How to Become a Data Analyst E-Book

Annie Nelson

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 496

Veröffentlichungsjahr: 2023

Beschreibung

Start a brand-new career in data analytics with no-nonsense advice from a self-taught data analytics consultant In How to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech, data analyst and analytics consultant Annie Nelson walks you through how she took the reins and made a dramatic career change to unlock new levels of career fulfilment and enjoyment. In the book, she talks about the adaptability, curiosity, and persistence you'll need to break free from the 9-5 grind and how data analytics--with its wide variety of skills, roles, and options--is the perfect field for people looking to refresh their careers. Annie offers practical and approachable data portfolio-building advice to help you create one that's manageable for an entry-level professional but will still catch the eye of employers and clients. You'll also find: * Deep dives into the learning journey required to step into a data analytics role * Ways to avoid getting lost in the maze of online courses and certifications you can find online--while still obtaining the skills you need to be competitive * Explorations of the highs and lows of Annie's career-change journey and job search--including what was hard, what was easy, what worked well, and what didn't * Strategies for using ChatGPT to help you in your job search A must-read roadmap to a brand-new and exciting career in data analytics, How to Become a Data Analyst is the hands-on tutorial that shows you exactly how to succeed.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Preface

Introduction

How Do I Know If Data Is a Good Fit for Me?

Who This Is Book For

PART I: The Fun Part

1 Is Data Analytics Right for Me?

What Does a Data Analyst Do Every Day?

What Makes a Good Analyst?

What Tools Should I Learn?

Which Entry‐Level Tech Job Is Right for Me?

What's Next

2 Understanding the Paths into Data

How Hard Is It to Become a Data Analyst?

What Are My Options for Getting into Data Analytics?

How I Decided on the DIY Approach

3 Designing Your Data Analyst Roadmap

Can You Shows Me Your Data Analyst Roadmap?

Building Your Roadmap

How Do I Choose the Best Course?

4 My Experience with Data Analytics Courses

The Beginning

What Came Next

PART II: The Scary Part

5 Introduction to Portfolios

What Is a Data Analytics Portfolio?

Can I See an Example?

Why Do I Need a Portfolio?

If I Have Experience from Another Job, Do I Still Need a Portfolio?

6 Portfolio Project FAQ

How Do I Find Free Data?

Can You Tell Me More about Completing Projects?

Should I Share My Work Publicly?

Project Time!

7 Portfolio Project Handbook

Project Levels: What Separates a Beginner from an Intermediate Project?

Guided Projects

From the Portfolio to the Job Search

Getting in the Mindset for Projects

PART III: The Hard Part

8 Starting Your Job Search

How Do I Know When I Am Ready to Start My Job Search?

Where and How Should I Look for Jobs?

Job Titles

Where Can I Find Salary Information?

What Is the Data Analyst Career Progression?

9 Résumé Building and Setting Your Public Image

How Do I Write a Résumé?

How Do I Optimize My LinkedIn?

Can You Tell Me How to Network?

10 Stages of Data Interviews

Why Do Interviews Take So Long?

Can You Tell Me More about the Interview Stages?

Resources

Using AI

11 How to Use ChatGPT to Aid Your Job Search

Writing a Résumé

Writing Cover Letters

Practicing for Interviews

Writing Follow‐Up Emails

Be Specific

12 My Job Search

“Open to Work?”

Beginning to Search

Getting Reponses (and Rejections)

Pivoting

Interviewing

Decision Day

PART IV: The Bonus Part

13 After the Job Offer

Starting the Job

Dealing with Imposter Syndrome

Steps to Success

What It's Like Working Remotely

Some Things About Tech That Surprised Me

Problem‐Solving

Travel

Data Has Changed My Life

14 Preparing for/Recovering from a Layoff

Don't Ignore Red Flags

Resumes and Networking—Restarting the Job Search

Updating My Portfolio

The Layoff

Adjusting for Your Situation

Closing Thoughts

A Data Analytics Roadmap Checklist

B Tableau Tips

What I Use Tableau For

Final Checklist

C My Data Analyst Journey

January

February

March

April

May

June

July

August–December

Acknowledgments

About the Author

Index

Copyright

Dedication

End User License Agreement

List of Tables

Chapter 10

Table 10.1 Breaking down what interview questions communicate to the interv...

List of Illustrations

Chapter 1

Figure 1.1 A sample dashboard that I created after a few months as a Tablea...

Chapter 9

Figure 9.1 The first draft of my data analytics resume.

Figure 9.2 The resume I submitted to my first job as a data analyst.

Figure 9.3 My new resume, now that I have been a data analyst for about a ye...

Chapter 13

Figure 13.1 The microfiber cloth and pipe cleaner contraption I made for org...

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

Preface

Introduction

Begin Reading

A Data Analytics Roadmap Checklist

B Tableau Tips

C My Data Analyst Journey

Acknowledgments

About the Author

Index

End User License Agreement

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How to Become a Data Analyst

My Low‐Cost, No Code Roadmap for Breaking into Tech

Annie Nelson

Preface

I've never seen myself as particularly “techy” or “good at math.” To be honest, I never enjoyed math, and if you'd asked me even a few years ago if I had any interest in learning how to code, I would've laughed at you. So how in the world did I go from that to writing this book about becoming a data analyst? Perhaps I should start with some context, because I find that the more I connect with others in the data community, the more I see my story reflected in theirs.

