Three skills every student seeking a career in analytics should develop

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skills_every_student_seeking_a_career_in_analytics_should_developThe job market for individuals with analytical skills is hot, and it’s only getting hotter. A recent study by the McKinsey Global Institute puts the situation in perspective, citing a shortfall of nearly 200,000 professionals with strong analytical skills by the year 2018. Businesses are looking to colleges and universities to help fill that gap, asking them to provide their students with the analytical skills they need to fill many of these currently vacant analytical roles.

How students can prepare for a career in analytics, and the role universities play in preparing them, is the focus of Dursun Delen’s recent article, Mandate for STEM Educators, found in the August issue of INFORMS magazine. Given the shortage, Delen, a professor of business analytics within the Department of Management Science and Information Systems at the Spears School of Business at Oklahoma State University, says educators haven’t had a “mandate this clear since the space race of the 1960s.”

And, the demand for analytics professionals doesn’t show any signs of abating any time soon. That’s great news for students educated in analytics. With so many businesses around the world looking to hire in support of their big data initiatives, these students are stepping squarely into a seller’s job market – a rare occurrence for many new graduates.

But that only works if the student is well prepared. In his article, Delen outlines three areas that will provide a firm foundation from which a student can build his analytical career.

First, is descriptive analytics, a simple concept but critical in solving big data problems. Descriptive analytics uses a sample of a given population or data set to report on and draw conclusions without having to use all data points. With the amount of data collected in today’s digital world, students with strong skills in descriptive statistics – think mean, median, standard deviation and a number of other reporting skills – is non-negotiable.

Delen cites predictive analytics as the second area critical to any analytical professional’s knowledge base. Of course, predictive analytics builds on descriptive analytics by using advanced statistical techniques, like data or text mining, to go beyond the snapshot descriptive analytics provides. According to Delen, predictive analytics builds a model to forecast future trends, understand customers, improve business performance, drive strategic decision-making and predict behavior.

Delen says the third layer in the “analytics hierarchy” is prescriptive analytics. Prescriptive analytics goes beyond even predictive analytics by identifying the optimal decision from the universe of options. Delen says techniques in prescriptive analytics include optimization and simulation, methods that can have the greatest impact for companies using advanced analytics.

To help build a student’s expertise in analytics, a number of universities have created advanced degree or certificate programs in analytics. In addition to course work, many of these schools partner with area businesses to provide practical experience solving real-world problems.

SAS plays a critical role in many of these programs by partnering with universities to offer master of analytics, applied statistics, data analytics and a number of other degrees that use SAS software as the analytical tool of choice. Other schools offer joint certificate programs, marrying course content with SAS knowledge. (If you’re a student looking for such a program, here’s a list of the master programs with a SAS focus, as well as joint SAS/university certificate programs.)

Delen says cloud-based technology makes it even easier for companies like SAS to get software in the hands of students as well. He cites the Teradata University Network (TUN) as a great example of how software providers can provide the tools and resources professors need to ensure that their students have access to various technologies. Experience with analytical software and advanced technologies will serve students well as they launch their professional careers.

Delen talks about the importance of hands-on experience in the education of students, which is one reason why SAS, along with the INFORMS Analytics Section, sponsors the Student Analytical Scholar Competition. The purpose of the competition is to practice the process of structuring and presenting a compelling proposal for analytical work in the prescriptive analytics realm. Students read a case study that is based on a real-life project involving optimization and/or simulation and must craft a document known as a “Statement of Work” (SOW). Such documents are usually created early in a project, after some exploratory work, but may or may not fully define the problem. The challenge requires students to combine the “hard” STEM skills they’ve learned along with “soft” skills like business problem framing, communication, and presentation, just as they would have to do if they were competing to win business.

If you’re a student interested in pursuing a career in analytics, I encourage you to read Delen’s full article on the INFORMS website and learn more about the work SAS is doing in support of teaching, learning and research in education.

Editor’s note: SAS is participating in the 2016 INFORMS conference, November 13-16 in Nashville. If you’re attending, here is a list of SAS-related presentations at the conference, including one by Delen. More info.

  • Identifying Shifting Production Bottlenecks Using Clearing Functions
    Baris Kacar, SAS; Lars Moench (University of Hagen, Hagen, Germany); Reha Uzsoy (North Carolina State University)
  • The Use of Simulation for Evaluating Forecast Models
    Sanjeewa Naranpanawe, SAS
  • Unlocking Your 80%: Unearthing New Insights with Text Analytics
    Christina Engelhardt, SAS
  • Panel Session: IoT-enabled Data Analytics: Opportunities, Challenges and Applications
  • FDD Data Flows Staging et al.
    Leo Lopes, SAS
  • Estimating Clearing Functions for Production Resources Using Simulation Optimization
    Reha Uzsoy (North Carolina State University); Baris Kacar, SAS
  • Analysis of a Presidential Debate Using SAS Text Analytics
    André de Waal, SAS
  • Strategies for Maintaining Sparse Dual Solutions in Large-scale Nonlinear SVM
    Joshua Griffin, SAS; Alireza Yektamaram, SAS
  • A Hessian Free Method with Warm-starts for Deep Learning Problems
    Wenwen Zhou, SAS; Joshua Griffin, SAS
  • An Accelerated Power Method for the Best Rank-1 Approximation to a Matrix
    Jun Liu, SAS; Ruiwen Zhang, SAS; Yan Xu, SAS
  • Local Search Optimization for Hyper-parameter Tuning
    Yan Xu, SAS
  • Building and Solving Optimization Models with SAS
    Ed Hughes, SAS; Rob Pratt, SAS
  • Data Discovery and Analysis with JMP 13 Pro
    Mia Stephens, SAS
  • The SAS MILP Solver: Current Status and Future Developments
    Philipp Christophel, SAS
  • Single-resource Capacity Control in the Presence of Cancellations, No-shows and Overbooking
    Jason Chen, SAS
  • Pricing and Revenue Management of Function Space in Hotels
    Altan Gulcu, SAS; Xiaodong Yao, SAS
  • Visual Statistics—SAS
    Dursun Delen (Oklahoma State University)
  • P-center and P-dispersion Problems: A Bi-criteria Analysis
    Golbarg Tutunchi, SAS; Yahya Fathi (North Carolina State University)
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About Author

Larry LaRusso

Principal Communications Specialist at SAS

Larry LaRusso is the editor of the SAS Tech Report and SAS Learning Report newsletters and the SAS Users, Learning Post and Analytics U blogs. He has worked at SAS since 2000 in marketing, communications, customer experience and management roles for both the Education Division and External Communications. You can follow him on Twitter @lalaru102.

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