

Why Should You Do a Data Science Mini Bootcamp?
Career InsightsConferencesFeatured Postdata science bootcampdata science mini bootcampjob trainingmini bootcampposted by ODSC Team August 19, 2019 ODSC Team

Companies are ready and willing to hire data scientists and jobs are going fast. So, job-seekers need to make themselves hire-able and stand out in a crowded field. Bootcamps have opened up and become an alternate method for getting the job-skills you may need, however, they still have some flaws. Luckily, data science mini bootcamps have begun coming into popularity, to get you all the information you need, faster and more efficiently. In this article, we’ll discuss six reasons why you should do a data science mini bootcamp.
[Related Article: 3 Unique Ways to Get a Job in Data Science]
1. Time to commit
While regular bootcamps are shorter than a university course, and more efficient than trying to teach yourself everything, they still last a few months, or a few weeks at the least (the shortest we found was 5 weeks). This means you have to plan your life around the course for months, are more likely to miss some of the classes and information, and have to do all that on top of your work. On the contrary, mini bootcamps are usually only one week. They’re intensive courses, but they reduce the strain on your life overall and you’ll leave with all the same skills (if not more).
2. You’ll have the skills sooner
By making mini bootcamps a week-long, companies have chosen to focus on tangible skills, rather than vague theories that won’t help you. And, you’ll have all those skills faster, meaning you can start applying them and yourself to the industry sooner rather than later. These instructors want you to leave the course able to start building your GitHub profile or applying for jobs.
3. Much cheaper
Mini bootcamps are also much cheaper, compared to full-length ones. This is partly because they’re cheaper for the companies to put on—just like it’s less time to commit for you, it’s less for instructors to commit to. Most mini bootcamps also know you’re giving up time at work to be with them, and want you to get the absolute most out of the experience, without worrying about how much money you’re spending.
Because mini bootcamps are so short, the instructors have to make their lectures as specific as possible. Unlike some classes that are 90% buildup with the main point in the last 10 minutes, mini bootcamps have no time for fluff. You get more information in a shorter time and don’t have to sit through hours of unnecessary talking that’s done for the sake of filling up time. At a mini bootcamp, everyone’s time is more valuable.
5. Faster understanding of what your job would be like
Many people enrolling in data science bootcamps don’t have much experience in the field, or are using the bootcamps to “test out” if they actually like the subject. The problem, again, returns to the fact that most bootcamps are weeks, or even months, long. This means you might realize in the second class that you don’t like the subject, but have paid and signed up for the rest of the course that you’ll have to waste your time to sit through (or you’ll just waste your money). With a mini bootcamp, you haven’t committed for months, and if you realize on the third day that you don’t like it, you’ll only have a few more days to attend, or you won’t be wasting as much money if you stop attending.
6. Tangible outcomes
With a data science mini bootcamp, you’ll finish well-rounded and focused, knowing exactly what you want, or don’t want to do, with actionable skills and contacts to begin implementing right away. If you let it, a mini bootcamp can start an incredible momentum in your life and career, while still giving you direction to take all this momentum.
[Related Article: Learning Data Science: Is a 5-Day Bootcamp Right For You?]
If this sounds like a good way to learn all you need to know about data science, attend the ODSC East 2020 mini bootcamp this April 13-17 in Boston! Get a week of focused learning from experts in data science in a hands-on setting.