Hiring Manager Explains: Data Portfolio Do’s and Don’ts

Summary

Christine, a seasoned data analyst turned data director and hiring manager, advises early-career data analysts on building a portfolio that showcases business-relevant insights and technical skills. She emphasizes the distinction between learning projects and those meant for showcasing, urging job seekers to focus on projects that apply technical skills to real business questions. Christine provides a roadmap for creating impactful portfolio projects, including using GitHub for showcasing work and tailoring resume bullet points to reflect the project's value. She also offers insights on how to effectively discuss these projects in interviews.

Takeaways

• 😀 Building a portfolio is crucial for early career data analysts, especially for those transitioning from other industries.
• 🔍 Projects in the portfolio should demonstrate business-relevant insights and the application of technical skills to stand out.
• 📚 There's a distinction between projects for learning and those for showcasing; the latter is essential for job applications.
• 🚫 Avoid generic projects like Google Analytics case studies, as they are too common to make an impact in today's job market.
• 🛠 Focus on projects that show the application of technical skills to solve real business questions, highlighting business metrics and insights.
• 📈 Use GitHub for portfolio projects to demonstrate familiarity with the data ecosystem commonly used by data analysts.
• 📝 Include a README file in each GitHub repository that outlines insights, recommendations, and the context of the project.
• 📊 Create dashboards using industry best practices for simplicity, clarity, and cleanliness to showcase your data storytelling skills.
• 💼 Tailor the portfolio to reflect the type of business model you're interested in, making it widely applicable to your target industries.
• 📋 Ensure the resume includes impactful bullet points that highlight the tools used, the metrics analyzed, and the insights provided.
• 🗣 Prepare for interviews by being able to discuss the data cleaning process, challenges faced, and how insights were discovered and communicated.

Q & A

What is the main purpose of building a portfolio for early career data analysts?

The main purpose of building a portfolio for early career data analysts is to demonstrate their ability to apply technical skills to business-relevant questions and to stand out from the competition, especially when transitioning from another industry.

Why are some online resources not sufficient for building a strong portfolio?

Some online resources are not sufficient because they often do not emphasize the importance of creating projects with business-relevant insights and do not show how to apply technical skills to real-world business scenarios.

What is the difference between portfolio projects for learning and for showing?

Portfolio projects for learning are those done to get comfortable with tools like Excel, SQL, and Tableau, and are primarily for personal development. Projects for showing, on the other hand, are meant to demonstrate the application of technical skills to business questions and should include business insights and recommendations.

Why is it not recommended to use Google's data analytics certificate projects as part of your portfolio for showing?

It is not recommended because such projects are often generic and commonplace, which do not help in standing out in the job market. They are more suitable for the learning phase rather than showcasing advanced skills and business insights.

What are the critical dos and don'ts of portfolio projects that Christine mentions?

The dos include focusing on projects that show the application of technical skills to relevant business questions, demonstrating understanding of business metrics, and providing insights and company recommendations. The don'ts include creating generic project names, focusing solely on the technical process without insights, and not using GitHub to showcase projects.

Why should a portfolio project not just focus on the technical tools used but also on the insights and recommendations derived from them?

Focusing only on the technical tools used does not demonstrate the candidate's ability to apply these tools to solve business problems. Insights and recommendations show a deeper understanding of the business context and the ability to communicate findings effectively.

What is the significance of using GitHub for portfolio projects?

Using GitHub is significant because it is a common tool in the data ecosystem used by data analysts to store and share projects. It shows familiarity with industry-standard tools and provides an accessible platform for hiring managers to review work.

How should the structure and design of a Tableau dashboard in a portfolio project reflect industry best practices?

The structure and design of a Tableau dashboard should be simple, clear, and clean, following industry best practices. It should include tables for reporting, line graphs for trend analysis, and mix graphs for distribution analysis, making it easy for non-technical audiences to understand.

What is an example of a company-relevant portfolio project that Christine provides?

An example of a company-relevant portfolio project is the analysis of subscription data from Zoom, focusing on overall trends and recommendations for the marketing and sales team. It uses a widely applicable business model and provides insights and recommendations based on the analysis.

How should insights and recommendations be presented in a portfolio project to effectively communicate business value?

