Loading...
You are here:  Home  >  Data Education  >  BI / Data Science News, Articles, & Education  >  BI / Data Science Articles  >  Current Article

Use a Data Science Bootcamp to Advance Your Big Data Career

By   /  April 18, 2017  /  No Comments

Data Science BootcampA sense of frustration has developed among some companies wanting to develop a Big Data Strategy using Data Science and aligned technologies. They can’t find people capable of doing the work. Experts have been warning of a Data Scientist shortage for at least six years, but it’s a strange sounding, “new” career field that many people have never heard of. As a consequence, some employers are focusing on skills and experience, rather than degrees and social connections. They want people who can get the job done. One possible route to gaining important knowledge of the many practices and techniques required is to attend a Data Science Bootcamp.

Data Science Bootcamps are often total immersion training programs, designed for students coming from a variety of technical backgrounds, but they can also consist of part-time online classes. These intense courses have been used in training Data Scientists and Data Engineers since around 2012. Many professionals from different fields are now learning Big Data skills, and getting secure jobs, by taking bootcamp classes.

Bootcamps are abbreviated, intense training programs designed for individuals with some data background. They are intended to fill in knowledge gaps. In the past, instructors simply wanted to get their students trained in the critical Data Science technologies and skills. The more the student knew, the easier the instructor’s task. Data Scientists must have a variety of tools and tricks in processing Big Data for organizations, and these Data Science Bootcamps focused on the details.

Today’s bootcamps typically offer their students a good balance of practice and theory. Very few programs rely exclusively on the classroom experience, though it is still used quite a bit. The concepts supporting the technologies are usually explained by an instructor during lectures, with books or printed materials providing a foundation for the student. This information is then applied to simulations and situations in labs, giving students valuable, hands-on experience. Historically, Data Science Bootcamps have targeted people who were mid-level Data Analysts or Software Engineers.

Times, however, are changing. At present, there are bootcamps available for all skill levels. Microsoft’s DS3 summer program trains college students “with no more than the normal computer background” to become Data Scientists. (There are still bootcamps for the PhDs. The Insight Health Data Science: Fellows Program is for M.D or PhD graduates transitioning to Big Data jobs at healthcare organizations).

Getting in to a Data Science Bootcamp

The education requirements for bootcamps vary widely. There are bootcamps offering beginner’s courses online, which have no background requirements, and there are advanced bootcamps requiring a provable background in certain programming languages or an advanced degree. Applicants for Metis’ program must pass a six-part rubric and a virtual interview. Candidates applying for the Zipfian/ Galvanize program must complete an assignment on their own time and pass two interviews. Data Science Europe accepts only about ten applicants per cohort, but also provides a 100% guarantee on job placement.

A large number of Data Science bootcamps are located in high tech areas like New York City and Silicon Valley/ San Francisco, and large European cities, such as Berlin, Dublin, Turin, and London. Data Science Nigeria is hosting a bootcamp in Lagos. There are also bootcamps available in smaller American cities, though these cities typically support a university or college. Interested individuals should do their own research, as new opportunities are constantly presenting themselves.

Selecting the Right Data Science Bootcamp

 While all bootcamps market themselves as unique, many three-month programs are quite similar. Metis, NYC Data Science Academy, and others, provide a three-month program with these common core goals:

  • Experienced Data Scientists teach and act as mentors
  • Work on real-world projects (and show them off on your resume/curriculum vitae)
  • Events, such as guest lectures, field trips, seminars, and networking events
  • An emphasis on career preparation, including hiring fairs and job interviews
  • The fundamental principles of Data Science (Hadoop, Data Visualization, Machine Learning, etc.)

On the other hand, some bootcamps do follow a unique model. The University of Michigan hosts a free “introductory” summer bootcamp which lasts for six weeks, and includes lectures on such topics as Data Structures & Data Visualization and Probability & Statistical Inference, and research projects on concepts like Genomics and Data Mining & Machine Learning. Applicants must be undergraduates from an accredited school, and have some knowledge of computing/programming.