In the United States the phrase “What do you want to be when you grow up?” is usually synonymous with both “What do you want your identity to be?” and “What career do you want to have?” I grew up thinking that what I did for work had to be my entire personality. I was never quite sure what I wanted to “be.” I was a natural caretaker and wanted to be seen as empathetic, and I've always loved spending time with kids. I also knew that I love spending time outdoors and going on adventures regularly.

This led to the natural conclusion that I should become a teacher or maybe a wilderness therapist. Well, after some time dabbling in teaching I realized that it was not the path for me. So after discovering the joys of nature‐based occupational therapy with children, I decided I knew what I wanted to “be,” and I signed up for my master's degree a few months later.

Fast‐forward a couple of years (and many thousands of dollars later), and I was more burnt out than I'd ever been. Unfortunately, a perfect storm of circumstances had rocked my (our) world(s) in the last few years. In my first year of graduate school (1) we entered a global pandemic (hello, 2020!), (2) I developed a mysterious autoimmune disorder in my brain, and (3) I was juggling full‐time graduate school and being elected president of the student government of my program.

Unfortunately, I didn't have the best graduate school experience. I felt that I was often being asked to ingest and regurgitate information seemingly at the instructor's discretion. I'm someone who genuinely enjoys learning, and being forced to perform under high stakes without the joy of being allowed to truly learn was a hard pill to swallow. I poured hours of time (and many tears) into trying to advocate for myself and my peers, only to get chastised for being “unprofessional.”

After two years of poor physical health (migraines, brain fog, and overwhelming fatigue) and poor mental health (due to graduate school), I knew that something in my life needed to change. The year 2021 in particular felt like it was grinding me down so hard that all that was left was an emotionless pile of dust. So, in the last semester of school I quit my job and moved out of my apartment to go live with my parents, to try to recover some of the spark I used to have for life.

Why am I telling you all this? None of the individual brush strokes of my story are likely to be relevant to you. But, if we back up a bit and look at the whole picture I've painted, I know it suddenly becomes a familiar picture for many people out there. What's the number one thing people tell me when I ask why they decided to take the leap and switch careers into data? “I was burnt out, unhappy, and wondering if there could be more than the life I was living before.”

As I started to recover from burnout, the first thing that returned was my interest in learning. The next thing to come was the acute awareness that I still needed to make it through another nine months before I could get a job as an occupational therapist and start paying off my tens of thousands of dollars in student loans. At first, besides the time I was spending on internships as a part of the end of my Master's Degree, I was reading five books a week and delivering for Instacart. At around the same time, I started to see all these TikToks from people who worked in tech talking about their remote jobs, how flexible they were, and how well they paid.

The idea of working remotely, having autonomy over your schedule, and being paid well enough to be comfortable was fascinating to me, but it felt far out of reach. I wrote it off initially, thinking I wasn't smart enough. “I'm just not a math person” and “I could never enjoy coding” and even “I like having a job where I'm on my feet often” were all mental statements I made to myself.

Sometime about mid‐January, I decided to look up “tech jobs you can do without learning to code.” To my surprise, it appeared there were many paths into the tech industry, not all of them requiring a computer science degree or any coding. I spent a few hours going down the rabbit hole of different options and finally landed on data analytics. What I gathered about data analytics is that it involves working with data in programs like Microsoft Excel. It can be done remotely or in person. And, crucially, it seemed like it had a pretty low barrier to entry.

I put all those pieces together and decided, “Maybe I could learn data analytics well enough that people would pay me to organize and analyze their spreadsheets as a side hustle.” It had all the markings of a perfect plan—I could do it on my own time, make more than minimum wage, and do it without having to drive anywhere. I'd found this Google Certificates course in Data Analytics that seemed to have the roadmap laid out for me to prepare for a job or side gig.

I was lucky enough at that point to have already prepared for the time I would be in various occupational therapy clinics full‐time to complete the requirements for my degree (kind of like student teaching), unable to have a steady source of income. I had some free time to spend learning, as long as it was cheap. Looking back, what was most exciting to me about signing up for and beginning that Google Certificates course was the ability to finally get to learn again.

I'll continue sharing the story of what happened next in the chapters to come, but the short version is that the random decision to try analyzing spreadsheets as a side hustle became not only the catalyst for eventually deciding to switch careers, but also one of the single most influential and pivotal moments in my life so far. As you can imagine, you don't get to the point of writing a book about how to become a data analyst without it being a life‐changing event.

I'm so glad that I put a very small amount of thought into the idea of trying out data analytics. If I'd thought about it and tried picturing myself as a data analyst among other data analysts, I never would've started.

If you’d asked me a year and a half ago what your average “data analyst” looks like, I would've answered something like this:

Male

Good at math/statistics

Into computers

Has a computer science or math degree

This is far beyond the scope of this book and I am not qualified to talk about it, but as I've discussed this topic with others in the industry, something that often comes up is that most people perceive tech to be dominated by cis straight white men. If I had to choose from a multiple‐choice list who would be the best fit for “data analyst” and “cis white male” was one of the options, I would've chosen that.

