All posts by Jesse

I'm a Data Scientist and also a fine artist living in San Antonio, TX.

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 jesse.jinna.ruiz@gmail.com.

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 https://www.ncbi.nlm.nih.gov/pubmed/22035711

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. brainmaps.org

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.

Book Review: All You Can Pay: How Companies Use Our Data to Empty Our Wallets


By Jesse Jinna Ruiz

All You Can Pay: How Companies Use Our Data to Empty Our Wallets explains how Big Data companies are not just emptying our wallets but changing our world. Authors Anna Bernasek and D.T. Mongan illustrate through easy to understand stories and thoughtful analysis how the use of data is changing the economy. From price discrimination to dynamic pricing and customization, Big Data is dismantling the traditional free market economy.

But what is the free market and why does it matter? The free market is a market where buyers and sellers “willingly exchange goods and services for mutual benefit.” (p.172) This supposed perfect free market is hypothetical because nothing is actually perfect. But we rely on the free market to establish a few conditions: (1) there’s a large number of buyers and sellers who have power of choice to exchange goods, (2) there’s no transaction costs, (3) there are commodity products on the market (meaning there are lots of products to choose from), and (4) everything runs on information (and hopefully, everyone has access to that information). But as we can guess, the Big Data companies breakdown all of the conditions of the free market.

In a perfect world, everyone would have access to information equally and no one would be able to take advantage of someone because of information. But this doesn’t really exist—not even in the free market—and the Big Data giants increasingly have all the power with access to our data. The Big Data companies also have the power to impose transaction costs and control other aspects of pricing. “Product customization, opaque pricing, and complex contracts are poised to expand from their natural origins in the world of services to all other sectors of the economy.” (p.177) And eventually “the macroeconomic effect of the end of the free market will be a general rise in price levels as the masters of data capture enormous profits. To the consumer, it will be something like living in an airport.” (p.179)

Bernasek and Mongan explain all the mechanisms of control and power that the Big Data companies hold with their data capabilities. The problem is that there is little government oversight and little public knowledge of this growing problem. So the first thing readers should take away from this book is that data is a property that belongs to the people. The authors call for readers to take more responsibility in fighting for the property rights associated with data. “All data is property.” (p.197) And the authors call for collective actions to take control over our data before it’s too late.

There are two tools in particular that the authors cite to achieve successful collective action: the law of property and the law of contract. “Personal data, particularly intimate, extensive, panoptic data, is a physical reality. Data is touchable and ownable. And it seems unarguable that deeply identifying personal data, the granular portraits of our lives and personalities made possible by big data, is owned by the individual it relates to. That data can be sold or rented or regulated according to personal choice. And that’s where the law of contract comes in. Individuals can contractually control the use of their data.” (p.215) All You Can Pay illuminates ethical concerns of data-driven corporations, educates on the economic impact of Big Data and recommends ways to control and alleviate the power imbalance. Read this book if you want to learn more about the economic mechanisms behind data-driven business, the ethical questions that result, and learn part of the history of how data giants became what they are today.

All the Money I Spent in NYC in 2011-12 (And Why I’ll Never Live There Again)

I first moved to NYC in 2006 to attend college at the age of 18. I was very privileged in that my parents paid for everything. After college, I set out to become an artist and the first thing I did was moved from the Upper West Side to Flatbush, Brooklyn.

I worked as a babysitter and I paid for all of my expenses. No more help from mom and dad. I had a one-bedroom apartment that I shared with a friend who lived in the living room. I also rented a studio space to paint in. I didn’t make enough money to survive so I had to rely on other (sporadic) forms of income and being extremely cheap.

My exit strategy was to go to graduate school. I spent the entire summer and fall preparing my applications to graduate schools. In the winter and spring of 2012, I was struggling financially and looking forward to moving out of Brooklyn. After getting accepted into graduate school, I finally left Brooklyn in July of 2012. I lived in Brooklyn for just over a year.

