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How to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech
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 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 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.
- GenresNonfiction
288 pages, Paperback
Published January 4, 2024
26 people are currently reading
54 people want to read
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Displaying 1 - 4 of 4 reviews
October 10, 2025
There is genuinely up-to-date advice in here for the time this book was published. I fear that the economic situation has changed too much to make this of much use to anyone. I've certainly given up on breaking into data analysis. Even when Nelson published, the field was very competitive. Now, companies are gambling on AI, all-in; the economy where Nelson found a job does not exist anymore.
Nelson describes the tremendous relief that came from lifting herself out of the working class into the email-from-Thailand jobs. To hear someone else's story of upward mobility is inspiring because individually considered, it seems like I could follow the same steps. Yet I am a different person I cannot follow her footsteps exactly.
There are simply too few chairs and too many dancers in this party-game. As Nelson mentions, the skill-learning portion is the easy part. The intuitive and emotional battle of networking and applying to jobs is the true test. I'm convinced I could be an excellent data analyst. Yet searching for a job, marketing yourself, is like a whole different job that I'm kind of ass at. I'm cynical and critical--incisive traits that make for a good bullshit detector, the kind of thing you want in a great analyst or investigator. Yet those vary qualities make make LinkedIn and corporate-speak repellent to me.
It's not Nelson's fault, but I'm disgusted with the tech space. It may be sour grapes, but I am glad I changed my mind about this field. They constantly emphasize the 'soft skills' and 'critical thinking' required to become a true analyst, yet the tech space is filled with people who lack judgment and discernment because they have emphasized the technical to the great expense of the human.
Michel Foucault talked about the paradox of disciplinary society. Consider soldiers: they must become intensely powerful specimens of human strength, speed, and determination. They need to be able to shoot-to-kill, to go over the top. Yet their officers would also like them to have the emotional mastery that would allow them to stop on a dime, to turn off the violence instantly and absolutely. Some will be able to make that work, yet dozens of others will be traumatized and scarred by the paradoxical pressures.
I see a similar paradox at plate in elite workers today. They must refine their intellectual abilities and technical skills to a high-point, but must always stop short of questioning the basic parameters outlined by their firm or industry. In interviews, you must show independence and autonomy, but also deference and submissiveness to authority.
While I was still practicing to become a DA, I did some public analysis of data on the Maven analytics platform, an expensive MOOC provider. Recall that MOOCS have a greater than 90% dropout rate. I don't think a single person looked at my analysis of Pixar box-office revenues. Such is the big challenge of making a 'portfolio'. I was proud to be one of the only people who had considered the effects of inflation on box-office totals and the underlying trends in the film industry. Yet the projects that had garnered the most attention were those that were visually pretty yet analytically shallow.
Most people who try to make this breakthrough will fail. I'm not saying that to discourage anyone, it's just a numbers game. The cream does not always rise to the top. We have produced more educated applicants than we have skilled job openings, and the balance has only worsened since Nelson published.
Sorry for the blackpill, I'm just frustrated. We all want advice from those who have succeeded. The nonfiction space is rife with people who have a success formula to sell you, though they always apply the caveat "--with enough gumption". Yet shouldn't we also learn from the failures? What did they do wrong? We don't know, because they don't tend to write books called 'How Not to Become a Data Analyst'
Here is my top tip as a failed aspirant: above all, try not to get disgusted and outraged with the tech sophists, or else it will be hard to kiss their asses on LinkedIn.
Nelson describes the tremendous relief that came from lifting herself out of the working class into the email-from-Thailand jobs. To hear someone else's story of upward mobility is inspiring because individually considered, it seems like I could follow the same steps. Yet I am a different person I cannot follow her footsteps exactly.
There are simply too few chairs and too many dancers in this party-game. As Nelson mentions, the skill-learning portion is the easy part. The intuitive and emotional battle of networking and applying to jobs is the true test. I'm convinced I could be an excellent data analyst. Yet searching for a job, marketing yourself, is like a whole different job that I'm kind of ass at. I'm cynical and critical--incisive traits that make for a good bullshit detector, the kind of thing you want in a great analyst or investigator. Yet those vary qualities make make LinkedIn and corporate-speak repellent to me.
It's not Nelson's fault, but I'm disgusted with the tech space. It may be sour grapes, but I am glad I changed my mind about this field. They constantly emphasize the 'soft skills' and 'critical thinking' required to become a true analyst, yet the tech space is filled with people who lack judgment and discernment because they have emphasized the technical to the great expense of the human.
Michel Foucault talked about the paradox of disciplinary society. Consider soldiers: they must become intensely powerful specimens of human strength, speed, and determination. They need to be able to shoot-to-kill, to go over the top. Yet their officers would also like them to have the emotional mastery that would allow them to stop on a dime, to turn off the violence instantly and absolutely. Some will be able to make that work, yet dozens of others will be traumatized and scarred by the paradoxical pressures.