Insights and recommendations should be presented in a way that is directly related to the business metrics and dimensions, focusing on the 'so what' of the findings. They should guide people on where to spend time investigating further, rather than providing conclusive recommendations.

What is an example of an impactful bullet point for a resume that reflects a portfolio project?

An example of an impactful bullet point is one that not only mentions the tools used but also highlights the business metrics, insights, and recommendations, such as 'Conducted analysis in SQL to surface insights on sales trends and SaaS metrics for a self-created Zoom data set containing 100K subscription records.'

How should a portfolio project be structured on GitHub to make it accessible and representative of real-world data analyst work?

A portfolio project on GitHub should have one to three public repositories, with one repository per project. Each project should include a README file that walks through the insights and recommendations, and provides context as if the candidate is an actual data analyst at that company.

What kind of interview questions should a candidate be prepared to answer when discussing their portfolio projects?

Candidates should be prepared to answer questions about the data cleaning process, challenges encountered, interesting insights discovered, how to make insights understandable to non-technical audiences, and questions about the structure and design of a Tableau dashboard they built.

Outlines

00:00

📚 Building a Portfolio for Early Career Data Analysts

Christine, a seasoned data professional, emphasizes the importance of creating a portfolio that showcases not just technical skills but also business insights. She explains that while many resources suggest building a portfolio, they often overlook the need for business relevance. Christine stresses the distinction between projects for learning and those for demonstrating expertise. She advises focusing on projects that apply technical skills to answer business questions, understand business metrics, and offer company recommendations. She also discusses common pitfalls in portfolio projects, such as generic project names and a lack of insights, and the importance of using GitHub to reflect the real data ecosystem used in the industry.

05:01

🚀 Crafting a Standout Data Analyst Portfolio

Christine provides guidance on how to create a portfolio that can help early career data analysts stand out. She critiques common portfolio mistakes, such as using generic project names and focusing too much on the tools used rather than the insights gained. She advocates for using GitHub to host projects, making them accessible and reflective of industry practices. Christine also shares an example of a strong portfolio project, analyzing Zoom subscription data to identify sales trends and offering actionable recommendations. She highlights the importance of simplicity and clarity in dashboard design and the need to demonstrate storytelling with data and the ability to communicate insights to non-technical audiences.

10:02

💼 Demonstrating Data Analysis Skills in Portfolio and Interviews

Christine discusses how to effectively demonstrate data analysis skills through a portfolio and during interviews. She advises creating impactful resume bullet points that highlight the use of tools, business metrics, and the impact of the analysis. Christine also suggests preparing for interview questions that delve into the data cleaning process, insights discovered, and the ability to communicate findings to non-technical stakeholders. She emphasizes the importance of using GitHub for portfolio projects, linking it on the resume and LinkedIn profile, and being ready to discuss projects in detail during interviews. Christine invites viewers to engage with her content, offering to address specific struggles and provide further insights into portfolio projects and applying technical skills to real business scenarios.

Keywords

💡Portfolio

A portfolio in the context of this video refers to a collection of projects that demonstrate an individual's skills and experience in data analysis. It is crucial for early-career data analysts to showcase their technical abilities and business insights. The video emphasizes the importance of having a portfolio that not only displays technical skills but also shows how these skills are applied to solve business-relevant questions.

💡Business Relevant Insights

Business relevant insights are the actionable and strategic conclusions drawn from data analysis that can inform business decisions. In the video, it is stressed that a portfolio project should go beyond technical proficiency and provide meaningful insights that can be applied in a business context. This helps to demonstrate the candidate's understanding of how to leverage data to drive business outcomes.

💡Technical Skills

Technical skills in this video pertain to the specific tools and methodologies used in data analysis, such as SQL, Excel, and Tableau. The script discusses the necessity of showing how these skills are applied to answer business questions, rather than just showcasing the ability to use the tools themselves.

💡Data Analyst

A data analyst is a professional who collects, processes, and interprets data to help organizations make decisions. The video provides guidance on how to become a data analyst, including the construction of a portfolio that reflects the necessary skills and insights to stand out in the industry.

