210: Build a Data Analyst Portfolio in 9 Minutes (Full Tutorial)

Speaker: 00:00:00

This is my brother Graham.

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Hi.

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And Graham wants to

land his first data job.

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Yep.

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But he doesn't have a portfolio

that's gonna convince a hiring

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manager to take a chance on him.

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So we're gonna build him a portfolio from

scratch today to having a full working

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portfolio in less than 20 minutes.

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Sound good?

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Sounds great.

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Let's get into it.

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Okay, the tool we're going to be using

today to build a portfolio from scratch

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is called MyDatafolio, and it's a new tool

that lets you build a really beautiful

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portfolio website pretty dang quickly.

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And actually, full disclosure,

it's actually made by me.

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And it's what I would like

to have in a data portfolio.

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So link in the description

down below to try it out.

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All right.

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So the first thing that we're

going to do is set up Graham's

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profile on My Datafolio.

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Just give a name, a portfolio URL.

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We'll just do a headline of data analyst.

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And for a bio, what should your bio be?

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Something like that.

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Looks great.

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Data analyst with a BS in

statistics, located Provo, Utah.

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We'll also add a quick profile

picture, which I will just steal

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from Graham's LinkedIn even though

it's not the best photo of all time.

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There we go.

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What skills do you have, Graham?

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Python, R, Excel, Pandas, Power BI.

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All right.

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There it is.

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Power BI.

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We can add some other ones, like Claude

is another one that you have used.

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Anything else?

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ChatGPT?

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ChatGPT.

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Okay.

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Awesome.

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Uh, we'll go ahead and link to your, uh,

GitHub profile as well and your LinkedIn

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so that way people can contact you.

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And, uh, we'll go ahead

and upload your resume.

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And then what color scheme do you like?

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Let's go with the nice

forest green right there.

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Nice forest green.

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We'll leave your contact

section blank for right now.

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And do you need to do any password

protection for any of your projects?

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I don't think so.

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Do you have a custom

domain you'd like to use?

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Not at this moment in time.

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Okay, let's go ahead and hit Save Profile.

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Okay.

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And just like that, you have a

portfolio already made for you.

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Boom.

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Whoa, that's pretty cool.

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Yeah.

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But, uh, you'll notice this portfolio

is missing something pretty important.

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Any work, anything.

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Any projects, right?

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Yeah.

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So let's go ahead and,

uh, add some projects.

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So when you're adding a project, there's

three different ways that we can do it.

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The write manually, which is the way

I used to do all of my projects, um,

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but we also have two other AI features,

which is an AI import and AI-guided form.

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We're gonna focus today, just 'cause

we're in a time crunch, trying to do

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this as quickly as possible, with the

AI import, which basically allows you

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to import any sort of GitHub repos,

Tableau public links, any files

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like Excel files or Python files or

R files you've done, and write the

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first draft of your project for you.

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GitHub repos are something we can try?

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Oh, we got a couple that we can try.

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Okay.

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So let's go ahead and try the AI import.

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All right.

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This is Graham's GitHub, uh, repositories.

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It's definitely a little bit messy.

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Definitely maybe needs some

love, but, um, let's take a look.

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Which one of these repos do you feel

like could be your first project?

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Which one would be good?

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Let's start with the non-parametic

log linear medical costs.

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Okay.

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You want to start here?

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Yeah.

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Okay.

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Awesome.

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And what is...

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What exactly is this repo, I guess?

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It's a school project that delves

into different, like, information,

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data to quantify, uh, like how

smoking and different factors affect

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medical costs on an annual basis.

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Is this like a homework assignment?

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Yes.

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So like anyone who has done any

sort of homework assignment, this is

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basically just a homework assignment.

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Yes.

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And it looks like it's in Python?

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Yeah Okay, interesting.

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I don't know much about this,

so we're just gonna try it.

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So all you have to do is grab, uh, the

repo link right here, go back to our

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AI import, go ahead and give the URL.

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Is there any other details that we

should give it or any other instructions?

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I don't know.

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All right.

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Let's just go ahead and hit

generate project article.

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This will take a few seconds to read

through everything inside of this GitHub

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repo and actually do the write-up.

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All right, so it just finished

doing your project write-up here

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and made the title Nonparametric and

Log-Linear Medical Cost Analysis.

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That's an interesting name.

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Okay, we're just gonna keep it as it is.

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It gave you this URL slug.

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It gave you this summary, "A case study

that combines nonparametric techniques

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and log-linear modeling to predict

and interpret highly skewed medical

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cost data, improving forecasting

robustness and interpretability."

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Sounds pretty professional.

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That sounds very professional.

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And then here's an overview, the problem,

the approach, data and methodology,

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key findings, results and impact,

conclusion, all written for you.

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Nice.

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Let's look at the, uh, results and impact.

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It says more re- do you remember

anything about this project?

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Yeah.

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Take a look.

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Can you read these results and impact

and see if it makes sense or not?

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Yeah.

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Do you want me to read them?

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Yeah, read out loud.

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Okay.

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"More reliable budgeting, improved

forecasting accuracy on accurate

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expenditures helps f- Finance teams

set reserve level with greater confi-

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confidence Do you remember that at all?

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Uh, yes.

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There was like a, they had like standard

questions with like the data set for

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the, like the presentation project, and

we, there was like findings that there

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was like very significant correlation

between like different factors and their

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predicted- Okay ... cost difference.

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So it's, it's not necessarily wrong.

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No.

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Okay.

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What about the...

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Let's do this one, I guess, right here.

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The log linear coefficients in

the two-part decomposition allowed

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me to i- identify which variables

most strongly influence utilization

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versus conditional costs, guiding

targeted inter- interventions.