I see a similar paradox at plate in elite workers today. They must refine their intellectual abilities and technical skills to a high-point, but must always stop short of questioning the basic parameters outlined by their firm or industry. In interviews, you must show independence and autonomy, but also deference and submissiveness to authority.
While I was still practicing to become a DA, I did some public analysis of data on the Maven analytics platform, an expensive MOOC provider. Recall that MOOCS have a greater than 90% dropout rate. I don't think a single person looked at my analysis of Pixar box-office revenues. Such is the big challenge of making a 'portfolio'. I was proud to be one of the only people who had considered the effects of inflation on box-office totals and the underlying trends in the film industry. Yet the projects that had garnered the most attention were those that were visually pretty yet analytically shallow.
Most people who try to make this breakthrough will fail. I'm not saying that to discourage anyone, it's just a numbers game. The cream does not always rise to the top. We have produced more educated applicants than we have skilled job openings, and the balance has only worsened since Nelson published.
Sorry for the blackpill, I'm just frustrated. We all want advice from those who have succeeded. The nonfiction space is rife with people who have a success formula to sell you, though they always apply the caveat "--with enough gumption". Yet shouldn't we also learn from the failures? What did they do wrong? We don't know, because they don't tend to write books called 'How Not to Become a Data Analyst'
Here is my top tip as a failed aspirant: above all, try not to get disgusted and outraged with the tech sophists, or else it will be hard to kiss their asses on LinkedIn.
July 25, 2025
Saw this at the library and thought it would be a good read. The title might not be scintillating but it does what it says. I had never heard of the author but wanted to know about this roadmap since coding isn't my thing. I had read other guides, looked into it myself, etc. but I wasn't sure what this actually involved.
Nelson takes the reader through her journey of a data analyst and what that can look like for themselves. What it is, what the applications are, who can learn, how to learn, how and where to apply, etc. If I had to sum it up, it is probably a combo of her own professional journey to this and with a career guide. So if you have read anything along these lines, or are a data analyst yourself, I would bet a lot of this is familiar.
But if you're not, there is much to absorb. From the "basics" of where to find jobs (standbys like LinkedIn, Indeed, etc. are here) to specific resources. As someone who has dabbled in this, I was somewhere in between. The "basics" like job boards were familiar, but some of the various resources were not, and so it was a mix of reading for genuine stuff I did not know to skimming the sections on writing a resume, etc.
Did I learn anything new? Yes. Was it game-changing? No, not really, but I would say I found it helpful to have a guide in a book format vs. YouTube video or website or whatever but that's just me. On the flip side, I would have liked a single website or something to click on the various resources she names in the next. I would also posit that if you're short on time and/or want a summary of what she says, flip to the end with the appendices and resources. For some, there might be people who find that most helpful.
I'm glad I read it and might find a way to get a e-reader version for keeps (due to the links to resources) but also did not find it especially illuminating in many ways. Good if you have no idea what a data analyst does but are interested/career changers, early career (think college student/grad). If you're already one and/or have some experience, this is probably not for you.
Borrowed from the library and that was best for me.
Nelson takes the reader through her journey of a data analyst and what that can look like for themselves. What it is, what the applications are, who can learn, how to learn, how and where to apply, etc. If I had to sum it up, it is probably a combo of her own professional journey to this and with a career guide. So if you have read anything along these lines, or are a data analyst yourself, I would bet a lot of this is familiar.
But if you're not, there is much to absorb. From the "basics" of where to find jobs (standbys like LinkedIn, Indeed, etc. are here) to specific resources. As someone who has dabbled in this, I was somewhere in between. The "basics" like job boards were familiar, but some of the various resources were not, and so it was a mix of reading for genuine stuff I did not know to skimming the sections on writing a resume, etc.
Did I learn anything new? Yes. Was it game-changing? No, not really, but I would say I found it helpful to have a guide in a book format vs. YouTube video or website or whatever but that's just me. On the flip side, I would have liked a single website or something to click on the various resources she names in the next. I would also posit that if you're short on time and/or want a summary of what she says, flip to the end with the appendices and resources. For some, there might be people who find that most helpful.
I'm glad I read it and might find a way to get a e-reader version for keeps (due to the links to resources) but also did not find it especially illuminating in many ways. Good if you have no idea what a data analyst does but are interested/career changers, early career (think college student/grad). If you're already one and/or have some experience, this is probably not for you.
Borrowed from the library and that was best for me.
December 2, 2024
Helpful tips and a good guide to make your own strategy to breaking into data analytics. Not everything is going to be relevant to everyone with a book like this so take what you need. Specifically Annie gives you great information about building portfolio projects- which is the biggest hurtle a lot of people get stuck on starting because it feels so overwhelming.
September 28, 2024
good explanation on how to become a data analyst in 2024
Displaying 1 - 4 of 4 reviews