Finding My First Job in Tech

The psychology of uncertainty is the biggest challenge when searching for a job. There’s ups and downs and rejection. In this article, I will describe my approach to job hunting, including goals, tips and techniques I used.

I landed my first job in technology after about a year and a half of learning to code, including a 4.5 month coding bootcamp. I was lucky to have a career advisor / advocate through the coding bootcamp that I attended. But even so, the process was difficult, long and unpredictable.

TDLR; Having an effective resume, using LinkedIn and actively networking are the most important tools for finding a job in tech. Here are the stats:

Number of days actively applying91
Number of applications sent209
Number of phone interviews6
Number of in person interviews4
Number of offers2
Number of rejections after interviews6
Ghosted after phone/in person interviews2
Success rate (success=job offer)0.95%
My job hunt
  • The Resume

Creating a great resume in four different formats is essential: one page resume – both for a human reader and a machine readable version – and also a two page resume – both for human and machine. Since I have a background in fine arts and design, I created something myself using Adobe Illustrator. If you don’t have design skills, then it is safe to rely on online templates but remember to customize layout for your specific strengths. For example, if you have little to no work experience but good projects, then list your projects at the top and the work experience at the bottom.

A less ethical hack is to copy, paste and hide / mask the job description with all its keywords directly into your resume so that when the resume is parsed through a machine reader, your resume will pass any screenings. Because I don’t know the efficacy of this hack, I wouldn’t recommend it. Also, I tried it and it didn’t seem to help so don’t waste your time.

  • Networking

Any introvert knows that this is the worst part about finding a job. I went to several job fairs and struggled through them. These job fairs did not lead directly to a job offer but I believe that in some mystical, intention-setting way, they helped me find a job. I would only recommend a job fair to someone who has researched every company that will be in attendance and someone who had a solid set of business material to hand out and a confident mastery of their own skills and what they can offer to a company. What this means is that you know how to sell yourself, or to put it another way, you know how to describe your skills and goals as a professional in your field. If you are less confident about yourself, but can still BS your skills, then give it a try. But it is essential that you research all companies in attendance and genuinely want to work for them.

Here’s the thing about networking—it’s a reflection of the wildly inefficient system that networking remains the best and most vital way to find a job. You have to know someone to get your foot in the door. So this means that applying to jobs through online portals without knowing anyone at the company is useless. It sucks to hear/know this, but it’s true.

  • Side note:

As a hardcore introvert, it took me a long time to realize what networking really is. I work best one-on-one. So ‘networking’ was always a nightmare. Here’s the basic idea though: Let’s say you want to meet and interact with 100 people. Doing this one person at a time will take awhile… Let’s say that at 1 person per day, it will take 100 days (since you gotta walk around and find a person willing to really speak with you)! But if you use networking and meet 5 people and get to know them well, then you can leverage each of those 5 people’s networks in order to gain access to a myriad of other people. Let’s say each of those 5 people you met has an average network size of 50, then you gain access to 250 people with only the amount of work it took to meet 5 people! Magic! That’s only 5 days to meet 250 – whereas with the one-by-one approach it took 100 days to get 100 people. It’s obvious that this method of leveraging people’s networks is more effective.

Okay, so now that I explained how important networking is, my last two tips are about networking.

  • LinkedIn

You have to get LinkedIn Premium (or free trial of it) in order to be able to send messages to people. Aim for contacting one person a day about opportunities at the company they work for. But you have to be strategic about it. Don’t just copy/paste generic messages in bulk to randos.

Here’s the strategy I took with LinkedIn. I applied to a lot of jobs through company websites, not knowing what I was doing. But for each company and job that I was really excited about I hunted down a person from that company and sent them a message using LinkedIn. I either found a HR person or a data person since that’s the field I was entering. I asked to speak in person with them, inviting an interview opportunity and showing confidence and initiative.

This method of cold-calling someone is controversial. But I was able to get interviews this way.

  • Friendship / sanity

Like I said before, the job hunt will test your sanity and morals. Making friends with other job-hunters is a good way to keep yourself from isolation and depression. Just having someone to speak with about the struggles of job hunting is the best way to cope with the emotions.

I was very lucky have friends from my bootcamp. I organized an ‘accountability buddy’ group with friends who were also searching for jobs. We met in person once a week to sit together in a café and apply to jobs and talk. I would recommend doing the same or at least having a mentor or someone to talk with about your process.

