ARTICLE

Preparing Yourself for a Job in Data Science, Part 1: bootcamp

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis

Going through a bootcamp

A bootcamp is an 8–15 week intensive course put on by companies like Metis and Galvanize. During the bootcamp you spend 8+ hours every day learning data science skills, listening to industry speakers, and working on projects. At the end of the course, you’ll usually present a capstone project to a room full of people from companies looking to hire data scientists. Ideally, your presentation gets you an interview and then a job.

What you learn

A good bootcamp has a syllabus which is highly optimized to teach you exactly what you need to know to get a data science job and little more. This goes beyond technical skills and includes opportunities to work on projects and network with people. Here is more detail on what you should expect the program to cover.

Skills

Bootcamps are a great supplement to an existing education. For example, if you’re someone who has worked as a software developer for years, a bootcamp can quickly fill in the details you need around the math and statistical techniques and how to think about data. By doing a bootcamp you’ll be able to get a data science job quickly, without spending two years in a program like a master’s degree. This might be attractive if you already have a master’s degree in a non-data science field. The skills you typically get are:

  • Machine learning methods — you’ll cover machine learning algorithms like random forests and support vector machines, as well as how to use them by splitting data into training and testing groups or using cross validation. You may learn algorithms for more specific cases like natural language processing or search engines. If none of those words made sense to you, that means you could be a good fit for a bootcamp!
  • Introductory programming in R or Python — you’ll learn the basics around how data is stored in data frames, and how to manipulate it by summarizing, filtering, and plotting data. You’ll learn how to do the statistical and machine learning methods within the chosen program. Although you may learn R or Python, you probably won’t learn both and you may have to learn the other after you finish the bootcamp if you need it for your first job.
  • Real-world use cases — You’ll not only learn the algorithms but also where people use them. Cases like using a logistic regression to predict when a customer will stop subscribing to a product, or how to use a clustering algorithm to segment customers for a marketing campaign. This knowledge is extremely useful for getting a job, and questions regarding use cases often show up in interviews.

Projects

Bootcamps have a highly project-based curriculum. Instead of listening to lectures for eight hours a day, most of your time is spent working on projects which best help you understand data science and gets you started with your own data science portfolio. This is a huge advantage over academia because your skills will be aligned with what you need to succeed in industry, because this is often similar to project-based work.

A Network

Lots of people go on from bootcamps to successful careers at places like Google and Facebook. The bootcamps keep alumni networks which you can use to get your foot into the door at those companies. The bootcamp may bring in data science speakers to talk to you during the program. People from industry view your final presentations. These people can also serve as connections to help you get a job at one of their companies. Having points of entry to companies with data science positions can make all the difference when it comes to finding jobs, and this perk of bootcamps must be stressed.

Cost

One significant downside to the bootcamp compared to self-teaching is the cost: the tuition is generally $15,000–$20,000. Although you may be able to get scholarships to cover part of the tuition, you also have to consider the opportunity cost of not being able to work full-time (and likely even part-time) during the program. Moreover, you’ll likely be on the job market for several months after your bootcamp. You won’t be able to apply during the bootcamp because you’ll be too busy and won’t have learned the skills yet, and even a successful data science job application process can take multiple months from application to starting date. That can end up being an aggregate of 6–9 months of unemployment, in addition to the cost of the program. If you’re able to teach yourself data science in your free time, or learn on the job, then you can keep working and not pay tuition, saving tens of thousands of dollars.

Choosing a program

Depending on where you live, there are likely only a few options for bootcamps. If you want to do an in-person bootcamp, even if you live in a large city there are probably only a handful of programs. If you don’t live in a large city and want to do a bootcamp, you may have to temporarily move to one. That can add to the cost of the program and make it more of an upheaval. Alternatively, there are online bootcamps for data science. Be careful, though: like with graduate programs, one of the benefits of in-person boot camps is that you’ll have people around you to motivate you and keep you focused. If you do an online course you lose that benefit, which can make an online bootcamp a $20,000 version of the same courses you could get through free or cheap massive open online courses.

Data science bootcamp summary

Bootcamps can be great programs for people wanting to switch careers and already have some of the basics of data science. They can also be useful for people leaving school who want a few data science projects in their portfolio when on the job market. These aren’t designed to take you from “0 to 60” though; most of them have competitive admissions and you need to have a background in the fundamentals of programming and statistics to get in and then get the most out of it.

What next?

Stay tuned for part 2, in which we will discuss another important step in the quest to find a good data science job: building a portfolio.

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