💡Hiring Manager

A hiring manager is responsible for the recruitment process, including reviewing applications and making hiring decisions. The video script is from the perspective of a former data director and hiring manager, offering insider advice on what hiring managers look for in a data analyst's portfolio.

💡GitHub

GitHub is a platform used for version control and collaboration that is widely recognized in the tech industry. The video suggests using GitHub to host portfolio projects to demonstrate familiarity with tools commonly used by data analysts in a professional setting.

💡SAS Metrics

SAS metrics refer to the key performance indicators used by Software as a Service (SaaS) companies. In the context of the video, the speaker uses a project analyzing subscription data from Zoom, a SaaS company, as an example of applying technical skills to a business-relevant context.

💡Tableau

Tableau is a data visualization tool used to create interactive dashboards and reports. The video mentions building a Tableau dashboard as part of a portfolio project to visually represent data trends and insights, showcasing the candidate's ability to communicate data effectively.

💡Resume

A resume is a document used by job applicants to present their skills, experience, and education. The video provides an example of how to effectively represent portfolio projects on a resume, ensuring that the candidate's experience and insights are communicated clearly to potential employers.

💡Interview

An interview is a meeting where a candidate is assessed for a job position. The video script discusses preparing for interview questions related to portfolio projects, emphasizing the importance of being able to articulate the process, insights, and recommendations derived from one's work.

💡Learning Projects

Learning projects are those undertaken for the purpose of gaining proficiency in a particular skill or tool. The video distinguishes between projects done for learning and those for showing, with the latter being more relevant for a professional portfolio intended to demonstrate readiness for a job in data analysis.

Highlights

The importance of building a portfolio for early career data analysts, especially for those transitioning from other industries.

The distinction between portfolio projects for learning and for showcasing business-relevant insights.

Christine's background in analytics, including her roles as a data analyst, consultant, and hiring manager.

The ineffectiveness of generic projects like Google Analytics case studies for standing out in the job market.

The necessity of demonstrating the application of technical skills to business questions in portfolio projects.

The significance of using GitHub for portfolio projects to showcase understanding of the data ecosystem.

The pitfalls of overly complex or personal projects that lack relevance to potential employers.

The importance of clarity and simplicity in project presentation, as opposed to complexity.

Christine's example of a stellar portfolio project analyzing Zoom subscription data for marketing insights.

The use of industry-relevant business models in portfolio projects to increase applicability.

The structure and design of a Tableau dashboard following industry best practices.

The emphasis on insights and recommendations over technical details in project write-ups.

The value of storytelling with data and the ability to communicate with non-technical audiences.

The role of data analysts in guiding teams based on data insights rather than providing conclusive recommendations.

Tips for representing portfolio projects effectively on resumes with impactful bullet points.

Preparing for interview questions about data cleaning, insights discovery, and dashboard structuring.

The upcoming free live workshops by Christine for further insights into portfolio projects.

