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It's Not Too Late to be a Big Data Scientist When You Grow Up
The average data scientists commands considerably more than $100,000 per year. At top employers like Twitter and Facebook, that figure is well over $135,000 per year. Since there are so few of these elusive creatures, they can command high salaries and be incredibly selective in who they work for. That means that most businesses are left without. So, if you can put together the skill set necessary to become a data scientist, you too can make great money and work for the world’s top companies—or, you can simply stay and power your organization to new heights. Here are the skills it takes to become a data scientist.
Big data, of course, has to live somewhere. In many organizations, that means a relational database like SQL. Other organizations that have a need for storing lots of unstructured data might opt for another type of database, like NoSQL. While these skills are different, you’ll need to become familiar with databases to become a data scientist.
Understand Mathematics & Statistics
Data analytics is heavy on math. The mathematics most useful in data science include linear algebra, calculus, statistical probability, hypothesis testing, and summary statistics.
Know Your Way Around Big Data Tools
Big data tools like Hadoop, MapReduce, Hive, and Pig are essential for a data scientist to master. These tools change somewhat over time, for instance MapReduce is slowly giving way to Spark (and perhaps YARN). Some of these come with considerable learning curves, though these platforms are maturing quickly and becoming somewhat easier to master and use. So, if you can get a handle on these now, the worst of the learning involving big data tools, per se is likely behind you.
Be Somewhat Proficient at Programming
While most organizations that are leveraging big data usually have IT departments staffed with experienced programmers who can help, it’s very helpful to have at least some knowledge of the programming languages and skills used in big data. For example, statistical programming in R and Python is incredibly helpful when your job is analyzing big data.
Get Educated on Machine Learning Tools & Technologies
Though not all organizations that are using big data are doing so for the purposes of machine learning and the potential for artificial intelligence, it is a good idea to be versed in these areas if you’re going into the field of data analytics. For starters, you will be able to advise your organization when ML makes sense for them (as well as when it doesn’t). Secondly, a large number of employers are specifically looking for data scientists to assist with ML efforts, and you’ll be limiting your potential employers without this skill.
Additionally, data scientists must be able to learn about and work within the industry where they go to work. A data scientist without an understanding of the business’ goals and needs for big data isn’t much good to their employer. There are programs available to teach data science in just a few months for those who learn quickly and have strong talents in the aforementioned skills.
Most organizations today turn to the cloud for their big data storage and processing needs, since the hardware and equipment to do so in house is so expensive and difficult to maintain. Learn more about Bigstep and how they can help with big data storage and processing with their Full Metal Cloud.