Inside the new Galvanize Part-Time Data Science Remote Bootcamp

Table of Contents

• Meet our Expert: Ryan Kasichainula
• Part-Time vs Full-Time at Galvanize
• Data Science Bootcamps vs Data Science Masters
• How to Succeed in a Part-Time Bootcamp
• The Data Career: Is Now the Best Time? 

You may know Galvanize for their full-time, immersive data science bootcamps, but did you know that they recently launched a part-time option? We touched base with Ryan Kasichainula, the instructor for this new part-time bootcamp, to find out how Galvanize’s new immersive part-time program is meeting the needs of busy students while upholding a rigorous Python curriculum. Plus – is this even the right time to change careers into data science? 

Meet our Expert: Ryan Kasichainula

• Ryan is the primary Data Science Instructor for Galvanize’s Part-Time Remote Bootcamp. 
• He graduated with a Master's in Statistics from Texas A&M in 2013 (read on for his comparison of a traditional master's degree vs a data science bootcamp)!
• Ryan knew about Galvanize through company events in San Francisco and decided to move into teaching because he loved mentoring junior data scientists at work.

Part-Time vs Full-Time at Galvanize

Galvanize is well-known for immersive data science bootcamps, but why launch the Part-Time Immersive?

People asked for it! Many students don’t have the financial stability to stop working for a period of time, so they have to keep working or tend to other personal responsibilities. Offering a part-time program opens up a career in data science for those who otherwise would not have the ability. Personally, attending a part-time class was a pathway to improve my own career and wouldn't have been possible if I didn't have a part-time option.

How does the part-time curriculum compare to the full-time data science immersive?

The part-time and full-time curriculum is exactly the same – only the pacing is different. 

The curriculum at Galvanize revolves around Python, machine learning, recommender systems, neural networks, and time series. Throughout the program, students are learning how to identify a problem, how to identify the correct tool to solve the problem, how to choose a tool, and how to use that tool.

What’s the teaching style and format of the part-time class? 

Galvanize hosts the part-time data science program synchronously in the Zoom classroom. Students attend class for six months in total (vs the three months full-time immersive): 

• Two-hour sessions in the evenings on Tuesdays & Thursdays
• Five-hour session on Saturday 9-2 PST

Most of our assignments are expected to be done via pair programming, and we shuffle the pairs. Everyone in this program gets experience using VS Code with a Live Share extension so they can essentially be doing pair coding, switching off, and working on the same code from two very different locations. 

Do applicants need experience in data to apply for Galvanize? 

Having those backgrounds is helpful in preparing for the course, because you already have those coding and math skills, but it's not a hard requirement. We offer pre-course material to get folks up to speed. 

A fair number of my students are middle-school or high-school teachers – not people with data analysis or software engineering backgrounds. 

The application is the same for the Part-Time bootcamp and the Full-Time bootcamp. One important note is that the part-time bootcamp is not intended to be a fall-back option. It is not any less rigorous. A part-time immersive bootcamp is still intensive and can sometimes even be more intensive if you’re juggling multiple responsibilities. 

Data Science Bootcamps vs Data Science Masters

Since you have experience with bootcamps and have a Masters in Statistics – which is better? How do data science bootcamps compare to a university curriculum?

Bootcamps are more up-to-date on industry trends. Bootcamps don’t get as deep into some of the math concepts, but that knowledge is not necessary for most industry positions. For example, I’ve only needed deeper math concepts once in seven years. If someone wants to work in model development or on cutting edge neural networks, then they might consider getting a Ph.D. 

In the Galvanize data science immersive, we also contextualize everything students learn to how it might be used in their future employment or how it might show up on future interviews. My master's program wasn't as career-focused. 

My master's program was focused on SAS (not Python), and after I graduated, I used SAS for a 6-month contract job. Then I used R for 3 years in my first full-time job. After that, I used Python exclusively.

Completing a Master's while working takes three years, compared to the 30 weeks of a bootcamp. If you're becoming a data science practitioner, then a bootcamp is the best way to go. 

How to Succeed in a Part-Time Bootcamp

One challenge in a part-time bootcamp is balancing your other commitments – what’s your advice to someone balancing a part-time bootcamp? 

• Time management: The part-time commitment is 10 hours of class per week plus 10 hours per week on assignments & studying. If working a 40-hour job, consider the 20 hours of class-related time. It's doable, but it is stressful. If you let the coursework take up more time than that, closer to 40 hours, burnout can likely ensue quickly. 
• Accept being a beginner: Data science is broad! Even people who have been doing it for years would still not consider themselves to be experts in every technique we cover in this course. Our class is geared toward: 
• Recognizing the problem
• Becoming familiar enough with the solutions to be able to pick the right one
• Seeking specialization after the program to get more comfortable implementing those solutions

Who are the instructors for the part-time bootcamp? 

I'm currently the only instructor for the part-time program. We will be starting a second cohort in the new year and will be hiring a second instructor to be able to make that happen. 

The Data Career: Is Now the Best Time? 

Regardless of the time commitment or format, why is data a good career path for 2021?

In general, data science is a high-quality job. It’s a respected field so you’ll feel important and valued. You’ll have the opportunity to give input on big decisions. You can see the impact of your work on a product, and you have interesting problems to work on. Plus, data jobs pay really well and seem to be less stressful than software engineering.

• Data scientists do not have the demand or urgency of being on a time-critical schedule, like a data engineer who’s on-call and may have to be at work at 4 am. 
• Data Scientists will be in high-demand so long as the world continues to collect data, which there shows no signs of decline, nor signs of automation in the near future. The parts of data science that do get automated will probably not take away from a data scientist's jobs if they keep up with their skills. 

Is this the right time to change careers into data? What do you say to people who wonder if they should wait until things go “back to normal”?

The most stable jobs are able to be done remotely right now, and one additional underappreciated skill that people are learning in this program is how to work remotely. It can feel unnatural to operate online after collaborating with people face-to-face. Remote bootcamps are preparing people for that kind of online work in a big way.

If you're not used to working remotely, attending an online bootcamp can act as an onramp into that style of collaboration, through group work like pair programming.

What is your advice for students embarking on a part-time program like this? Any tips for getting the most out of it especially if they are trying to change their careers?

Data science in general is an especially good choice if you're curious. If these kinds of questions interest you, data science might be a great fit:

• Do you want to know more about how the world works and you want to use data to do that?
• Do you browse Twitter and wonder if it's possible to automatically figure out which people are in favor or against some idea?
• When Facebook properly identifies someone in a picture, do you ask yourself, “How do they do that? Is that something I can do? Can I do something different with that technology?”

If you're asking yourself those questions, you'll understand data science as a concept, more than slogging through the technical work for it. Having passion and curiosity will help people get through the program more easily. 

For our readers who are beginners, what online resources or meetups do you recommend for aspiring data science?

Two of my favorite resources are the towardsdatascience blog, and the 3blue1brown youtube channel. These both might be too advanced for aspiring data scientists though. For beginners, check out Galvanize’s prep courses and free online data science lessons

Find out more and read Galvanize reviews on Course Report. This article was produced by the Course Report team in partnership with Galvanize.