overview of tools to understand which ones align with your interests and passions.
• 🎓 The Google Data Analytics Certificate is suggested for gaining a general understanding of popular tools and their applications.
• 📝 Incorporate analytical skills like problem-solving and critical thinking into projects to apply and improve these abilities.
• 🏢 Domain knowledge is beneficial, and applying data analysis skills within your current industry can lead to success.
• 🗣️ Soft skills like communication are important and can be showcased through social media or other platforms.
• 🔄 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

00:00

📚 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.

05:02

🛠️ 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.

10:04

🌐 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.

Keywords

💡Data Analyst

A data analyst is a professional who collects, processes, and performs statistical analysis on data to help businesses make informed decisions. In the video, Luke, who identifies as a data analyst, shares his pathway to becoming one and offers advice on the skills needed for the role. The term is central to the video's theme as it sets the context for the career journey and skills discussion.

💡Technical Skills

Technical skills are specialized abilities that are required to perform specific tasks or jobs, often related to the use of technology or tools. In the context of the video, Luke emphasizes the importance of technical skills such as Excel and SQL for data analysts, stating that they are the most important skills to learn based on job posting data. These skills are integral to the video's message about the necessary competencies for a data analyst.

💡Soft Skills

Soft skills refer to personal attributes that enable someone to interact effectively and harmoniously with other people. In the script, Luke mentions soft skills such as communication and writing, which are important for data analysts to effectively convey their findings. The video suggests that while technical skills are crucial, soft skills are equally important for career success.

💡Analytical Skills

Analytical skills involve the ability to assess information, draw conclusions, and solve problems. Luke discusses the necessity of analytical skills like problem-solving and critical thinking for data analysts. He also touches on the importance of math skills, particularly algebra, probability, and statistics, which are used in data analysis projects.

💡Domain Knowledge

Domain knowledge refers to the understanding and expertise in a specific area or field. In the video, Luke suggests that having domain knowledge can enhance a data analyst's ability to apply their skills effectively within a particular industry. He shares that applying newly learned analytical skills within one's current domain can lead to success in a data analyst role.

💡Iterative Process

The iterative process mentioned in the video is a cycle of learning and applying skills that is repeated to progressively improve and build upon knowledge. Luke recommends an iterative two-step approach of learning and then using the skills, which is a key part of his recipe for learning anything and is essential for retaining and showcasing skills.

💡Coursera

Coursera is an online learning platform that offers courses, certificates, and degrees. In the script, Luke highlights Coursera for its role in the learning process, particularly for the first step of acquiring new skills. He also mentions the platform's projects that allow learners to apply their knowledge, which aligns with his recommended learning and applying process.

💡Portfolio Projects

Portfolio projects are pieces of work that individuals create to demonstrate their skills and experience. Luke advises using portfolio projects as a means to apply newly learned skills and to showcase one's abilities to potential employers. In the video, he gives an example of creating a food nutrition calculator using Excel to illustrate this concept.

💡BI Tools

BI Tools, or Business Intelligence Tools, are software applications used to analyze data and present critical information to help make better business decisions. In the video, Luke identifies Tableau and Power BI as popular BI tools for data analysts. He suggests learning these tools as part of the technical skill development for a data analyst.

💡SQL

SQL, or Structured Query Language, is a domain-specific language used in programming and designed for managing data held in a relational database management system. Luke emphasizes SQL as one of the most important technical skills for data analysts, as it is frequently required in job postings and is essential for data manipulation and retrieval.

💡Excel

Excel is a widely used spreadsheet program that offers data organization, analysis, and visualization features. In the video, Luke mentions Excel as a foundational technical skill for data analysts, noting that it is a popular requirement in job postings and a good starting point for building a career in data analysis.

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