  • Time

Lastly, and most importantly, you must give yourself time to go through the process of finding a job. And it is a process.

The best piece of advice I ever got was from a former art teacher. “Favor yourself with your time”. Give yourself plenty of time to go through the process. You’ll get better results and, although it is hard because your livelihood is on the line, trust that everything will work out in the end.

Spoiler alert – the job hunt never really ends.

Thanks for reading!

How I Went From 28k to 70k in One Year (And a Half)

By Jesse Ruiz

In about a year and a half (counting self-teaching), I went from earning around 28k as an adjunct professor of art (plus side jobs) to earning around 70k as a developer.

I will discuss my background and the possibility that you can achieve a similar career change. In general, I highly recommend coding bootcamps to enter the tech industry. But it is a difficult task and it’s not to be entered without adequate preparation and planning.

In this article, I will focus on a bigger picture discussion of why and how I changed careers. I have written other articles that discuss the online courses that I completed before I attended data science bootcamp and a review of the bootcamp I attended. For these articles, please refer to my blog.

In short, the reason I jumped on the bandwagon is because I was financially dependent upon my parents and not earning enough to live independently. In this sense, I am a typical creative millennial. I have a few prestigious (useless?) degrees in art and philosophy. However, a few years after completing my graduate degree, I found myself broke and very lost financially.

I taught fine art as an adjunct professor for a few years but this was sporadic temporary work without any benefits or job security. So while I was teaching and living at home with my mother, I started to research careers, taking personality and strengths tests and creating lists.

The internet told me I would be a good accountant or software engineer. Then I somehow discovered data science and after reading about the field in depth, I knew it would be good for my personality and strengths. So my first piece of advice is to know your strengths, weaknesses, personality traits and how they would best suit a potential career.

Now that I knew what I wanted to do, I needed to tackle how? I thought about going back to school to get another Bachelors or Masters degree. But then I learned about bootcamps and I was convinced that this was my best option since they are faster than traditional schooling. My second piece of advice is to consider all your options for educating yourself. Bootcamps are the quickest way into a job.

The decline of the traditional education system should not be understated. During my higher education, while I was very well taken care of because of my privilege, I was not trained nor guided to any specific career. My parents were supporting me financially and although they wanted me to become a lawyer, they supported my decision to pursue fine arts as a profession.

My higher education taught me resourcefulness, great learning habits, adversity and hard work. I did not take any internships. I did work study jobs and aspired to do creative and intellectual things as you do in prestigious institutes of learning. But frankly I was very depressed and struggled through most of undergrad. In the end, I learned more about myself than about career and the real world.

So my belief in bootcamps stems from an appreciation of their efficacy but also I acknowledge the purpose and strengths of traditional higher education. Bootcamps are specifically designed for placing students into jobs whereas higher education advances personal development and intellect (for most).

Getting back to my point, I was enabled to find a job in the data science field in such a short period of time because of the bootcamp I attended and because of the self-teaching I did beforehand. I doubt that I would have been able to be so successful in the bootcamp if I had not gone through undergrad/grad school and self-teaching. My strengths in math and logic helped me get through the dense statistics and machine learning algorithms.

The biggest challenge of bootcamp is the speed and intensity of the curriculum and the tenuous job-hunting period after graduating. No one tells you that the job hunt is the hardest part because it’s a psychological battle that creates self-doubt, competitiveness and uncertainty. My third piece of advice, if you attend a bootcamp, is to make sure you find one that has a great career advising team that will guide you through the entire process.

In the end, bootcamps were created because of the high demand for skilled workers and the training you receive is highly valued. So if you are thinking about attending one, please consider it seriously and find people who have gone through it to discuss your questions.

I am hopeful that this article will be helpful for those of you who are actively searching to advance or start your careers in the tech world. If you have any questions, please reach out!

Review of codeup, the most expensive bootcamp in the usa

I graduated from Codeup in June 2019 as part of Ada cohort, the first Data Science cohort. In this article, I will review Codeup, giving pros and cons of the school with some attention to the data science program in particular and also discuss my experience in the program. My opinions will differ from others because of my particular background. I am an artist, a self-taught programmer with less than 1 year of experience with Python (at start of program), and I have a very privileged educational background. I am from San Antonio, TX (where Codeup is located) and was living there before I attended, and I got generous funding to attend the data science bootcamp.

TLDR: Codeup is a great school with many resources and great instructors. I believe the great curriculum, instructors, network and potential for financial aid make the cost worth it.

