Resources and my experience with self-studying, boot camps, and financial aid
Boot camps or career accelerator programs are short-term education programs designed to help you learn new skills and find a job. I recommend boot camps for experienced folks switching into tech from different fields. The real value in boot camps is the career placement assistance that they provide. But boot camps aren’t for everyone so I recommend doing lots of research before considering attending one.
I will share some tips about finding a boot camp in the USA, my story about how I chose to attend Codeup in San Antonio, TX, and how I got funding to attend. I chose to attend a boot camp for data science but I believe my tips apply to other subjects like UX/UI, web development, and other fields in tech.
Disclaimer: My experience finding funding for boot camp is not typical. I was extremely lucky. I don’t want to plant a false idea that finding full or 100% financial aid for boot camp is easy or possible. Most people have to take out loans for boot camp. However, I believe finding at least some funding is possible.
My first tip is to spend at least a few months to a year researching the topic you want to study and the boot camps 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 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 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 the boot camps when you are comfortable with your basic skills in programming (or whatever you are trying to learn) and when you are committed to attending. Boot camp admissions will aggressively seek you out. They want you to attend their programs. 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. If you get rejected from a boot camp, you can still re-apply later.
Lastly, there are always loan companies that specialize in loans for 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.
Although I didn’t take out a loan for boot camp, I think about loans in this way: If you take out a loan and need to repay it after you find your first job, the only real sacrifice you are making is perhaps a few hundred dollars a month. So maybe you don’t get a luxury apartment and instead something cheaper for the first few years of your career. Not much of a sacrifice if you are really committed to working for a career in tech.
In my experience, I learned about data science online and spent 10 months researching the subject and boot camps. I took a bunch 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, tedious, and uncertain (they were very unorganized) 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. I chose Codeup because I was able to find funding, it was in my hometown, and I genuinely liked the people I met there.
I will share a table of the exact funding amounts that I got below. This will probably not be the norm because I got extremely lucky with my funding. Everyone’s circumstances, background, and financial situation will vary greatly. I am sharing my experience with as much transparency as possible to illustrate one path to attending a boot camp. There will certainly be many different paths! So don’t be discouraged if your circumstances seem dire or different than mine were.
All in all, this whole process is precarious, scary, and hard. You should give yourself plenty of time to research and learn about bootcamps and the subject you are trying to study. Coding boot camps are not a method to get rich quick! If you need any advice, feel free to contact me directly. And if this was helpful please send it along to anyone you think would benefit from it.
Lastly, if you are thinking about attending Codeup or any other bootcamp, please feel free to contact me if you have other questions and use my name as a referral if you get into Codeup. I always split my referral bonus with people. Good luck and best wishes!
Below are all the resources that I personally used to study Data Science on my own before attending bootcamp.
List of resources for researching boot camps
https://www.coursereport.com/best-coding-bootcamps
List of the Courses I Took With Notes (Self-study, in the order that I took them)
1. Data Science & Analytics Career Paths & Certifications: First Steps with Jungwoo Ryoo
Notes: **Requires sign in. Bypass by using local library access or your university’s access, i.e. "Sign in with your organization’s portal"
Difficulty/My Critique & Experience: Easy
Link to course: Lynda.com (search title after you sign in)
2. Statistics Foundations with Eddie Davila
Notes: Same as above
Difficulty/My Critique & Experience: Easy/Medium
Link to course: Lynda.com (search title after you sign in)
3. Excel 2016 Essential Training with Dennis Taylor
Notes: Same as above
Difficulty/My Critique & Experience: Easy, Run through videos at 2X speed
Link to course: Lynda.com (search title after you sign in)
4. A Gentle Introduction to Programming Using Python
Notes: Utilized Python 2. Required setting up Python environment on your computer.
Difficulty/My Critique & Experience: Medium, Very fast paced. Not a good idea to learn Python 2. Stopped course halfway
Link to course: MIT 6.189 OCW
5. Learning Path: Becoming a User Experience Designer
Notes: 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"
Difficulty/My Critique & Experience: Medium. Mostly lectures.
Link to course: Lynda.com (search title after you sign in)
6. Python Tutorial
Notes: Took a couple of days to complete. Sign up for free; doesn’t require setting up an environment on your computer
Difficulty/My Critique & Experience: Easiest, short exercises.
Link to course: Mode Analytics
7. Learn Python 2
Notes: Sign up for free; lots of exercises; doesn’t require setting up an environment on your computer.
Difficulty/My Critique & Experience: Easy/Medium; took about a week to complete
Link to course: Codecademy Python
8. Data Structures Fundamentals
Notes: Enroll for free on EdX, self-paced
Difficulty/My Critique & Experience: Medium/Hard; Didn’t understand most of it; stopped halfway.
Link to course: EdX UCSD Data Structures Fundamentals
9. Introduction to Algorithms MITX
Notes: Enroll for free. Video lectures and HW assignments
Difficulty/My Critique & Experience: Hard. Stopped after 5 lectures.
Link to course: MIT 6.006 OCW
10. Statistics and Probability Khan Academy
Notes: Join for free. Very robust website with quizzes and video lectures.
Difficulty/My Critique & Experience: Easy/Medium; Spent about 4 weeks on it, slowly. One of my fav. sites.
Link to course: Khan Academy Stats and Prob
11. Linear Algebra Khan Academy
Notes: Join for free. Very robust website with quizzes and video lectures.
Difficulty/My Critique & Experience: Easy/Medium; Spent about 2 weeks on it, slowly.
Link to course: Khan Academy Linear Algebra
12. Introduction to JavaScript: Drawing and Animation
Notes: Join for free. Very robust website with quizzes and video lectures.
Difficulty/My Critique & Experience: Easy/Medium; Spent about 2 weeks on it, slowly.
Link to course: Khan Academy Intro To JS
13. Data Science Math Skills, Duke University
Notes: 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.
Difficulty/My Critique & Experience: Easy, work through exercises slowly. Spent about 1 week on it.
Link to course: Coursera, Data Science Math Skills, Duke U
14. Linear Algebra for Machine Learning, Imperial College London
Notes: Same as above
Difficulty/My Critique & Experience: Easy/Medium; Spent about 2 weeks on it. Didn’t learn the page rank assignment because of the pay wall.
Link to course: Coursera, Linear Algebra for Machine Learning, Imperial College London
15. Basic Statistics, University of Amsterdam
Notes: Same as above
Difficulty/My Critique & Experience: 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.
Link to course: 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
Summary of Financial Aid:
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.