The point I'm making here is this: I definitely did not see myself represented in the tech world. I can't imagine that I would've decided to seek a new career that I knew nothing about, where I wouldn't fit in with anyone, while not having any idea if I would ever be good at it—even if it promised good pay and remote work.

That's part of the reason I'm writing this book: I hope readers can see themselves represented in my story. I am neurodivergent, I have an autoimmune disorder. I am a (white) woman. When I was writing this preface and explaining how I hated calculus in high school, I realized two days later that it was algebra. I never even took calculus in high school (I did take pre‐calc). I love spending my time with children. Three years ago was the first time I ever had a full‐time job during the summer, because prior to that I would take my summers off so I could spend afternoons swimming, camping, rock climbing, and generally not working or sitting in front of a computer.

I'm not what someone would consider the “ideal candidate” for a data analyst role on paper. But you know what? In just six months I taught myself data analytics for less than $100 and landed a great job. I have loved being a data analyst, and I don't get the “Sunday Scaries” anymore, where I spend most of my Sunday dreading going back to work on Monday. I have also never gotten a less‐than‐glowing performance review from my manager(s) since I changed careers, and others around me tell me that I am learning fast and doing exceptionally well for someone so new to the industry.

So far I've talked about data analytics from the perspective of what it looks like to be an entry‐level data analyst. But how about what comes next? If you launch a data career, what can you expect your roadmap to look like in the future?

The beautiful thing about working in tech in general, and analytics is no exception, is that it is always growing and evolving. Earlier I mentioned how curiosity and a love of learning are a valuable part of the data analyst mindset. There are so many ways you can make a career in data.

The only thing I can say for sure is that you can't get into data analytics and expect that it's always going to stay the same. As you move up the chain of data analytics, the expectations for you in your role will evolve. Additionally, the tools themselves will always be changing and evolving. I heard a story recently about someone who got into a senior data analyst role just by getting really good at Excel. When they decided to try to get another role, they couldn't. They refused to learn SQL, and when they tried to find a senior data analyst role that didn't require any SQL (or Python!), they couldn't find one.

Don't worry if you don't know what SQL and Python are yet; they're tools data analysts use, and you'll learn about them in this book.

Since every industry needs to have data analyzed, and data is such a fast‐growing field, the possibilities are almost endless. Here are some options and job titles for the future career path you could take once you get into data:

Data quality analyst

Senior data analyst

Senior research analyst

Senior financial analyst

Analytics manager

Director of analytics

Data scientist

Data engineer

Analytics engineer

Chief data officer

Data project manager

Project manager

Product manager

Data governance specialist

Data quality engineer

Data steward

Data evangelist

Head of data analytics

When I decided to get into data analytics, I did it because of the seemingly infinite possibility for growth. I thought that I'd spend a few years as a data analyst, and then look into transitioning into a data scientist role. Now that I have gotten into not only data analytics but also consulting, I've realized I'm very interested in data strategy and quality. I enjoy thinking about the big picture and understanding how each piece of the data puzzle fits with the rest.

In the future I see myself potentially going in the direction of data/analytics engineering, data strategy, or even head of analytics or chief data officer. Who knows where my career will go—not me! It does seem to be pretty common that people think data analytics will be their stepping stone to data science, and then they discover some other path along the way that interests them. I don't want to become a data scientist, but it is a popular transition.

One big benefit of data analytics, at least in 2023, is that it has a fairly low barrier to entry. I often see people who had no prior tech or data experience getting their first data jobs. It happened to me! Many of the other roles I listed require previous experience. Fortunately, becoming a data analyst could be the springboard or prior experience you need to access any one of them.

Even though the barrier to entry is low, it still takes work! In this book, I walk you through everything I needed to know to make the leap from my old career into data analytics. This will prepare you to do the work of becoming a data analyst, without all the uncertainty of not knowing where to start or what to focus on.

Introduction

If you've read this far, you're probably interested in jumping into data analytics. Congratulations! It tops the charts as one of the best things I've ever done for myself. In this book I will lay out my story, and along the way I'll relate that to how you, too, can get into data analytics.

In this book, I'll discuss how I got my first data job six months after I began learning this new career, and it cost me less than $100. I've seen hundreds of people go down this same route since I got my job and experience success as well. I've compared my story and struggles with those of many others and identified the things that we all have in common.

There are many different paths to getting a role as a data analyst, and no two journeys will look the same. At the end of the day, job searching often comes down to luck and timing. I'm not going to tell you exactly what to do, and I can't guarantee that at the end of it all you'll get a job. I can't guarantee you a certain salary, or that it will take you a certain length of time.

What I can tell you, though, is what worked for me. I can tell you what felt hard for me and what things I would do differently if I were to do it all over again. I can tell you the things I've seen people do who followed the same path to successfully land a job in data analytics.

How Do I Know If Data Is a Good Fit for Me?

One of the questions I get asked the most often is “Can I get into data analytics if my only experience is in _____?” The answer is yes. Data analytics is a unique career because it has a fairly low barrier to entry. Every industry needs data analysts! I may not be the best “financial data analyst,” but I'm working on a project right now with research/survey data, and my research background has given me a leg up. So, toss aside any perceptions that you may have about “who” makes the “perfect” data analyst. If it seems interesting to you, then you might be a good fit!

