How to Approach Learning the Data Analyst Skillset

Participate on LinkedIn

There’s a vibrant community of data professionals that hang out on LinkedIn.
‍
Many experienced data analysts share content and resources that can be helpful in your learning journey.
‍
If you don’t have a LinkedIn yet, sign up and then make sure to complete your profile.
‍
You can follow the instructions in this video for setting it up.
‍
Next, start connecting with or following data analyst creators (like me and Kyle) on LinkedIn.

• Albert Bellamy
• Andrew Madson
• Amna Ahmed
• Alex Freberg
• Andy Kriebel
• Ankit Bansal
• Avery Smith
• Annie Nelson
• Brent Dykes
• Charlotte Chaze
• Chris French
• Christian Wanser
• Carly Taylor
• Christopher Chin
• Cody West (i.e. me)
• David Langer
• Diane Grair
• Deb Ashby
• Dhaval Patel
• Dylan Anderson
• Eamonn Cottrell
• Gerard Duggan
• Gilbert Eijkelenboom
• Ian Klosowicz
• Jess Ramos
• Joe Squire
• Jonathan Smith
• Kyle Malone
• Kat Greenbrook
• Kedeisha Bryan
• Kelly Adams
• Lauren Rosenthal
• Luke Barousse
• Matt Mike
• Megan Lieu
• Munna Das
• Matt Brattin
• Princilla Abena Koranteng
• Richard Nero
• Sonali Kumar
• Sundas Khalid
• Thais Cooke
• Vyas Adithya

Here’s my biggest piece of advice to get the most out of LinkedIn: Engage by liking and commenting on the posts of creators.
‍
Ask questions, provide your thoughts, show appreciation if a piece of content was helpful for you, etc.
‍
You don’t need to post on LinkedIn, but make a habit of commenting on ~3 posts per day.
‍
The more you put in, the more you’ll get out.
‍
If you start doing this right when you start learning, you’ll have MANY more opportunities present themselves by the time you start searching for jobs.

Create learning time blocks

When most people start something new, they go about it all wrong.
‍
Do NOT “try to find time” each day to learn analytics.
‍
That will never work.
‍
Remember, habit is everything.
‍
The best approach for most people is to schedule a 90-180 minutes time block at the same time every morning.
‍
Mornings are best because you get your learning time in BEFORE the world starts throwing things at you.
‍
If you try to schedule learning time during the day, life inevitably gets in the way.
‍
Meetings pop up on your calendar, child emergencies, work emergencies, etc.
‍
You might need to wake up earlier than you normally do to make this happen.
‍
Which means you might need to go to bed earlier.
‍
I’m religious about my morning focus time.
‍
I schedule a 2-hour time block every single morning for “deep work” (i.e. focused work that required my full attention and concentration) and don’t let anything disrupt this.
‍
My guess is 80% of the results I get in my work come from just this 2-hour time block.
‍
I like time blocking my entire day and use Google Calendar to do it.
‍
Here’s an example of my calendar (it looks a little different now that I’m at Cash App):

Do I always follow my time blocks?
‍
No, I’m only human.
‍
But I follow them the majority of the time.
‍
For a minority of people, evenings are best for their schedule.
‍
But regardless of if it’s the morning or evening, the point is to be consistent about the time.
‍
If you want to learn more about time blocking, check out these resources:

• Here’s a video that you can watch
• The complete guide to time blocking

Subscribe to supplemental learning resources

In the next section, I’ll be providing you with a curriculum and roadmap for learning all the skills you’ll need to become a Data Analyst.
‍
But there are a few supplemental learning resources I recommend subscribing to that will help assist in your journey.
‍
Start with these 3 to avoid information overload:

• The Query Newsletter: This is a newsletter Kyle and I put together and send out every Thursday. You’ll receive our favorite learning resources, SQL tips, hilarious data memes, and more!
• Alex the Analyst YouTube Channel: Alex puts a lot of effort into his videos which are an excellent way to supplement your learning.
• /r/dataanalysis: This subreddit is fairly active and there are great resources and discussions about learning data analysis

Now that you’ve set yourself up for success, it’s time to relearn how to learn.
‍
Why?
‍
Because you’ve probably picked up a lot of bad learning habits from school.
‍
School incentivizes you to get good grades by passing tests.
‍
Passing tests requires you to memorize, not learn.
‍
Memorizing won’t help you become a data analyst — you have to actually learn.
‍
So how do you learn?
‍
The best book on learning how to learn is called Ultralearning By Scott Young.
‍
The most important principle from the book in the context of learning data analysis is directness.
‍
Directness means diving right in and obtaining hands-on experience actually doing data analysis versus spending your time reading or watching courses.
‍
Reading and taking courses are convenient and comfortable activities that have their place.
‍
But hands-on, project-based learning is where you should be spending most of your learning time.
‍
Most people avoid this type of learning because it’s hard and uncomfortable.
‍
Often you’re struggling and moving forward slowly.
‍
If you struggle too much, it can make you frustrated which can be detrimental to learning.
‍
The trick is to learn from books and courses just enough so that you can apply the skills directly.
‍
With that in mind, let’s talk about how to structure your learning.