So here is the data… I recorded every single penny that I spent while I lived in Brooklyn from October 2011 – July 12, although the time I actually lived there was May ’11 – July ‘12. This data focuses only on expenses, not earnings, because I earned some of this money through very shady means, which I am not proud of, because one of my jobs was suddenly cut because of layoffs. Nevertheless, the good things that came out of this period are that I learned how to manage my own finances on a very tight budget and I learned the NYC hustle. Rent was my biggest expense at $500 a month. I shared a one-bedroom apartment with a friend. It cost $1000 total in Sunset Park, Brooklyn in 2011. I lived in the bedroom and my friend lived in the living room. We split the rent evenly because my friend was generous. But it was not the most comfortable living arrangement. The second biggest expense was food, including cost of groceries, restaurants/eating out, and snacks/food on the go. This accounted for between 10-30% of my monthly expenses. Next, a monthly unlimited metro ticket was $104 and sometimes I had to spend more if I lost it. Finally, I spent a good amount of my income on both my art studio and art supplies, sometimes up to 16% of my monthly expenses but not any more than that.

Chart of monthly expenses living in Brooklyn, NY
Chart of monthly expenses living in Brooklyn, NY

Over the course of 10 months, the two biggest anomalies occurred during Christmas holidays and during my move out of Brooklyn in July. In December, I spent extra money on a plane ticket home, gifts and mailing gifts. Similarly, in July, I spent money on an airplane ticket, mailing all of my belongings (about 30 boxes) via USPS to my new home, and hotel costs.

Plot of Total Expenses Over Time

Overall, I was able to consistently keep my monthly expenses below $1800. But there was an upward trend to spend more as I lived in Brooklyn longer excluding the month of July when I moved. This was accounted for by a change in my living situation. I got a new roommate and I elected to pay more for rent because I lived in the private bedroom. Perhaps I also got better at tracking my expenses too.

Granted, if I stayed in Brooklyn, I could have found a better job to live more securely and earn more income. But this was in 2012. Cost of living has sky rocketed since then. For a single artist with no debt, living so cheaply in NYC is possible but, let’s be honest, living in purely survival mode is no way to live.

It was beautiful to live in an artistic epicenter like Brooklyn. I learned a lot about myself and about making a living. But I would not choose to live there again because of the financial struggles. First, the cost of living is exorbitantly high. Second, the quality of life is poor—read: smelly, loud, dangerous and stressful. Third, I was far from my family. Fourth, I didn’t have reliable income. Fifth, the weather sucked. Again, I love Brooklyn but I would never live here again – not even if I were making boat loads of money. Why? Because I can live on a similar budget very comfortably in many different places. The costs of living in NYC are just too many.

Plot of expenses broken down by category over time

Women in Technology

By Jesse Jinna Ruiz

In a scholarship application for a coding boot camp, I was asked the following question and it really bugged me for a few reasons:

In a traditionally male dominated field, what benefits do you think women can bring to the class environment and technology field? What makes you the most deserving candidate for the scholarship?

Here was my response:

There are two answers to this question that address what is asked. The first answer accepts the premise there’s something inherently different between men and women. And traditionally, most people accept this premise and a response might list the inherently different and beneficial qualities of “women” to include, for example, diverse group dynamics and work styles, solid managerial qualities, strong empathetic perspectives and etc.

However, the second type of answer would not accept the premise that there is something inherently different between men and women and would even go so far as to argue, in a radical feminist fashion, that the qualities of “women” and “men” are not consistent with gender but instead social constructs that societal/cultural norms instill in artificial types (‘men’ and ‘women’).

Obviously, I side on the radical feminist perspective to answer this almost misogynistic question about what benefits women might bring to the table (if only they had shot). Women bring benefits to their work or class environment as much as any other person no matter their gender. But not categorically women qua women.

The question isn’t a bad one for a scholarship application, but it is discouraging to ask what women can contribute to the technology field. What about other gendered folx? What have women already contributed to the field? And what can men or persons in positions of power and privilege do to enable minorities to impact the field? These are the real questions that should be asked.

