Big Data Career Paths - dummies

By Jason Williamson

The types of roles and career paths in big data are many, but they do share some common attributes. And don’t worry: They don’t all require a PhD in math or statistics.

Not everyone is a data scientist

So, what is a data scientist? She is practitioner who helps the company achieve a competitive advantage through the use of the data. When the big data field began to emerge, people quickly jumped at labeling what they thought the corresponding job function would be. The term data scientist was thrown around in IT circles, but people weren’t really sure what that job would look like.

What emerged was the idea that big data can only be done by the most advanced mathematicians, statistical modelers, and specialized programmers. For many people, images of a Wall Street quantitative analyst comes to mind. (A quantitative analyst, or quant, is someone who uses models to determine when to buy and sell specific stocks.)

There continues to be a demand for traditional data scientists, but the field has expanded to include a broad spectrum of functions — in part because the advancement of technology has made using big data systems easier.

Requirements of big data professionals

Big data jobs share some common requirements no matter what career path you choose. If you’re wondering if this career field is for you, take a look at the following list. Many jobs in this space require that people have experience with or interest in the following areas:

  • Marketing and analysis: The process of using analytics to better understand the how’s and why’s of buyers in order to increase sales.

  • Product placement: The process of getting products featured in movies and television to increase awareness and brand recognition.

  • Product management: The process of creating products for commercial use.

  • Relational database management systems (RDMSs): Foundational database skills.

  • Not Only SQL (NoSQL): Methods for accessing data outside of traditional SQL programming.

  • Cloud computing: Leveraging utility computing by renting for computer power and storage, paying only for what you need and scaling on demand.

  • MapReduce: A paradigm for dealing with massive amounts of servers in a Hadoop cluster. Hadoop is a widely used programming model to sift through massive amounts of data using parallel processing.

  • Healthcare informatics: Using data to drive innovations for healthcare.

  • Statistics: Studying a collection or group of data for analysis.

  • Applied math: Practical application of mathematics in the real world.

  • Business intelligence systems: IT systems that allow business users to organize data into information to support business decisions.

  • Data visualization: Software that takes information and presents it in a visual format for interpretation and analysis.

  • Data migration (extract transform and load [ET]): Software tools to move data from one system to another and transform it into a structure that is usable by the target system.

If you’re already knowledgeable in any of these areas or interested in these topics, you can feel confident that you’ll be able to chart a career path in this emerging field.