Who This Is Book For

This book is for you if you want to take what I call the “DIY” approach to getting into data analytics. You are happy to teach yourself and you don't need an instructor to tell you what to do or teach you hands‐on. If you take this approach, it will likely be the cheapest; many people, including myself, have done it for less than $100. However, do not pick this approach just because it is the cheapest.

My “DIY” approach is good for people who already have good critical thinking skills—people who already know how to do research on the Internet. (You'd be amazed at the number of questions I get in my inbox like “What is SQL?” You should be able to Google things like that yourself and not have to ask someone else.) This approach is good for the career changers—people who already have professional experience in another field and just need to learn how to translate that into data analytics. This can be just about anything, from waitressing, to teaching, to occupational therapy (I've done all three).

This book will be helpful if you like to solve problems on your own. I'll provide a general roadmap and some sample projects. However, I'll focus on sharing my experiences with you—and it will be up to you to take that information and apply it to your own situation.

I find that the type of people who are curious, passionate, and good at critical thinking tend to have the easiest time making their way into data analytics and enjoy it the most when they get there. If you'd prefer to have someone to walk you through every step of the process—teach you the technical skills, provide you with résumé feedback, stage mock interviews, help you with your LinkedIn profile—know that this book will not do that.

This book walks you through my journey to become a data analyst, and I'll offer advice about selecting courses and learning data skills. I'll share with you my real insights of what it felt like to run a successful query for the first time, as well as my doubts about my abilities when I reached challenging subjects.

I'll talk about building a portfolio, which is the key to any successful transition into data analytics. Building a portfolio was exciting but intimidating for me when I was learning, and so I'm taking the guesswork out for you and will break it down so portfolio building is approachable—and hopefully fun!

I'll also discuss all things job search. I'll share my honest experience of job searching, being rejected, and considering giving up on data analytics. You'll see how I pivoted my strategy and how I landed my first job in data. I'll also share practical tips about networking, résumés, and LinkedIn. There's even a guide for using artificial intelligence (AI) to help you succeed at every step of the job search and interview process.

If you enjoy learning independently and are willing to put in the work to become a data analyst, then this book will be just the guide for you. My goal is to take all the guesswork out of the equation so that you can set yourself up with a roadmap and avoid all the mistakes I made along the way.

Changing careers into data analytics requires persistence and determination—but it's worth it in the end. At least, it was for me!

PART IThe Fun Part

Chapter 1

: Is Data Analytics Right for Me?

Chapter 2

: Understanding the Paths into Data

Chapter 3

: Designing Your Data Analyst Roadmap

Chapter 4

: My Experience with Data Analytics Courses

1Is Data Analytics Right for Me?

What's Here

What does a data analyst actually do every day?

What makes a good data analyst?

What tools should I learn?

Which entry‐level tech job is right for me?

What does it even mean to be a data analyst? Before you can dive into data, you probably have a lot of questions about what it would really look like to be a data analyst. If you are in a completely different career (like I was before I transitioned into data), you may want to know about the day‐to‐day and what would make you a good analyst.

Once you understand what it looks like to be a data analyst, you can make a more informed decision about whether it will be the right fit for you. At the start of my journey, I was unsure if I would enjoy the work of data analytics, but I knew that the idea of remote, flexible work was appealing. Fortunately, I ended up loving everything about being a data analyst once I dove in. It turned out it is the right fit for me!

Since data analytics is a broad and diverse career path, there are many different options for what this career path can look like. In this chapter I will share with you the basics of what it's like to be an entry‐level data analyst, as well as career progression options.

What Does a Data Analyst Do Every Day?

Later in this book, I will talk about what my day‐to‐day looks like, and share some stories from other data analysts I know whose jobs are vastly different from mine. For now, let's just talk generally about what data analysts do day to day.

Something that I love about the field of data analytics is that it is incredibly diverse. I don't know two people in data who do the same thing. The thing is, unlike other career paths, there is no one “area” that data analytics belongs to. If you're a nurse then it's fairly predictable that you are going to be working with the human body. Real estate agents pretty predictably sell houses and properties. But every industry out there has data, doesn't it?

That means if you become a data analyst you might need to know about

Banking

Healthcare

Stocks

Insurance

Auctions

Manufacturing

Research

Sales

Marketing

Human resources (HR)

Construction

The list goes on and on. Although there is a core set of tools and skills that most data analysts will need, the day‐to‐day of the job is going to be heavily influenced by the demands and culture of the industry and company that the role exists within.

I have found that generally there are two primary divisions to “data analytics,” but almost all roles mix and match from both. Data analytics tends to represent the technical aspects of the job—which means utilizing things like Microsoft Excel and SQL to analyze data and draw conclusions. However, almost all analyst roles also incorporate “business analytics,” which involves taking what you learn from your technical data explorations and applying it to the real problems and challenges facing the business.