Recommended learning structure

With directness as a key principle in mind, here’s the learning structure I’ll be providing you in the next section for learning things like spreadsheets, SQL, and Tableau:

1. Spend ~1-2 weeks on a course to get a brief overview of the basics
2. Spend ~1-2 weeks on guided tutorials
3. Spend ~4 weeks on portfolio projects

Courses & Guided Tutorials

Courses are useful for starting your learning journey when you know nothing about a topic.
‍
They allow you to dip your toes in and build some foundational understanding.
‍
But courses are not usually effective for learning beyond basic concepts because they lack application.
‍
I recommend only spending a week or two on courses for each new skill you’ll be developing.
‍
In the next section of this guide, I’ll link to the courses I recommend you take.
‍
Once you’ve developed a rough and foundational understanding of a new topic like spreadsheets or SQL, the next step is to begin applying your new knowledge.
‍
It’s difficult to apply new knowledge without guardrails, which is where guided tutorials come in.
‍
Guided tutorials help you apply your new skills, but are guided so the learning process doesn’t feel too painful.
‍
You’ll get practice going through the motions of a new skill before you apply it without any help.
‍
In the next section of this guide, I’ll also link to the guided tutorials I recommend you go through.

Portfolio Projects

It’s time to take the training wheels off and apply what you know without the help of guided tutorials.
‍
This is the step where your learning will accelerate.
‍
When you don’t have someone to guide you step by step, you realize there is a lot of stuff you thought you knew, but now you forgot.
‍
That is ok.
‍
This phase is all about getting those reps in and doing data analysis without training wheels.
‍
It’ll feel hard and uncomfortable, but that’s how you’ll know your learning.
‍
Learning isn’t easy, so it shouldn’t feel easy.
‍
There’s a catch-22 that exists when you’re trying to get your first job as a data analyst.
‍
It’s difficult to get a job as a data analyst without experience, but it’s difficult to get experience as a data analyst without a job.
‍
The answer?
‍
A personal data portfolio.
‍
When you start looking for jobs, a portfolio of projects you’ve created is crucial for setting yourself apart from all the other people trying to break into data.
‍
A portfolio SHOWS you have the skills of a data analyst, rather than you relying on having to TELL an interviewer you have the skills.
‍
Showing is 100x more convincing than telling.
‍
Over the next 6 months, you’ll know you’ve learned data analysis because you’ll have a portfolio of projects to prove it.
‍
You can use your portfolio to land interviews and show recruiters and hiring managers you’re qualified for the job.
‍
In the next section, we’ll discuss portfolios in depth.

Books to support your learning

I wouldn’t rely on solely books for learning data analysis because so much of the actual work of a data analyst happens on a computer.
‍
Books can be a helpful supplementary resource, but most people don’t read effectively.
‍
Here’s the counter-intuitive approach I recommend for reading to learn data analysis:

• Read data analysis books to wind down before bed.
• Use the book you’re reading as an exploratory resource. Don’t feel like you need to memorize anything. Follow your curiosities.
• If something is boring to you, skip it. Don’t feel like you need to read all the way through.
• Bounce around from chapter to chapter. Read whatever peaks your interest.
• If something resonates with you when you read it, underline or highlight it for quick reference in the future.

Your goal by reading leisurely before bed is to keep your brain thinking about data analysis so that when you sleep, your brain can make new connections and solidify new learnings.
‍
You don’t need to do this to learn data analysis, but it will help speed things up.
‍
Here are the books I recommend picking up:

• How to Measure Anything: Finding Value in Intangibles in Business
• Lean Analytics: Use Data to Build a Better Startup Faster
• Naked Statistics: Stripping the Dread from The Data
• Joe Squire’s Data Analyst Field Manual (free ebook)
• SQL Quickstart Guide

Finding answers to your questions

This is THE most important part of learning something new.
‍
Learning is all about:

• Identifying what you don’t understand
• Formulating what you don’t understand into a question
• Asking that question to a variety of resources
• Receiving an answer that continues building upon your knowledge

• Google
• ChatGPT
• Your LinkedIn network

Here’s an example of what this would look like in practice.
‍
Say you’re taking a course in the Google Data Analytics Certification and you don’t understand how the different SQL joins work.
‍
First, Google something like, “How do the different types of SQL joins work.”
‍
Read 2-3 resources.
‍
Here’s a guide on how to Google effectively (yes this is a skill that you should learn).
‍
If there are no good resources, ask ChatGPT.
‍
Here’s an example prompt:
‍
“Explain to me like I’m a college undergrad how SQL joins work and why they’re important. Provide me with a few examples.”