Transcripts

so many online resources said that you

need to build a portfolio to become an

early career data analyst especially if

you're transitioning from another

industry but a lot of these resources do

not tell you that if these projects

don't have business relevant insights

and don't show how you actually apply

your technical skills to business

relevant questions they're not actually

going to help you stand out from the

stack my name is Christine and I worked

in analytics since 2015 starting out as

a data analyst in healthcare Tech

Consulting and eventually becoming a

data director and hiring manager where

I've helped hire interview and train

many analysts over the years in this

channel I'm going to be bringing you

unique insights from inside the industry

so that you can understand the most

effective road map to becoming a data

analyst today something that's really

important for you to remember when it

comes to portfolio projects is that

there's a difference between projects

that are for Learning and then projects

that are for showing so the first bucket

are projects that you do when you're

getting comfortable with Excel SQL

Tableau python are is not really

commonly used in the industry anymore so

sorry to those you guys who did the

Google data analytics certificate I will

talk more about this in another video

but just know that if you are focusing

on projects in this bucket that are

clearly in the learning phase they're

not going to get you past the resume

wall you actually need to focus on

projects that actually show how you

apply your technical skills to relevant

business questions by demonstrating your

understanding of business metrics

insights and Company recommendations to

actually stand out from the stack so in

the next few minutes I'm going to walk

through a few critical dos and don'ts of

what these kind of projects actually

look like and then towards the end I

will show you a stellar portfolio

project that you could use to stand out

to hiring managers and also talk about

how you should actually demonstrate this

on your resume and what interview

questions you should prep for when

talking about these projects I see a lot

of projects that look like this this or

this and these projects are just not

going to cut it in today's job market

and let me tell you why so in the first

project we have a Google data analytics

case study we have SQL project one SQL

project 2 and then Excel project so you

can probably already guess some of the

things that I'm going to say about this

portfolio one of them is that the Google

analytics case study is just too generic

and common place to stand out in today's

job market so I really don't recommend

doing this as an actual portfolio

project for the showing bucket do it as

a project more so for the learning

bucket if you are doing that certificate

then of course the project names are

just way too generic this is not how we

would be talking about projects at work

we would actually be talking about

projects at work probably using the

company name or using some kind of

business metric or team name and so you

should do the same when you're actually

talking about your portfolio projects

you can see that the projects the write

up also focuses on what this person did

it talks about using SQL Excel to write

queries and calculate certain things but

I'm not actually interested in the fact

that you use these tools I care a lot

more about why you use these tools and

what you use the tools to discover so

it's completely devoid of insights and

recommendations in this writeup and

that's something that you need to have

to be able to stand out and show your

understanding of how you actually use

use these tools as a cohesive system on

the day-to-day job the other thing is

that this is a personal website that is

built on top of the GI of UI but it

doesn't necessarily look like that nice

so it doesn't actually help you to build

a personal website if it looks like this

you should actually be using GitHub

because if you don't it's a lost

opportunity to show that you know the

real data ecosystem we use GitHub all

the time as data analyst to store our

projects and so the more your portfolio

can represent the way that we actually

use these tools on the job the better so

here i' would have to click through many

different links to actually get to the

meat of the project I would first have

to click on the header then it brings me

to a Google drive folder where then I

have to find the right file and then

once I get to the right file I have to

click on that file for a high manager

who's going to be looking at your

portfolio for honestly like maybe a few

seconds if they even get there you

should surface all the important stuff

in a really accessible way so that it's

right there for them when they go to

your portfolio this project is focused

on writing SQL qu to understand player

performance in the US Open and the

introduction to the project is kind of a

personal story about why this person was

interested in looking at the US Open

metrics and then the project itself is

just focused on showing the queries and

then showing the output of these queries

this overall to a hire manager it is

very clearly a learning project because

there aren't any company relevant

metrics here that's not really clear to

me what the so what is for why I want to

understand player performance in the US

Open for example example if I'm working

in sales or marketing or product or

Finance by showing just the SQL query

and then the output of that query we

know that so many people know how to

write SQL these days that you need to go

beyond what's actually in the query and

the output of the query and talk more

about the so what of what you calculated

in this project one of my students had

initially focused on Dungeons and

Dragons analysis where he was using

Excel to look at different stats of the

different elements and weapons in the

game to a hiring manager this is very

obviously also a learning project

because it has such a personal and Niche

interest where if I was applying to any

other company than the company that

developed Dungeons and Dragons it's

probably not going to be that relevant

to me you can also see that he uses

Excel functions like dropdowns

conditional formatting color coding and

there's kind of a lot going on in the

spreadsheet where it's hard for me to

actually see what's important this is

not industry best practice where at work

we care a lot more about Simplicity and

Clarity rather than complexity so what

does the company relevant portfolio

actually look like first off make sure