 So herein lies the answer to the deeper question: people learning/working in the technology field or any field should acknowledge the disparities of race, gender and sexual orientation on larger scales. And people in power should exercise their influence and authority to institutionally empower women, minorities and other gendered folx within the field and change that field and society in turn. The field can benefit itself by stripping away barriers and assumptions that have been taught through generational stereotypes/norms. This work needs to happen institutionally.

I don’t believe men and women are inherently different. I think society constructs limitations and barriers and institutions and businesses can fight and correct them. Being a gay, biracial cis-woman who just entered the technology field, I want to work with people that respect and acknowledge the need for equality and dignity for all. And I’d like to exist in any place as myself and not a gender.

Tips for Choosing and Funding a Coding Bootcamp

By Jesse Ruiz

Bootcamps or career accelerator programs are short term education programs designed to help you learn new skills and find a job. If you are thinking about attending one, I will share some tips about finding a bootcamp, my story about how I chose to attend Codeup in San Antonio, TX and how I got funding to attend.

My first tip is to spend at least a few months to a year researching the topic you want to study and the bootcamps available. There are tons of resources online to learn programming. I will provide a detailed table below of the courses I took, most of which are free. While you are learning the basics, start to learn about the bootcamps that teach this subject, read through bootcamp curriculum, take notes on tuition costs and start dates and note whether or not they provide scholarships. This first step is crucial for figuring out if this topic is something you are genuinely interested in.

Secondly, when you start researching bootcamps, you will find that cost of tuition can be high. The best strategy is to look simultaneously look for funding and bootcamps. First look locally and seek out local and federal grants to attend based on being under-employed, unemployed or under-represented in the field (minorities). I was only able to find funding because I met with a local career training program which enabled me to access local and Department of Labor funds. If you don’t meet the criteria of being being under-employed, unemployed or under-represented in the field, then don’t worry! There are still other scholarships and loans out there.

Warning!—only start to contact/call up the bootcamps when you are comfortable with your basic skills in programming (or whatever you are trying to learn) and when you are committed to attending. Bootcamp admissions will aggressively seek you out. They want you to attend their courses. You should have clear intentions about what you want to do, how much money you want to spend, and how good you are at programming. Just be honest with the people you speak to about your circumstances. This is a process so take your time. Often, if you get rejected from a bootcamp, you can still re-apply later.

Lastly, there are almost always loan companies that specialize in loan for students of bootcamps. If the cost of tuition is still prohibitive, you can consider loans as your last option. In most cases, these loans can be repaid easily with the job you will (hopefully, most likely) get after you graduate. Some bootcamps offer refunds if you don’t get a job (with conditions) and others offer deferred tuition where you don’t pay anything until you get your first job.

As for my experience, I learned about Data Science online and spent 10 months researching the subject and bootcamps. I took a slew of courses online to learn the basics, which I will share below. Then I started to apply to bootcamps. Ultimately, I was able to find Codeup in my hometown. I visited their campus and spoke with their admissions representative about funding. I loved that this school was in my hometown, so it was a practical choice for a full-time program. I also liked the instructors and admissions people that I met. The admissions person told me about their funding options and sent me to a local career training program, which informed me about local and federal grants that were not easily accessible online. Working with this local program was long and uncertain but I stuck with it. The real reasons I was able to get funding through them were because I had been under-employed for years, I had used up all my savings, I was living at home with family and I was unemployed at the time that I applied for the funding. In the end, I chose Codeup because I was able to find funding, it was in my hometown and I genuinely liked the people I met there, especially Maggie Giust, the Senior Data Scientist.

I will share a table of the exact funding amounts that I got below. This will probably not be the norm. I got extremely lucky with my funding.

All in all, this whole process is precarious, scary and hard. You should give yourself plenty of time to research and learn about the process, the bootcamps and the subject you are trying to study.