I first heard about Codeup, a small career accelerator school, because of their web development program. When their data science program was announced, I was very excited. The staff and instructors at Codeup were my first selling point as I was really impressed by their Senior Data Scientist, Maggie Guist, and their Director of Strategic Partnerships, Stephen Salas. Before I applied to the program, I visited the campus and met with Maggie. Like when previously choosing what graduate school to attend, I followed my instincts by seeking out cool instructors and people that I get along with.

The application process was straight forward with small technical tests, short answer questions and longer essay questions for scholarship applications and finally a phone call interview with someone at Codeup. In my case, I spoke with Stephen Salas. I got accepted and during my decision-making process, I had to determine my financial aid situation because I had no money. Codeup recommended a number of financial aid options including government grants, scholarships, and personal loans, which was the worst-case scenario. I wrote about the financial package I got in the previously mentioned blog post.

In addition, I spoke with alumni from the web dev program and learned about the pace of the program and the experience of attending a bootcamp. I was terrified of choosing the wrong thing to study. Would I really like it? Was I giving into corporate, capitalist America? I found that most people I spoke with really liked Codeup and found the whole experience transformative in a positive way.

I accepted and secured my spot in the program and we were given a number of online courses for preparation. I had 2 months to complete them and I went through all the courses carefully. Ultimately, when we started the program it was very slow at first. I was expecting this because of what others told me. The first week of the program were exciting as we focused on understanding the full data science pipeline but then it quickly got boring as we learned the basics of Python. However, I was appreciative of this ‘slow’ time because I really struggled with the 9-5 routine. As the program progressed and we did our first projects with real data, I became somewhat depressed as we were tackling business concepts that I didn’t understand. Everyone else in the class seemed to understand business principles like churn, attrition and customer acquisition and even excel at giving insightful business recommendations based on these and other factors. But having no experience in ‘business’, I struggled with this the most.

So my first critique about the program would be lack of teaching business concepts and I’d recommend more business oriented instruction.

While the program progressed from Excel, Python, basic statistics, and SQL, we moved into the fundamental data science methodologies. This was the meat of the program and what I enjoyed most since we got a good taste of different methods in data science practice. We had regular group projects on each of the methodologies we learned, which was useful and instructive. Each project was followed by a class presentation which was very nerve-wracking. But by the end of the program, it got easier and we all started to excel at this.

The second critique of the program was time since we didn’t have enough to cover some topics and the last few weeks of instruction were very rushed. However, they changed the program length to address this already.

The last two weeks of the program were wholly committed to completing a group capstone project which we were able to design ourselves. But since these were group projects not everyone got to work on a project that they designed. Finally, we presented our findings to a group of employer partners at the very end of the program. Because I am interested in design research and I got to design a project idea that I was passionate about, I found the capstone project proposal process challenging, fun and exciting. At times the process was confusing to some people and maybe even unfair if someone’s project was not chosen. However, the overall experience of working in groups was undeniably valuable and the instructors did a fantastic job creating groups and guiding our capstone projects.

My experience of Codeup was positive overall. The best parts of Codeup were the instructors and staff, the very smart students that I met while attending and the large network of partnerships that they have established over the years. In particular, their relationship with local organizations helped me find enough financial aid to attend, which was incredible. They also offer scholarships that were very useful.

If you have any questions about Codeup or bootcamps in general, I’d love to help if I can and you can email me at

Aesthetic Epistemology: A Review of Erna Fiorentini’s Article “Inducing Visibilities…”

By Jesse Jinna Ruiz

Original article:Inducing visibilities: An attempt at Santiago Ramón y Cajal’s aesthetic epistemology” / Studies in History and Philosophy of Biological and Biomedical Sciences 42 (2011) 391–394 1

Fiorentini’s study on the scientist Santiago Ramón y Cajal, the father of neuroscience, introduces the idea of “aesthetic epistemology” to describe the method by which Cajal studied histology. Histology is the study of the anatomy of cells and tissues of plants and animals using microscopy and hence by its very nature it is a field of study where we cannot directly observe the thing studied.

“Aesthetic epistemology” describes a form of knowledge production where visualizations are created to make something visible that was hidden to the observer and also improve the sensibility of the observer. Around 1887 Cajal improved upon a staining technique to make visible the neuronal structures of the human cerebral cortex, a part of the body so densely packed with neurons that it cannot be viewed with standard microscopic tools. By using this staining method and creating extensive detailed drawings of his findings, Cajal was able to “induce visibility” or create visualizations of test results that he then pieced together to represent deeper knowledge about them. Hence, Cajal created drawings that represented the information found in the staining technique results as posited visualizations of the actual (invisible) neurons. His aim was not to show what a neuron in the cerebral cortex looked like but also to explain the whole system and its functions.