Online vs Physical Attendance

Bootcamps requiring physical attendance have several advantages. They offer team-based projects, face-to-face mentoring relationships, and a three-month time-span to get to know Data Science professionals. Vis-à-vis relationships “can” provide some excellent networking opportunities. If part of the goal is networking, classes requiring a physical attendance may be the best path.

Physical attendance bootcamps also come with some problems. They consume quite a bit of time and require individuals stay focused on a challenging curriculum, for a minimum of sixty hours each week. This is a concentrated effort, and not having the time available is almost a guarantee for failure. If you’re employed, and your employer doesn’t support your efforts to upgrade yourself, consider using your vacation time and sick days. Some Data Science bootcamps require travel and lodging, and may be held in an exotic location, or even on a cruise ship. These are potentially distracting, and may be a conflict of interests, considering the intensity of most bootcamps.

Online bootcamps offer a great deal of flexibility. Most online bootcamps offer adjustable scheduling and flexible payment plans. They can be taken on a part-time basis, allowing students to study and learn at their own speed, while making monthly payments. Online bootcamps, designed to mimic the physical attendance classroom experience, are also available, though they tend to be more expensive and often require an upfront payment.

Chip Paucek, the CEO of 2U, a consulting firm providing designs for online programs, describes online face-to-face time, stating, “This is kind of a must-have. You have a level of intellectual intimacy in a live class that you don’t have any other way.”

Gary Rosche, a student enrolled in a University of North Carolina/2U class, agreed, saying,

“Face-to-face communication really does enhance the whole experience, and provides a better learning experience. You develop a real personal relationship with the faculty and other students.”

Others may not have the same need for face-to-face communications with a professor, instead preferring more flexibility in their scheduling. There are bootcamps that offer the freedom of “learning at your own pace,” and choosing “a daily schedule that works,” which may be based on biorhythms, a fluctuating work schedule, or both. One example of this kind of bootcamp is the K2 Data Science Bootcamp. This kind bootcamp is especially advantageous for career changers, who work full-time and want to improve their lives with a career shift. Many online bootcamps come with job finding services and a money back guarantee, should graduates not find a job, immediately.

Gaining Hands-On Experience

Anna Smith, a Data Scientist at Bitly, said,

“It’s hard to find people with all the right skills, where they can go into an environment, drown themselves in data, and come up with actionable things. Sometimes, you have to look in odd places. There is no Data Science discipline that churns them out.”

Regardless of the type of Data Science Bootcamp being chosen, find out what kind of projects they work on, and the amount of real-time experience they offer in working on projects. This is extremely important. The person with the most “experience” will get the job. This experience may be familiarity with a specific program (for example, Apache Spark), or familiarity with a broad spectrum of programs. Either way, it is imperative you gain “documented” hands-on experience, even if it is only a project for your best friend’s business, and you are doing the project for free.

Words of Warning

Do not incorporate a philosophy of minimalistic education. This is a career field requiring individuals to constantly update their information and develop an understanding of current and new technologies. As the technologies evolve and change, so must the Data Scientist.

When starting a Data Science Bootcamp, the founders need no screening, nor do they need to register with a government or academic agency. A single individual can attract customers, simply by making promises and having an employee capable of teaching code. A small reality-based office might be useful if the teaching is done in person, but for online schools, no office is needed. Video calling tools and a Slack group are all that is required.

Student testimonials can easily be faked, along with salaries and job placement rates. Don’t get scammed. Surf around, looking at a variety of bootcamps, read multiple reviews, call the office, and make comparisons. Become an informed consumer, and attend the program that is a best fit for your needs.

 

Photo Credit: Istimages/Shutterstock.com

About the author

Keith is a freelance researcher and writer.He has traveled extensively and is a military veteran. His background is physics, and business with an emphasis on Data Science. He gave up his car, preferring to bicycle and use public transport. Keith enjoys yoga, mini adventures, spirituality, and chocolate ice cream.

You might also like...

ASG Technologies: Bridging Enterprise Data Intelligence from the Mainframe to Big Data

Read More →