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What IT Certifications Are Useful When Becoming a Data Scientist

    

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Editor's Note: This post was originally published August 2017 and has recently been updated and revised for accuracy and comprehensiveness.

In the last 20 years, IT has been reshaped by numerous trends related to the improvement of the internet. Cloud computing, smartphones and the Internet of Things have all been enabled by ubiquitous IP network connectivity. Just last year, Vox reported the internet speed in the United States rose by nearly 40 percent. The report says download speeds jumped 35.8 percent and upload speeds improved by 22 percent. 

Such rapidly increasing speeds have fueled the popularity of everything from on-demand music streaming to vast cloud-managed databases. The latter systems are now essential components of the field known as data science (alternatively "big data" or "data analytics"), which only emerged in the late 1990s.

Data science: A new frontier in internet-centric innovation

What is data science? We can loosely define it as a cross between mathematics (especially statistics, but also algebra and calculus to lesser degrees) and computer programming. A practitioner, known as a data scientist, will typically manipulate vast data-sets using a combination of statistical techniques and technical platforms, which might run the gamut from basic spreadsheets to cutting-edge cloud frameworks. The ultimate goal is to draw actionable insights from all the information at your fingertips.

If you want to become a data scientist, what should you do? It is undoubtedly one of the most alluring career paths at the moment for anyone interested in IT. Forbes has highlighted a 30 percent increase in demand for data scientists in the last year and a 348 percent jump since 2013. At the same time, the U.S. Bureau of Labor Statistics projected 19 percent growth (much faster than average) in employment for computer information research scientists between 2016 and 2026.

Any prospective data scientist will need to be fluent in several specific applications and frameworks, many of which you are likely to encounter during the course of obtaining common IT certifications from vendors such as Microsoft and trade associations like CompTIA. Let's take a look at some of the key tools to know in this field.

Microsoft Excel

It may seem odd that a program as old as Excel (it first launched more than 30 years ago) is at the forefront of data science operations. However, according to Excel with Business, Microsoft Excel has an estimated 750 million users worldwide. That makes it a valuable tool in businesses across industries.

Excel fits the classic mold of "easy to learn, hard to master." Its ubiquity in enterprise software (as part of Office 365, the business world's favorite cloud computing platform) makes it an obvious point of entry for data manipulation tasks, as does its ease of use for non-programmers. Advanced formulas and chart creation features further cement its utility within the sphere of data science. An Excel certification can help you learn about pivot table, Excel programming and other functions that unlock the spreadsheet application's power.

Infrastructure-as-a-Service platforms (Azure and AWS)

IaaS is integral to data science because it provides IT resources on a vast scale and on demand. The virtually limitless computing power, storage capacity and networking capabilities of an IaaS cloud greatly simplify the process of extracting insights. Unsurprisingly, the O'Reilly survey takers pointed to specific IaaS components such as Amazon Redshift as key tools in their work.

Although there are many nominal players in the IaaS market, a few vendors account for the vast majority of all IaaS spending: Amazon Web Services leads the pack, while Microsoft Azure has long been the runner-up and well ahead of would-be competitors such as Google and IBM. The specific features of each platform differ, but the fundamental technical skills needed for managing them are related. Accordingly, an AWS expert might pursue specifically designed coursework in Azure to acquire the additional expertise for working with his or her company's cloud implementation.

Linux and Windows

The Linux kernel is at the heart of numerous open source operating systems and tools, due to its easy availability, robust design and high flexibility. It's also growing rapidly in popularity. In fact, Forbes says Zorin OS, a desktop Linux distribution, has been downloaded more than 17 million times.

"Linux has a large ecosystem of compatible tools."

Linux has a large ecosystem of compatible tools, and it is commonly used in data science alongside Apache Spark and Hadoop as well as Python, all of which are open source. In contrast, Windows has its own different constellation of supporting big data apps and frameworks, which usually includes the aforementioned Excel in addition to Microsoft SQL Server and Windows Server.

Which OS a company uses will depend heavily on its size, budget and key applications. Many organizations choose to use both Linux and Windows to varying degrees, due to the unique benefits of each ecosystem. Certifications such as CompTIA Linux+, which helps with the installation and maintenance of Linux-based systems, and Office 365 can help you stay ahead of the curve for both OSes.

Get on the track to a data science career by earning IT certifications

There is plenty to learn in becoming an expert data scientist, given the need for skills that span technical programming, project management and mathematical analysis. Obtaining IT certifications from a trusted institution such as New Horizons Career Development Solutions is a practical step on the road to data science success.

First, look for a location near you to plan your visit. Then, be sure to take a glance at our full course listings and our supporting resources like our webinars page, where you can learn more about how to look for IT careers that interest you. Data science is positioned to be one of the most lucrative career tracks for the rest of the decade, so it will literally pay to get the training you need to stand out from the pack of job applicants.

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