All posts by Jesse

I'm a data engineer and artist living in New Haven, CT.

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

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 (search title after you sign in)
2.Statistics Foundations with Eddie Davila Same as above Easy/Medium (search title after you sign in)
3. Excel 2016 Essential Training with Dennis Taylor Same as above Easy, Run through videos at 2X speed (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. (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,

The Open Source Data Science Masters, Created by Clare Corthell,

List of 5-Day Data Challenges, Kaggle,

Siraj Raval, How-To Videos and Curriculum on Github and Youtube,

Update: As of October 2019, I have learned that Project Quest no longer has funds from the Department of Labor, which was the majority of my funding. Please contact Project Quest directly for more information about the resources that they currently provide.