A common phrase in the data sphere is “heads‐down work.” That refers to when you are doing a deep dive into the data/project. Heads‐down work tends to be pure analysis/building, so it doesn't involve emails, meetings, or presentations. A role that leans more heavily toward the “data” side of analytics tends to have more heads‐down work. When there are meetings, they are often an internal review of the work that has been done and planning for future work.

Roles that lean more toward the “business” side of analytics, on the other hand, will involve a lot more face‐to‐face time—internally and externally. This might mean spending some time doing heads‐down analyses but then presenting that information to internal executives or external stakeholders or people this analysis affects. It may also involve observing and taking part in processes within a business—I find this to be especially common in small businesses/start‐ups that are still defining how they will collect and organize their data.

As I have gotten deeper into the data space, I have realized that oftentimes the most valuable part of an entire data project is the meetings that happen at the beginning—before anyone has even looked at any of the data. These meetings are more than just requirement‐gathering sessions; it is the time when knowing how to ask the right questions will determine not only how successful a project is, but also how long it takes. As a data analyst, it's your job to have a clear understanding of what the business problem is that you are trying to solve (using data), and how what you are doing is going to directly impact the mission/bottom line of the business.

As a note, I'm not sure why, but I don't know if I have ever actually heard someone use the counterpart “heads‐up work.” I think I used it in one of my interviews and now I look back and wonder if they thought it was a weird thing for me to say!

Although I think it is possible to get a role that is strictly a “business analyst” role where you hardly even touch any of the technical tools, or conversely to get a purely “data analyst” role where you do not have to go to any meetings or gather any business requirements, that is incredibly rare. Additionally, it is short‐sighted. That could work for someone who is entry‐level, but even a mid‐level analyst generally needs to know how to do both.

Most analyst roles are going to end up being a mix of gathering requirements, heads‐down work, meetings, the occasional presentation, and more generally, spending time thinking about a project plan. For example, in my role as a consultant I spend about 5–10 hours a week meeting with clients to plan, validate my work, and show them what I have been working on. Another fiveish hours a week go to internal meetings, many of which are with my boss to check in about my projects and get support where I need it. The remaining hours I spend getting connected to data, analyzing it, and visualizing it.

Hours/Time

One of the biggest selling points for me when it came to switching careers into data analytics was that I could work remotely. Not every data analyst role is remote; I think about one‐third of the entry‐level analysts I know work in person (in an office) or hybrid (a mixture of both). Lucky for me, my role is 100 percent remote. In fact, my company doesn't even have an office. We do have a subscription to coworking spaces, though, should I need that option.

The day‐to‐day of someone who works from home is naturally going to look different to someone who works in an office. All of the analysts I know who work in an office have told me that it is the standard schedule of commuting to work, packing their lunches, and working fairly normal office hours. Everyone I have talked with who works in the office has said that instead of tracking their time, their progress is monitored based on their progress toward their projects/goals and that they are generally expected to just be there during normal hours.

When I changed careers and got this (fully remote) job, I was so curious about what that was going to look like. Would I still be expected to work a normal 9 to 5? How would they track my time? Although I am technically a “consultant,” I do work for my company full time, I get benefits, and so forth. So I'm not a “freelancer.”

I cannot stress enough how much I have loved working remotely. My company has a good culture, which is part of the equation. I am generally expected to be available during standard working hours. I have multiple clients at a time, so I'm also expected to be scheduling meetings with my clients during normal working hours and attending internal meetings during the workweek. Which is normal and completely reasonable.

However, is my boss tracking if I am in my seat by a certain time? Or if I am working my hours at a certain time of the day/day of the week? Absolutely not. On a normal day I will wake up (without an alarm!) and start working by about 9 a.m. Sometimes during the day I will step out to go grocery shopping, do some laundry, or go for a walk. If I am not feeling well I will sleep in, or I will log off early (if I don't have meetings to attend).

I do have to bill a certain number of client‐facing hours (working on a client's project or meeting with them) as well as internal hours (working on internal projects or participating in learning, research, and skill development). Since I generally prefer to be flexible in my daily routines, I will often work a few hours on the weekends to make up for time during the week when I was busy with other things (like grocery shopping).

This works for me, but it isn't expected! My coworkers tend to work their full workweeks during standard weekday hours, and then they log off for the entire weekend. The point here is that since we work remotely, we are allowed more control over our schedules and flexibility with them than we would if we worked in an office.

As someone who was about to go into healthcare, this kind of flexibility is mind‐blowing to me. I have friends from graduate school who have told me that they needed to use some of their (very limited) paid time‐off (PTO) time just for doctor appointments. And, when they did use their PTO to go to the doctor, their boss made them feel guilty about it the entire time because things could not run as smoothly without them there.

I have talked with many other data analysts who work remotely, and not everyone has had the same experience as I have. A handful of people have told me that although they work remotely, they are expected to be at their desks from 9 to 5 as they would at an office job. If their boss were to call them on Microsoft Teams (a common app that businesses use for communications) and they did not pick up, that could lead to them getting in trouble if it happened a few times. Coincidentally, those are also the people who tell me they are actively upskilling so they can look for a new job.

A middle point between their experience and mine is what I see as the most common set of expectations for a remote data analyst role. Most people I talk to who work remotely generally are expected to work a normal 9 to 5. However, if they had a doctor's appointment during the day or needed to take a short break to go for a walk, they would just have to talk to their managers about it, and it would be fine.

