The popularity of data in business is rising, and with it, the abundance of data-related jobs.
In any given day you might chat to the water cooler with a Data Scientist, share the lunch table with a BI Specialist or dance with a Machine Learning Specialist at the office party.
With so many datas, scientists and engineers floating around it can be tough to know where one job ends and another begins. Much less, who do you go to with your analytics questions?
This guide will outline each of the most popular data jobs, their roles, responsibilities, skill sets, pay and why each one is important to your business.
The job title you probably most closely associate with data is the Data Scientist. It’s a relatively new title (famously coined by Jeff Hammerbacher and DJ Patil in 2008), but it is quickly becoming one of the most popular. It has even been called the Sexiest Job of the 21st Century!
Despite its fame, the actual role of the Data Scientist is one of the most debated – probably because the role varies considerably from company to company.
In all data related jobs there’s a certain amount of skills overlap. The best way to differentiate them is to think of their skills like a T. They’re a generalist in a variety of different areas, but have deep domain experience in one particular area. For a Data Scientist, that deep experience is probably in Statistics and Machine Learning.
Statistical and machine learning knowledge is the domain expertise required to acquire data from different sources, create a model, optimize its accuracy, validate its purpose and confirm its significance. At minimum, Data Scientists need to know how to take some data, munge it, clean it, filter it, mine it, visualize it and then validate it.
In addition to all that statistical modeling, Data Scientists also need to know how to explain their findings to business decision makers, understand the business and product model, be good at problem-solving and know some basic engineering. The most popular Data Scientist languages are R and Python, but they may also know Scala, Java or Closure.
All that boils down to this:
“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” – Anjul Bhambhi, VP of Big Data products at IBM
So, to become a data scientist you need a solid foundation in computer science, modeling, statistics, analytics, and math.
Their role varies from sector to sector, but in general, they sift through all the incoming data streams (both internal and external) with the goal of discovering new insights and solving business problems. Then they communicate their findings and recommendations to the organization’s leadership.
The technical nature of the job (and the shortage of good candidates), means that Data Scientists earn good money. According to Glassdoor, Data Science is currently the 15th highest paid job in America, averaging $91,000/year nationally and $110,000/year in Silicon Valley.
Like their fellow Data Scientist, Data Analysts perform a variety of tasks around collecting, organizing, and interpreting statistical information. Their mainly responsible for using data to identify efficiencies, problem areas, and possible improvements.
Think of it as data science light. While they may not have the mathematical chops to invent new algorithms, they have a strong understanding of how to use existing tools to solve problems. They need to have a baseline understanding of five core competencies: programming, statistics, machine learning, data munging, and data visualization.
These are often the guys making charts and reports for management as well as the ones conducting primary research (like surveys). This part of their job means communication skills are essential. They need to take complex ideas and present them in a way that non-technical people can understand.
The line between Business Analysts and Data Analysts has become so blurred that they’re essentially the same thing. Both use their reports and analyses to help management make decisions and set goals.
While they possess some technical skills, your traditional Data Analyst, is far less technical than the average Data Scientist. Instead of R and Python, they deal in Microsoft Excel, Microsoft Access, SharePoint, and SQL databases.
With the simpler skillset, comes a lower pay scale. The average Data Analyst earns around $54,000/year. Data analysts come from a diverse set of backgrounds that can include anything from technology, information management, relational database design and development, business intelligence, data mining or statistics.
On the other side of the technical spectrum from the Data Analyst, you’ll find the Data Engineer.
Typically software engineers by trade, Data Engineers are the designers, builders, and managers of the data infrastructure. They are responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place. They also make sure those systems are performing smoothly.
The core job of the Data Engineer is to make sure data is flowing smoothly from source to destination so it can be processed and analyzed. To do that they need to know complex Hadoop-based technologies (MapReduce, Hive, Pig), SQL technologies (PostgreSQL and MySQL), NoSQL technologies (Cassandra and MongoDB) and data warehousing solutions. In addition, they should also be familiar with a variety of coding languages such as Python, C/C++, Java, Perl, R and more.
Data Engineers may work largely behind the scenes, but they are an essential part of the data ecosystem in your business. As such, they get paid quite well – an average of $91,000/year.
The Bottom Line
Collecting, storing, analyzing and presenting data takes a team of people. No one data job is any more important than any other. Each role has a unique and important part to play in making sure management has all the information they need to make decisions.