This process of extracting and visualizing data to form knowledge is what Fiorentini terms “aesthetic epistemology”. In her own words, “Cajal’s highly sophisticated drawings do not reproduce a given three-dimensional visibility, but rather induce an advanced form of it.” (Fiorentini, p. 393) Hence, Fiorentini argues, the induction of visibility requires not only advanced visualization techniques but those visualizations are constitutive of forms of knowledge production. “Cajal’s strategy of visibility induction referred to rational and aesthetic visual sensibility likewise, and considered both to be constitutive elements of knowledge production.” (Fiorentini, p. 394) Part of this process entails an aesthetic of sorts because the artist-scientist rendered drawings by hand, teasing out knowledge through the very process of drawing.

Looking at Cajal’s drawings side by side with recent brain imaging visualizations shows the surprising accuracy by which Cajal was able to induce visualizations of the neurons in the cerebral cortex.

(1) & (2) From Erna Fiorentini’s Article “Inducing visibilities: An Attempt at Santiago Ramon y Cajal’s aesthetic epistemology”1(3) Golgi-stained neurons from somatosensory cortex in the macaque monkey. 2007.

The concept of inducing visualizations is an implicit part of data visualization within data science. Data is typically divorced from the things that they quantify, and typically data visualizations are representations of the numbers but not the subject described by those numbers. In other words, merely maps, graphs and charts. Hence, data visualization specialists typically rely on writing to create meaningful stories about data.

Cajal’s work shows the promise and possibility of using art as a form of knowledge production. It is apt for data visualization specialists to use the concept of inducing visibilities and aesthetic epistemology to incorporate aesthetics and art practices into their work whenever possible. It is also highly encouraged that artists learn to become not only data literate but experts in data science in order to pave the way for advancement in the field of data visualization.

Book Review: Embodied Cognition by Lawrence Shapiro

By Jesse Jinna Ruiz

Embodied Cognition by Lawrence Shapiro is a thorough and incredibly useful introduction to the emergent philosophical field called embodied cognition. Shapiro discusses three major schools of thought currently competing in the problem space of cognition. These schools are umbrellas for different hypotheses, which are competing against the standard cognitive science approach to cognition.

The standard cognitive science approach to cognition analyzes cognition in terms of computations. In this way, the body is a kind of receiver of information and cognition emerges from the computational processes that happen between the body and the world. The result is a focus on these computational processes and less concern with the interaction between the body and its environment.

The first school of thought competing against standard cognitive science is the conceptualization hypothesis. Instead of just receiving information from the world, computing things and so forth, the conceptualization hypothesis says that the unique constitution of the human brain and body gives us certain concepts that give rise to cognition. The body is seen as a unique interpreter of stuff through which we get cognition and every unique type of body (e.g. species of animal) has specific cognitive abilities in virtue of its body.

The second school of thought competing against standard cognitive science is the replacement hypothesis which directly aims to replace the standard cognitive science approach to cognition. The replacement theorists think its methods and theory are better because they do not see cognition as computational but instead as a dynamic and constant relation between body, world and mind. In this way, the body is a dynamic system deeply intertwined with its environment.

The third and last school of thought competing against standard cognitive science is the constitution hypothesis, which aims to show that cognition extends beyond the mind. The body is a hybrid of both its mind (internal to the body) and things outside the mind (like other parts of the body or even things outside the body). So instead of cognition as a computational model where the body receives stimuli from its environment through its body, the body is a unified whole with different components and cognition takes place within the mind but also extends beyond the brain to different component parts.

Now, these hypotheses all have their objects of study. I won’t go into the studies themselves because it gets confusing pretty fast. However, in the concluding remarks, Shapiro assesses the strengths and weaknesses of each school. The hypothesis that comes out on top is the constitution hypothesis because it can work in harmony with standard cognitive science and contribute beneficial insights to the robust and plentiful methodologies and tools of standard cognitive science. This book is a great introductory book for an academic setting or for highly motivated readers who are interested in the philosophical ramifications of cognition. However, the bulk of the material is not easy to get through if you have little to no experience with philosophy or cognitive science fields.

Data Scientist