When it comes to prompting ChatGPT, be specific.
‍
Here’s a guide on how to use ChatGPT and effectively prompt it.
‍
You’ll probably have answered your question by now, but if you haven’t, reach out to one of the people you’ve been engaging with on LinkedIn.
‍
When you ask a question, make sure the question is VERY specific and provide as much context as possible.
‍
Also, ask your question using as few words as possible, while still providing enough information and context.
‍
You don’t want someone to have to read a novel to help you, but you don’t want them to have to ask you 3 follow-up questions before they can answer. The name of the game here is reducing friction for the person you're asking as much as possible.
‍
If you want to learn more, check out this guide on asking better questions.
‍
Here’s a hack to increase the rate of response you get: ask your question on someone’s LinkedIn post, do NOT DM them.
‍
If you have a question, that means someone else also has that same question. When you ask your question publically, more people get to benefit from the answer.
‍
Creators will always prefer helping thousands of people with their content rather than one.

Active learning and “checking in”

When you’re learning from courses or other resources, you want to be actively learning and checking in with yourself.
‍
After a concept is presented, ask yourself, “Does that make sense?” If it doesn’t, rewatch the section again.
‍
If it still doesn’t make sense, Google it and then ask ChaGPT.
‍
If you still don’t understand, use your network on LinkedIn.
‍
Remember, your goal is to LEARN, not finish the course or resource.

Recruiters and hiring managers are looking to hire people who have relevant experience.
‍
But relevancy has many shapes and sizes.
‍
Think through what possible “angles” or narratives you have on why who you are and your previous experiences make you a good fit for [insert data analyst role].
‍
First, decide on the industry or type of company you want to work at.
‍
If you have experience as a waiter, maybe you broadly try to break into food and beverage-related companies.
‍
If you’ve worked in HR for a decade, maybe you’re trying to break in as an HR analyst.
‍
If you have a large social following, maybe you use that to tell a story on why you’d be a good marketing analyst.
‍
If you are going to school for Finance, maybe that’s your angle for a Financial Analyst or FP&A position.
‍
By creating an angle, you maximize your chances of landing your first role.
‍
Write down a few different “angles” you have — they’ll be relevant in the next section when we talk about portfolio projects.

Directness is the core principle to remember for learning to become a data analyst.
‍
The way you directly apply the skills you want to learn is by creating a data portfolio.
‍
Over the next 6 months, you do NOT want your goal to be “learn to become a data analyst.”
‍
What does that even mean?
‍
That goal is too broad.
‍
It needs to be more specific.
‍
That’s why I recommend this instead:
‍
Your goal over the next 6 months is to create 7 portfolio projects.
‍
In this section, we’ll cover the basics of creating portfolio projects.
‍
As you start learning things like Excel and SQL, this section will make more and more sense, so make sure you come back to this as needed. I must mention this now so that you go into learning with a data analyst portfolio as the end goal.

1. Components of a good portfolio projects

The truth about a portfolio project is that recruiters and hiring managers won’t spend more than 1 minute looking at it.
‍
That means your portfolio projects should be:

1. Easy to understand
2. Interesting and easy to explain
3. Visual and tell a compelling story

When you’re coming up with an idea for a portfolio project, think about projects related to your “angle.”
‍
If you want a job as a marketing data analyst, use marketing datasets. Or if you train in Brazilian Jui-Jitsu and want a job at the UFC, use MMA datasets.
‍
Trending topics are also great.
‍
If it’s a World Cup year, analyzing World Cup data is timely and interesting.
‍
Or during COVID, a COVID analysis would have also been timely and interesting, but probably not a great choice now.
‍
You want to make sure you’re interested in the topic, ideally passionate about it. That interest and passion will shine through and make the project 10x better.
‍
Also, try to pick projects where you can create cool visuals that tell a compelling story in a few seconds. You can use one of those visuals as the featured image in your portfolio project write-up and “hook” the reader.
‍
You’re going to be creating 7 portfolio projects.
‍
You don’t need all of these components for every project.
‍
But at least 1 project, and ideally the last project you do before applying for jobs, should be your “featured project.”
‍
Your featured project should be your best project and the one you’re going to spend most of your time talking about during interviews.
‍
I recommend doing this project last because you’ll have the most experience, and thus be able to conduct the best analysis.
‍
I created an example data analyst project write-up here you can review here to give you an idea of what a portfolio project write-up looks like as part of my course on showcasing your data analyst portfolio.

