Big Data is a big growth area for tech pros. But how should people interested in the field build up their skills to become competitive in these hot new roles?

Building Your Foundation Skills

The first place to start, if you’re brand new to Big Data, is to learn as much as possible about the concepts underlying it, which will make the learning of actual tools and techniques that much easier. For example, that means tackling the “engineer” part of Big Data engineer: the math, the computer science, and the programming languages. Nailing the basics is going to help you take on the hard stuff required for the “Big Data” part of any job. To find Big Data jobs, click here. But where should you start? If you study the job descriptions for Big Data roles, you will find some common requirements for most positions:

• Statistics and math
• Basic computer science (especially algorithms and data structures)
• At least one object-oriented programming language (like Java, Python or C++)
• Knowledge of some of the tools and technologies for parallel programming and Big Data processing (Hadoop, MapReduce, Apache Spark, Zookeeper, etc.)

Once you have the fundamentals down, the next step is diving into actual Big Data technologies. Because there are so many, it can be overwhelming to decide where to start; you can make an informed choice by studying the companies for which you most want to work. What tools do they use? What kind of experience do they expect? You might as well become proficient in the technologies used most at the places you want to work.

How to Become Proficient Enough to Get Hired

If you’re not yet experienced with the tools used by your dream company, it’s a good idea to put them on your “must learn” list. Now take a look at your skill set:

• Do you have a solid understanding of math and programming?
• What do you need to learn to bridge the gap for the jobs you want?

When you look at job descriptions for Big Data roles, does anything in your current skill set overlap with companies’ needs? Are there skills or experiences you don’t yet have, but which seem to appear in a lot of job descriptions? If so, you likely need to acquire those skills before you become a viable candidate. Depending on your goals and the kinds of roles you want, there are a lot of options for building your skills. The best type of skill-building, though, is hands-on project work, where you actually do the sorts of things that would be required of you on the job. Luckily, you don’t have to have a Big Data job in order to get real work experience in the field. Here are some examples of ways to get experience:

Extra Projects in Your Current Role

If you are rocking your current assignments, you may want to start looking around for extra work that will stretch you in all the right areas. Can you access a data set and do a little analysis for product or customer insights? Try to find ways to integrate on-the-job Big Data work into your current role; in addition to helping you build your skills, it will help you add value to your current employer, too. 

Contributing to Open-Source Projects

There are a ton of open-source data tools and technologies. Helping fix bugs or add new features is a great way to give back and improve these technologies, while really learning how they work. Plus, it looks really impressive to have open-source contributions to a library or tool used by the company hiring the role.

Side Projects

There are all kinds of free data sets out there on the Internet; why not try building your own little project? Or maybe you know a startup or entrepreneur who could use a little help. Offering free or inexpensive work in exchange for helping you learn is a pretty good trade for both parties. Of course, be careful about your commitments—you don’t want your spare-time side-learning project to be on someone else’s critical path.

Puzzles and Toy Programs

If you don’t have a lot of time, or you don’t have the bandwidth to set up a full project or feature, try your hand solving some puzzles or completing simple tutorials. The Kaggle competitions are a great collection of little problems; they are sorted by difficulty, too, so you can practice and make progress at your own pace. If you aren’t ready to try things on your own yet, perhaps a more formal training program could prove helpful. There are all kinds of tutorials, trainings, and courses (both free and paid) available. For example:

• Coursera: This site has all kinds of classes on programming, computer science, data science, Big Data, and machine learning. Most of them are free and taught by university instructors. It’s a great resource and a good place to start if you need to improve your foundational skills.
• Big Data University: This site’s whole focus is Big Data, and most of their courses are free. There’s great introductory material, and most of the courses only take a few hours, so you can build your skills without a big time commitment.
• Udemy: This site has a lot of paid classes, and the quality is hit-or-miss (it really depends on the instructor). That being said, there is a big selection, and you can start right away.
• Specialized training: For every Big Data technology, there is a company that offers instruction for it. AWS offers training on using their services for Big Data; Cloudera has instructor-led courses on Hadoop. Options abound, depending on what technologies you want to learn.

Big Data is a big opportunity for people looking to make their careers on the forefront of technology. Even if you lack the skills to get a data-related job today, there are plenty of ways to get up to speed and find a role fast.

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