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||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|
|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