I have talked to several transitioned teachers/healthcare workers who have remote jobs that are like the third option I talked about. We marvel together at how much of a relief it is to be able to take care of things like doctor appointments during the day without spending every minute of it worrying about work—and having to use PTO to do it. This is especially relevant to my friends who have children, pets, and more responsibilities than just a few cacti (like me).

This may be unique to me, but another perk of working from home is that I am working from … home! This means that I never have to change out of sweatpants, I completely control the temperature, I can play whatever weird music I want to (or have it be completely silent when I am feeling overstimulated!), and I have access to my full kitchen and refrigerator.

I don't know about you, but one of the hardest things about being an adult is having to feed myself every day. Now that I work from home I can stick some food in the oven mid‐morning, and it's ready by lunch time. When I need a movement break in between meetings in the afternoon, I can get up and do the dishes left over from my lunch. Not having to pack my lunch every day saves me a lot of headache and a lot of money. I almost never eat out anymore because it's so much easier to just make something in my own kitchen.

One final point on the general workday of a data analyst—especially one who works remotely—is the topic of the workspace setup. Many people take a lot of joy in setting up a workspace that they love, whether it be plants, a cool second monitor, or funky lighting. A hot topic on LinkedIn right now is standing desks + under‐desk treadmills.

When I worked as a nanny and later in occupational therapy clinics, I had a pretty physical job. I was walking around a lot! Many people tell me, “Oh, I could never get a computer job, I couldn't stand sitting all day!” Enter the under‐desk treadmill—or just walks. I live in New England, so it gets pretty cold here in the winter time. So, I invested in an under‐desk treadmill. Once I got used to it, I can pretty much do any kind of work while walking, although I don't take client meetings from the treadmill.

In a standard week, I walk 10–20 miles on my little treadmill while I am working. It is pretty common in the data space to hear people talking about getting out to take walks during the day when the weather is nice. Yes, as data analysts we do have desk jobs. But to think of it as completely sedentary is a big misunderstanding! I also have found that working at a job that I genuinely enjoy, where my boundaries are respected and I am not having to deal with unhappy people all day, leaves me feeling energized. At the end of the day I am perfectly happy to go to the gym, get chores done around the house, or meet up with friends to socialize because I am not completely wiped out from whatever I did at work.

In‐Person Data Jobs

To add some context to the conversation of what it is like to work remotely, I talked to some of my data friends who work hybrid/in‐person jobs. All of them still do enjoy their jobs! They all said in their own way that in‐person data jobs have a similar structure to a lot of the other (non‐data) jobs they have had.

They follow a fairly normal 9–5 schedule for in‐office work, and then fit in going to the gym or the grocery store after they leave work. Each of them told me that they do have good flexibility to leave work to go to the doctor if they need to but that it is an unusual occurrence to cut time out of their workday.

Some people I talked to feel like they need to be in the office for 8–9 hours a day, 5 days a week. Others told me that they feel comfortable leaving once they finish their work, and possibly shifting their work schedule to be earlier or later if needed.

I have only one friend whose job is fully in‐person (with occasional days from home); the other five I talked to all have a hybrid schedule of a few days in the office and a few days out each week.

I asked my friends who have a hybrid job if their days looked different on the days they work from home from the days they come into the office—and they all said yes. In fact, they all said that they still have meetings over Zoom when they go into the office, and they would prefer to have even more days from home each week if it were up to them.

On the days that they work from home they feel that their time is more flexible. They are more likely to take a walk or go to the gym in the middle of the day. Since they do not have to commute, they are very aware and appreciative of the additional time they get back in their day on the days where they work from home.

My friends with hybrid jobs said that a lot of their job could be done remotely. On the days that they are in the office, their employers make sure to schedule team meetings and planning sessions—meetings that benefit from being able to get people in a room together. Two of my six friends who have in‐person/hybrid jobs expressed to me that they enjoy getting to go into the office and socialize with their peers and prefer that option.

The other four people I talked with said that, although in‐person coworking can be nice sometimes, they prefer the days when they work from home, and that when they search for their next job they will be looking for a fully remote role.

I may have some self‐selection bias going on with the people I picked—I am a huge advocate of remote work, so the people who make friends with me may be more likely to also be lovers of the freedom that remote work brings. This means that I am likely not representing the portion of data analysts who prefer in‐person work very well!

Many of the employers who embraced remote work during the pandemic are now requiring employees to return to the office at least some days of the week. That means people who are okay with hybrid jobs will likely have an easier time finding a job than people who are committed to remote work (like me).

I can say from experience that for every one job that recruiters reach out to me about on LinkedIn that is fully remote, there are four to five that are at least hybrid. It seems like fully in‐person jobs are rare—and from what I have seen they are more common in industries that are not “techy”—and all of their other employees are in‐person full time as well.

I wonder if employers are finding it significantly harder to fill fully in‐person data jobs with good candidates than roles that are at least hybrid, because the dominant sentiment I see from my many connections on LinkedIn is that most people prefer to work at least hybrid.