2. Pick your tools

Since the goal is to create 6 portfolio projects, here are the tools I recommend using to create each:

• 2 Excel / Google Sheets-only project
• 2 SQL + Excel / Google Sheets
• 2 SQL + BI tool projects
• 1 final Featured Project with SQL + BI tool

3. Finding Datasets

Great portfolio projects aren’t copied YouTube tutorials.

They’re unique analyses conducted on relevant datasets.

You can find great datasets on Kaggle. There are lots of other places to source datasets from but start with Kaggle.

Here are the steps to finding quality Kaggle datasets:

Click filter, filter for usability rating of 10, and click apply:

Now search for a keyword like “marketing” or “finance”:

Play with sorting by both “Hotness” and “Most Votes”:

After you complete your first 2-4 portfolio projects, I recommend finding a dataset that’s a little messier than what you’ll get on Kaggle.
‍
Real-world data is messy, so messy data is good practice.
‍
Let’s say you want to work for the UFC so you’re doing an MMA fighter analysis and need a fighter's win record.
‍
You stumble upon a website called Sherdog that has a profile page for each fighter along with the stats you need.
‍
How do you get this data?
‍
Manually copy and paste it into a spreadsheet.
‍
There are faster ways of collecting data online through APIs or scraping, but since you’re just starting, manual collection is the best route.
‍
Another option is to get your data from public datasets.
‍
These datasets are notoriously messy and incomplete which is why I HIGHLY recommend using these for your last project or two.
‍
Here are some options

• IPEDS - I write more about IPEDS here.
• Census and ACS - I write more about these datasets here.
• All US government datasets can be found here.

Also, each week on Thursday, the newsletter I send out (subscribe here) always contains a “dataset of the week” and often features a lesser-known public dataset.

4. Choose your business problem

The “business problem” is the problem you’re looking to solve or answer with your analysis.
‍
Here are some example business problems:

• What is our most profitable customer segment?
• What will revenue be next month?
• What marketing channel is the most efficient?

Sometimes you choose the business problem after you see what data you have available.
‍
But in the real world, the business problem usually presents itself first and then you have to find the data necessary to solve it.
‍
For portfolio projects, either way works.
‍
There should also be some outcome to the project that shows “business value” or the value the insights derived from your project could have on the business.
‍
For example, if you do a customer analysis using data from Sephora (a company that sells makeup) and find customers that buy X brand have the highest LTV, then the business value there is that you can give that information to the marketing team so they can focus their marketing efforts on acquiring more of that type of customer by emphasizing advertising on that brand of makeup.
‍
When choosing a business problem:

1. Make it relevant to the type of data analyst role you want
2. Make it easy to understand the value

Sometimes you’ll do an analysis and find something interesting that would make an even better insight into a different business problem.
‍
If this happens, it’s a good thing!
‍
Change your business problem so it’s stronger.

5. Conduct your analysis

This is what you’ll be learning how to do in the next part of this guide 🤓

6. Your portfolio project write-up

How you present your portfolio projects is even more important than the portfolio project itself.
‍
A poorly presented portfolio project can hurt, rather than help you because it can signal you’re a poor communicator, disorganized, etc.‍
‍
I recommend creating a portfolio project write-up for each project you create.‍
‍
Use Microsoft Word or a Google Doc to take notes during your analysis and then afterward to complete your write-up.‍
‍
The write-up should include the following sections:

1. Project Summary: I recommend including a ~5 minutes video of you walking through the entire project here to demonstrate your communication and storytelling skills.
2. Project Links: Links to any dashboard or code you have in this section.
3. Business Problem & Key Questions: Summarize the business problem you’re solving with data and the key questions you answered.
4. Data Preparation: How did you obtain and prepare your data?
5. Approach: What was the approach you took in answering the key questions and business problem?
6. Results and Key Takeaways: What’s the answer to the business problem and key questions and what should the reader takeaway?

Here’s an example of a well-crafted portfolio project write-up, including a video I create as an example for my course on showcasing your data analyst portfolio.

Keep in mind these principles while crafting your write-up:

• Make it short and digestible
• Make it clear
• Make it aesthetically pleasing without clutter

7. Showcasing your portfolio

Once you have your 7 portfolio projects created, it’s time to showcase your portfolio for the world to see!
‍
I recommend using Substack for this.‍
‍
Here are a handful of portfolios you can use for inspiration:

• My example portfolio
• Zach’s portfolio - he took my course and shortly after landed his first data analyst job.
• Here’s Ali Ahmad walking through a few portfolios he likes

That's it for Chapter 2!
‍
In the next chapter, you'll be given a 6-month roadmap for learning the Data Analyst skillset.