If you need any advice, please feel free to contact me directly. And if this was helpful please send it along to anyone you think would benefit from it.

List of resources for researching bootcamps

https://www.switchup.org/

https://www.coursereport.com/best-coding-bootcamps

List of the Courses I Took (In order that I took them)

Name of Course Notes Difficulty/My Critique & Experience Link to course
1. Data Science & Analytics Career Paths & Certifications: First Steps with Jungwoo Ryoo **Requires sign in. By pass by using local library access or your university’s access, i.e. “Sign in with your organization’s portal”   Easy Lynda.com (search title after you sign in)
2.Statistics Foundations with Eddie Davila Same as above Easy/Medium Lynda.com (search title after you sign in)
3. Excel 2016 Essential Training with Dennis Taylor Same as above Easy, Run through videos at 2X speed Lynda.com (search title after you sign in)
4. A Gentle Introduction to Programming Using Python Utilized Python 2. Required setting up Python environment on your computer. Medium, Very fast paced. Not a good idea to learn Python 2. Stopped course halfway MIT 6.189 OCW
5. Learning Path: Becoming a User Experience Designer This is a group of courses meant to teach UX. **Requires sign in. By pass by using local library access or your university’s access, i.e. “Sign in with your organization’s portal” Medium. Mostly lectures. Lynda.com (search title after you sign in)
6. Python Tutorial Took a couple of days to complete. Sign up for free; doesn’t require setting up an environment on your computer Easiest, short exercises. Mode Analytics
7. Learn Python 2 Sign up for free; lots of exercises; doesn’t require setting up an environment on your computer. Easy/Medium; took about a week to complete Codecademy Python
8. Data Structures Fundamentals Enroll for free on EdX, self-paced Medium/Hard; Didn’t understand most of it; stopped halfway. EdX UCSD Data Structures Fundamentals
9. Introduction to Algorithms MITX Enroll for free. Video lectures and HW assignments Hard. Stopped after 5 lectures. MIT 6.006 OCW
10. Statistics and Probability Khan Academy Join for free. Very robust website with quizzes and video lectures. Easy/Medium; Spent about 4 weeks on it, slowly. One of my fav. sites. Khan Academy Stats and Prob
11. Linear Algebra Khan Academy Join for free. Very robust website with quizzes and video lectures. Easy/Medium; Spent about 2 weeks on it, slowly. Khan Academy Linear Algebra
12. Introduction to JavaScript: Drawing and Animation Join for free. Very robust website with quizzes and video lectures. Easy/Medium; Spent about 2 weeks on it, slowly. Khan Academy Intro To JS
13. Data Science Math Skills, Duke University Join for free. Audit courses for free. Some times you can get stuck when they ask you to pay in order to submit quizzes. If this happens to you, just skip the quizzes or sign up for a “free trial” and cancel before you are charged. Easy, work through exercises slowly. Spent about 1 week on it. Coursera, Data Science Math Skills, Duke U
14. Linear Algebra for Machine Learning, Imperial College London Same as above Easy/Medium; Spent about 2 weeks on it. Didn’t learn the page rank assignment because of the pay wall. Coursera, Linear Algebra for Machine Learning, Imperial College London
15. Basic Statistics, University of Amsterdam Same as above Easy/Medium; Spent about 2 weeks on it. Made a new account so that I could get a ‘free trial’ to submit quizzes. Slowly did all work. Coursera, Basic Statistics, University of Amsterdam

Other courses I dabbled in and other resources:

Basic HTML and HTML5 and CSS, FreeCodeCamp.org

The Open Source Data Science Masters, Created by Clare Corthell, http://datasciencemasters.org/

List of 5-Day Data Challenges, Kaggle, https://www.kaggle.com/rtatman/list-of-5-day-challenges/

Siraj Raval, How-To Videos and Curriculum on Github and Youtube, https://github.com/llSourcell/Learn_Data_Science_in_3_Months