Big data is becoming a big deal. Thanks to the data explosion, there’s a huge (and ever-growing) demand for data scientists, data engineers, statisticians, and data analysts. By 2018, there will be 4 to 5 million data analysis jobs in the country—and based on the average salary, career opportunities, and positions, Glassdoor ranked “data scientists” as one of the best job in America.

5 Steps to Land a Job in Data Science

1. Know the Right Stuff

Since data science is a fairly new field, the requirements are still in flux. However, a couple trends stand out: a statistics and math background, a knowledge of basic computer science (including data structures and algorithms), competency in one or more object-oriented languages (like C++, Java, or Python), and experience with Big Data technologies like Hadoop, Mahout, Pig, Hive, and so forth.

If you’re feeling a bit overwhelmed by all the different programming languages and software packages and databases, considering using the R-Hadoop technology stack. Both are free, comprehensive, and popular—plus, they’re both fairly easy to learn. (http://will-stanton.com/becoming-an-effective-data-hacker/)

2. Get Certified

While you’re learning R, you might as well get certified. More than two million data scientists rely on this language, making it the most widely used statistical language in the world. So, if you want to prove your mastery of R, you can get your Revolution R Enterprise Professional Certification from Revolution Analytics.

Python is also becoming a popular choice for data scientists. The O’Reilly School of Technology offers an online Python certificate program, as does the University of Washington.

3. Take a Course

Maybe you’re looking for a more generalized program. Coursera has a Data Science sequence: after you finish the nine courses and complete a capstone project, you get a specialization certificate. Harvard Extension’s four-course Data Science program also comes with a certification upon successful completion.

4. Understand Data Munging, Visualization, and Reporting

Data scientists spend a lot of time cleaning up data sets. This is also called data munging, mining, or wrangling, and Stanford has some great classes you can use to solidify your understanding of it: Stats 202, cs246, and cs246h. Furthermore, you should understand data visualization, which is exactly what it sounds like: making visual representations of data.

Finally, practice data reporting. In any data analysis, you’ll need to create a summary of your analysis and results.

5. Take On a Project

Projects are one of the best ways to show employers you’d be a great fit for a data science job. And hopefully you’re passionate about data, meaning this will be fun for you.

For example, one job candidate who was a player on World of Tanks made a utility to scrap data from the game’s server. He analyzed that data to see how he should change his strategy, the kinds of players he needed on his team, and so on.

Data scientist Will Stanton has more ideas, like “find a dataset of historical results from the World Series, create a couple of visualizations using ggplot2, and create a simple web-app in Shiny to display the visualizations, or “build a classification model to identify survivors of the Titanic using the Kaggle Titanic dataset and R’s caret package.”

 Syed Ahmed is the co-founder and Chief Technology Office at TARA. He graduated in 2014 from Queensland University of Technology with two master degrees and founded TARA Inc. in 2016. Syed specializes in creating applications related to machine learning and data science, as well as process development and business process engineering.