Once you are introduced to the benefits of eliminating your commute and being able to spend more time at home, with family, and on other activities besides driving (for at least a few days a week), it is hard to go back.

What Makes a Good Analyst?

If you're still reading, then it means you have decided that maybe data analytics is the right choice for you! Congratulations! When I made that decision it changed my entire life. So let's talk about what makes a good data analyst and what it looks like to be in the career. I find the following information to be exciting; I love that I have found a career where these things are valuable!

When I started learning about data analysts, all I had available to reference to understand what makes a good data analyst was a Google search. The results were mixed. Lucky for you, I have leveraged my 50k+ network and over a year of being in the data space to ask the question, “What makes a good data analyst?”

I talked to senior data analysts, random people on LinkedIn and TikTok, data scientists, data engineers, a few heads of operations, vice presidents, chief data officers (CDOs), chief operating officers (COOs), recruiters, and many random people with unknown titles on LinkedIn and TikTok about what makes a good data analyst. Oftentimes these people are the ones responsible for hiring, mentoring, and promoting data analysts. Here is what I learned from them, plus some of my own thoughts thrown into the mix.

So what makes a good data analyst? Critical thinking. This was the number one response that I got. When you are an entry‐level data analyst, you might be able to get away with just clocking in, clocking out, and doing what is asked of you. But in the majority of data analyst roles the most valuable thing you can bring to the table is your ability to engage with problems and business needs and draw your own conclusions.

Think about it this way. Do you need to know how to swing a hammer and use a saw to be a good carpenter? Absolutely. But is it the ability to use a specific tool that makes a good carpenter? Definitely not! Data analytics is the same way. SQL, Excel, Tableau … At the end of the day they're all just tools. A good data analyst needs to know how to think like an analyst.

I've put together a list of the nontechnical skills that have been critical to my success in my role lately, and they are planning, organization, critical thinking/strategy, and communication/collaboration. I will explain each of them in the paragraphs to come. Before I elaborate on the list, though, I do want to mention one thing. Since I am a consultant, my role seems to be different from the roles of the other entry‐level data analysts I know.

If you encounter terms in this chapter you are not entirely familiar with yet, do not worry about it! Any terms you need to use as a data analyst will be introduced to you later on in the book, but I am going to give some examples in this chapter to make a point.

It seems to me that many entry‐level data analyst roles exist within just one company. Usually, these roles report to a senior‐level data analyst/analytics manager. Their manager is in charge of planning, cross‐department collaboration, and overseeing and assigning project tasks, whereas the entry‐level data analysts are the ones who are assigned specific analytical tasks and report their work back to their manager. From what I have seen, entry‐level analysts gradually take on more collaboration, planning, and strategy as they gain more experience in their roles.

Planning

Data does not exist on an island in any business. You are never going to find a data project that is just “crunching the numbers.” Data analysts (especially in more senior positions) have to plan carefully with all of the parties involved. At the beginning of every data project, the analyst in charge needs to meet with stakeholders—which could be managers, executives, or clients. They need to gather requirements for the project and set the scope, which often involves a series of meetings, emails, and the creation of planning documents.

While scoping and planning the project, the analyst in charge also needs to take a look at the data and determine if they have the access they need, and if the data that they have available to them is what they need for the project. Connecting to and digging around in the data often has different technical requirements than what you would see in the actual project.

Once the planning stages are over, the project begins. For any data project that takes more than a few hours, the analyst working on the project may need to be sending emails back and forth, hosting meetings, or even working in collaborative documents to ask questions and check in with the stakeholders.

Once a project is finished, the analyst in charge often needs to do some kind of a presentation. Junior‐level analysts may just send their work off to their boss, but senior analysts will often be presenting the insights of their work to managers, executives, or clients.

For example, I recently worked on a project with a fairly small company. When we got together to gather requirements at the beginning, we met with the chief financial officer (CFO), as well as two people heavily involved with marketing.

In that meeting, as we discussed how this project was going to benefit their organization and simplify each of their roles individually, it became clear that the work I would be doing would benefit each of them differently. We needed to have separate follow‐up meetings with the CFO and people in marketing, because their roles in the project were different.

In the meeting with the CFO, I got connected with their database and existing Excel spreadsheets, and we went through and defined different columns in their data and how their reporting is built. When I met with marketing, we did not talk about individual fields in the data at all and instead did a whiteboard session to plan out the different dashboards I would be building, We also reviewed which metrics they send out in their newsletter every month to make sure those metrics would be front and center and easily accessible to them.

After those meetings, I got connected to the data and started re‐creating fields from their Excel spreadsheet in Tableau (to automate their reporting). But I had to check in regularly with both marketing and the CFO, because the names of columns in the database did not match the names of columns in the Excel spreadsheet, which also did not match the language that they were using for those fields in the newsletter.

Organization

Have I experienced data analysts who are not very organized? Yes. But do I think it is a critical part of my success? Also yes. As a data analyst, I am usually an intermediary, or interpreter. I am receiving information that has been put into the system by other people, and I am analyzing and visualizing it so that other people can take that information and use it to make business decisions.

This opens up a huge need for organization. Here are some of the things I need to have a system of organization for every day:

Files

At the start of the project I usually get a lot of files, and they all need to be well labeled and stored together so anyone can easily access them later.

