10 Paths To Learning The Best Data Tools For Your Career

Learning new data tools could increase your worth and salary. But the best choice depends on which tools you already know. Here's a look at which ones to add to your tool kit next.
Start With Excel
Start With SQL
Start With R
Start With Python
Start With MySQL
Start With Microsoft SQL Server
Start With Tableau
Start With Java
Start With PostgreSQL
Start With Visual Basic/VBA

Data pros are among the rock stars of IT organizations today. These professionals perform a variety of tasks, from analytics, to engineering, to coding, and are frequently called upon to contribute to projects that are strategic to the enterprise overall.

In a digital age where data volumes continue to grow and gaining insights from all that data has become essential to success, data pros are at the center of it all.

Their salaries recognize those contributions. Executive recruitment firm Burtch Works' annual survey of data scientist salaries shows they range from $97,000 for less experienced workers, to $152,000 for those more experienced, and $240,000 for management-level data scientists.

Glassdoor says the average data scientist salary in the US is $113,436 as of Nov. 1, 2016. A data engineer's average salary in the US is $95,526, according to Glassdoor.

Knowing the salary range can help you negotiate your rate or decide to pursue a new career. But once you are in that job, is there anything you can do to boost your salary even higher? O'Reilly's 2016 Data Science Salary Survey provides a more nuanced set of insights about what tools data pros should learn in order to increase their salaries.

Which Tools to Add to Your Stack

However, the O'Reilly report does not break down into a simplistic view what individual tools and skills earn more for data pros. Instead, it provides a model of the data to provide a more personalized answer to the question: "Of all the tools you could learn next, which one would provide the biggest salary boost based on the tool you currently use?" Because, if you've only used Excel up until now, learning Apache Spark or Apache Kafka is not what you need for your current work. 

"The real question is whether a tool is useful for getting done what you need to get done," the report says. "If you've never had to analyze more data than can fit into memory on your local machine, you might not get any benefit -- much less a salary boost -- by using a tool that leverages distributed systems, for example."

So the O'Reilly survey offers something better than a list of tools. It offers a snapshot of the best tool for you to learn next, based on the tool you currently use. Even though those who know Apache Spark may earn more, if you only know Excel, learning Spark next won't add to your salary. The best next tool for you to learn is SQL.

[Considering Hadoop? Read Hadoop Pros and Cons for Enterprise Users.]

"In the following sequences of tools, the next tool on the sequence was frequently used by respondents who used all the earlier tools, and these sequences had the best salary differentials at each step," the O'Reilly report said.

In other words, if you are looking for the best tool to learn next to get the biggest salary boost for yourself, these slides will provide the answer. If you know the first tool on the list in the sequence, you should learn the second tool in that sequence, and then the third, and so on. Here are the lists.

Next slide