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That's also true because they're, uh,

like filtered out different factors and

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variables, and I think smoking was by

far the, like most influential factor.

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Okay.

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So it gets some of the results right.

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Um, I guess it also said that the log c-

of the cost was a more stable coefficient

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and linear relationship, so the log

was the right way to do this modeling.

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Okay, so this doesn't

feel 100% wrong to you.

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No.

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Okay, cool.

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And it's telling you kind of an overview

of the project, uh, what the problem

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is, which is predicting the medical

cost of something for like budgeting

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reasons, and then it gives you, you

know, kind of how you did the data

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exploration, you did the transformations,

then you did the modeling, and then

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basically evaluate how everything went.

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Okay, very cool.

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So, uh, we can hit save project right here

on this project, and now if you go back to

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your portfolio and you hit refresh, boom.

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Whoa.

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You got a project right here, right there.

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That's easy.

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All ready for you to- It's real easy.

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That's what I like to hear.

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Okay, awesome.

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Let's do a- another one.

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What is another, uh, project or

another repo that we should do?

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Let's do the NBA heat map.

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All right, let's do NBA

heat map right here.

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I'm just gonna copy and paste up here.

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Go back to add project, AI

import, paste this right here.

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Any other instructions?

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No, I think my .md

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files are pretty good.

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Okay.

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Generate project article, and,

uh, we'll see what it does.

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All right, it just finished.

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NBA shot heat map explorer.

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Let's see.

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So I built an NBA shot heat map

explorer to turn raw NBA shot and

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play-by-play data into actionable,

visible, intuitive insights.

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Is that what this project's all about?

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That's exactly what it's about.

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Okay, let's see.

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So then it goes through problem, the

approach, data and method- methodology.

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So you're getting the

data from the NBA API.

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Correct.

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Okay.

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And then doing some filtering,

some spatial, uh, aggregation

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with the hex bin stuff going on.

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Yep.

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Okay, awesome.

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Then you're doing some

kernel density estimators.

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Okay, great.

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Key findings, distinct ro- role

profiles are clearly displayed,

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hidden inefficiencies surface

quickly, strategic match up.

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Um, so you can do team

level heat maps to show.

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Okay, very cool.

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Visual artifact.

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Very nice.

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So, um, obviously we're just pulling

straight from the GitHub, right?

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So it just has whatever you have- in

here, which I'm guessing it doesn't

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have, like, any saved images, right?

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Not in that folder particularly, no.

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Okay.

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See, well, that's something you could

have told me earlier when I said,

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"Do you wanna add anything else?"

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Well, okay, now you know.

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We can, we can actually, like, go

in and add those images as well.

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Um, so that would help you.

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So let's go ahead and hit Save Project.

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Let's go back to our portfolio, and

let's hit refresh on the full portfolio,

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and boom, you got two projects.

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Now, I did see that on your

LinkedIn the other day you had

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posted about this project, right?

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Yes.

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So let's see.

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Here's your LinkedIn page, and here's

the image I saw that you posted.

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Mm-hmm.

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I'm gonna actually right

click on this image, and I'm

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gonna go back to our project.

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I'm gonna go to the Heatmap Explorer here.

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I'm gonna upload that image

as a cover image right here

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and, uh, see how it looks.

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Whoa.

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Let's go back, refresh.

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Oh, that looks way better.

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Boom.

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You open it up, it actually

includes that image at the top now.

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So I like that.

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Do we have any other images

on your LinkedIn of this?

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No.

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Boom.

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Not yet.

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Not yet.

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Okay, sweet.

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This is a Project and Portfolio.

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It shows what different libraries you

used in Python and obviously Python here.

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At the top it w- will allow people

to view your code, and you have

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your full write-up down here.

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Um, it allows people to see

other projects, so here's

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your other project once again.

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Um, here's the different

libraries you used here.

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Um, and then you can always have

your users go back to your portfolio.

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You can send this to people.

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You can try dark mode or light mode.

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It has your GitHub, your LinkedIn,

your resume, your little summary, your

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different skills up here at the top,

your projects, and then a call to action

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down here at the bottom to work together.

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That's awesome.

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Thank you so much.

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That's gonna be very

helpful for me, I think.

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Okay.

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The other thing I wanted to show you

is it actually, we have these KPIs

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here for the pro plan of MyDatafolio,

which actually shows you how many page

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views you have and how many visitors.

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So I'm the only person who's

visited, so it's the one.

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So far.

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But, like, basically it'll let you

see that this has four views, this

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has one view, so on and so forth.

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Um, that...

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This is kind of exciting-

Ooh ... 'cause when someone actually

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looks at it, you'll, you'll know.

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Yeah, you can actually see if,

like, a recruiter or a hiring

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person is actually looking at it.

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Exactly.

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So you can always edit the projects,

share a n- unique project, and share

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your portfolio from right here.

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That's awesome.

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I'm excited to actually use this and get

in there and edit a few things around.

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All right.

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There you have it, folks.

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I don't know how many minutes that

took, but hopefully less than 20.

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And Graham went from having no portfolio,

just, like, some loose homework projects

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or some projects that he's done in GitHub.

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You can even just upload a file,

for instance, in up- add projects.

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You can actually just upload, like,

your Python file or your Excel file

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and it will try to do its best.

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Obviously, the more information

you give it, the better it'll do.

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But hopefully that gets you guys

excited to go try out MyDatafolio.com

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and try it out for themselves.

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Yeah, I'm excited to go and actually try

and apply to a few more jobs with this.

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All right.

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Link in the description.

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Trust it out.

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Let me know what you guys think.

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