Documentation

For every project I do, I need to keep track of all kinds of things. For example, what does the column “Gains” mean? Is this profit after the cost of goods is taken out? Or before? Additionally, I have to document what I am building and how it is all interrelated. The goal is that once I finish a project, anyone can use that work without having to refer back to me to explain how things are built.

Workload

It is rare for analysts to have just one project that they are working on at a time, that has only one stream of work related to it. Each person has to learn how to organize their own day so that they are making all the meetings they are required to go to and still completing all their tasks and projects on time.

Communication

If you have worked an office‐type job before (or really, most jobs), you have already encountered the phenomenon of the never‐ending emails. You start your day with a list of things you need to get done, and throughout the day you're likely going to get a steady trickle of emails adding to that list. It may sound trivial, but on top of making sure everything else about a project is well organized and documented, adding on the need to keep track of emails and perform ad hoc organization of whatever comes your way in an email will rely on you already having good organization systems in place.

Critical Thinking/Strategy

It's hard for me to say enough about this skill—it's the most valuable skill I bring to every project. The thing about working with data is that there is never just one way to do something. Even if there is one “right” answer (which there rarely is), there are many different ways to get there. This is where critical thinking comes in.

Let's take a SQL query, for example. Many times there are at least 10 different ways you could write a query and get the exact same table as an output. But, five of those ways are going to take the database three times as long to run the query, two of those ways are going to be long and difficult for anyone else to read and understand, and of the other three, two of them will be better aligned with the way your boss writes their queries.

It is up to you to understand not only how to use SQL, but how to piece together different tables, columns, and functions to achieve the end result you want. The next day you might come back to write a similar query, but just a few things about the data have changed and you have to think through the best way to do it all over again.

Not only is critical thinking necessary for individual tasks, but it also relates back to what I was saying in the “Planning” section. Working on a project is like completing a puzzle. You are taking all the business requirements for the project, combining them with the needs and preferences of the person you are delivering the work to, nesting that within your knowledge of the way the business is organized and its priorities, and needing to fit your individual analyses and uses of the analytical tools within that—like one big puzzle.

At the end you either get something that is messy, over budget, and not quite usable by the intended audience, or you get a piece of work that your intended end user will rely on to make business decisions. For example, let's go back to the marketing dashboard. If you take 60 hours to build a marketing dashboard that doesn't quite do what your client needs it to do, in business terms that means that they just paid you for 60 hours of work and, in the end, they still have to go back to their original Excel spreadsheet every month to pull all the numbers they need to include in their newsletter.

Although that picture is quite grim, let me cast this in a different light. This skill of applying my critical thinking skills and solving puzzles every day is my favorite part of my job! I am the type of person who loves to always have their brain engaged. My brain rarely “shuts off.” In the past I have had a hard time staying with jobs long term, because I have had jobs that, although they required a lot of physical effort, were not intellectually stimulating. After a few months I would get bored, and then every Sunday I would dread going into work the next day.

Now, I get my Sundays back. I do not have to dread Monday rolling around, because I genuinely enjoy my job. Of course, every job has its stressors. However, I would say that for at least 75 percent of my day every day I am using my “puzzle‐solving” brain. Every project I work on brings new challenges, new things to learn, and a new way to analyze data. Most of the data analysts I know also express how critical thinking is not only the most important part of their role, but also the reason that they love being a data analyst.

Collaboration/Communication

Data analysts are not the ones who create the data, nor are they the ones who use the data. Data is created by salespeople, skilled workers, electric bills, property taxes, online reviews … you get the point. Data is used by executives and managers—people who pay bills, hire and fire new employees, change their businesses’ marketing strategies to run more ads on a well‐performing platform, and meet with investors to make more money. So who is the data analyst in all of this? The intermediary.

This means that data analysts are rarely ever going to be working in isolation. For every project, data analysts will likely have to work with people in multiple areas and at multiple levels of the business. For example, when I worked on that marketing dashboard, I was communicating with executives. However, I found the CFO to be a very easy‐going guy and we worked best with a less “formal” communication style. I did not need to include a formal introduction to every email, and he was comfortable with us getting right to the point of the task at hand.

I wanted to maintain a friendly yet effective tone in my emails, and I remember that at the beginning of the project we ended up needing to send some emails back and forth that were quite dense, as I tried to understand how different columns from different tables and data sources related to each other. At one point I believe we ended up tacking smiley faces onto the ends of these particularly tricky emails to communicate something along the lines of “I know this material is quite dense and I do not want you to think I am judging you or that I am lecturing you.”

On the other hand, when I was emailing the marketing department at around the same time it was usually just me clarifying things like, “I took ‘Sold Amount’ and added ‘Shipping Cost’ to it; does that accurately capture that ‘Price’ field you referenced in the newsletter?” To which I would get a response like “Yes, that is correct. Thank you. [Insert standard signature here].”

At the same time, I was checking in with my boss several times a day via Slack messages to ask him things like, “Hey, can you look at this calculated field for me? Here is the formula I used. Does this look right to you?” or “I could not fit all six of these charts on the top line of the dashboard, so I did it this way instead. What do you think of the grid format?”