that you have a GitHub you can add a

picture have a quick bio and have one to

three repositories where in every single

repository you have a read me that

actually covers your insights and

recommendations in this project from one

of my workshops I analyzed subscriptions

data from Zoom where I was looking at

overall Trends and recommendations for

the marketing and sales team since 2020

and the business question I was

answering is essentially what are the

trends in sales and how does this differ

across key customer segments what areas

do you recommend we look further into to

improve sales over time the data is a

mix of madeup data I blended from kagle

and chat GPT and it's available for you

to download through my GitHub which is

linked in the description below right

away this is a lot more company relevant

because Zoom is a SAS company and SAS

businesses often share similar Northstar

metrics so use a business model that is

widely applicable to the kinds of

industries that you're actually

interested in in the read me I give some

context on the company from the

standpoint of a data analyst actually

working at that company so it's as if

I'm presenting to the marketing or sales

team notice that I didn't dive into the

technical details and process I'm going

right into the Northstar metrics the

insights and the recommendations if we

look at the Tableau dashboard it's also

using industry best practices on

dashboard structure and design in terms

of its Simplicity Clarity and

cleanliness here I see that you not only

Built tables for reporting line graphs

for Trend analysis and mix graphs for

distribution analysis and from this I

can learn a lot more about your business

thinking in terms of the insights and

recommendations a lot of the analysis in

this project is founded on taking a key

business metric like sales and then just

slicing it by key Dimensions like plan

type plan region and plan period and

then highlighting these ups and downs

and outliers in an easy to understand

way and this tells hiring managers a lot

of other things like the fact that you

know fundamentals of Storytelling with

data you can separate highlevel facts

from low-level facts and you can also

communicate to non-technical audiences

for recommendations we're not

necessarily looking for something that

is extremely mathematically complex or

even that conclusive it doesn't have to

be something like and therefore we

should stop selling this product once

and for all it can be something that's

more along the lines of where people

should spend their time investigating or

further looking into the data so for

example work with the product and sales

team to understand why there's a dip in

this specific plan type over the last 3

months this represent how data analysts

actually work with people on the job

it's not necessarily our job to come up

with a final recommendation but rather

to guide People based on what we're

seeing in those numbers so overall this

project stands out a lot more because it

has business relevant metrics and

dimensions it focuses on insights and

recommendations so I understand what

value you would actually be bringing to

the team when you're using these tools

and it has a readme and a GitHub profile

and it uses more than one tool at a time

to demonstrate that you know how to use

the real data ecosystem that we use at

work to be honest with you guys hiring

managers are not going to be spending a

ton of time actually looking through

your portfolio if they even get there

instead it's more important for you to

focus on building these projects so that

you have rigorous enough experience for

you to actually talk about when you get

to the interview stage you do however

need to be able to represent these

projects well on your resume if they're

actually going to help you when you're

actually applying to jobs so here's an

example of an impactful bullet that does

this project Justice conducted analysis

in SQL to to surface insights on sales

Trends and SAS metrics for a

self-created zoom data set containing

100K subscription records worked

independently for 3 weeks to clean and

analyze data in SQL and built

performance dashboard in Tableau to

visualize Trends related to plan types

regions and plan periods surface

insights and recommendations geared

towards sales and marketing teams

focusing on monthly promotions and

Enterprise plans so notice that I not

only talk about the tools here but I

mentioned the actual metrics and I also

include important keywords like SAS

metrics sales and marketing team so in

your final portfolio this is how your

project should look in GitHub have one

to three public repositories with one

repo per project and for each project

have a readme file that walks through

those insights and recommendations and

gives context on what you're doing as if

you're an actual data analyst at that

company also make sure that your GitHub

is linked in your resume and that it's

an actual clickable link so that someone

can go to it very easily and also

include your GitHub in your LinkedIn

profile so that when you reach out to

data hiring managers which I will talk

about in a future video in terms of how

to do that effectively people can really

quickly go to that portfolio and get a

sense for your skills when talking about

your portfolio projects in early career

data analyst interviews be prepared to

answer questions like how did you clean

this data set and what were some of the

challenges that you bumped into what are

some of the more interesting insights

that you discovered and how did you find

them how would you make sure that your

insights are understandable to

non-technical audiences and then also

walk me through a tableau dashboard that

you built and tell me more about why you

structure the dashboard in this way

leave a comment below if you want to

understand how to give standout

responses to these kinds of interviews

because I know that these kinds of

questions can be a bit tricky if you

haven't actually worked as a data

analyst before so I have a lot more to

share about portfolio projects and how

to actually apply your technical skills

to real business questions and business

thinking on the job but that's all we

have time for today I am going to be

doing some free live workshops soon so

just check out the description to see if

there's one coming up if you have

questions or comments please just drop

me a note below I will read through

every single one and I would love to

hear from you directly on what exactly

you're struggling with and what you

would like to see more content on so

don't forget to subscribe and I'll see

you soon