A cornerstone of any university analytics program is the ability for students to work with real world data. These opportunities abound at Central Michigan University’s Institute for Health and Business Insight (IHBI), located within The Herbert H. & Grace A. Dow College of Health Professions, a not-for-profit consulting group that specializes in the use of data and analytics to solve business problems.
IHBI’s goals include building bridges between the public and private sector, training the next generation of data scientists, and promoting the application of analytics to solve healthcare, social, and business challenges. IHBI hires students from programs across Central Michigan University’s campus. They have hired students with backgrounds in statistics, mathematics, business, computer science, information technology, and geography, just to name few. The staff at IHBI works with each student to develop their skills, working on actual business problems.
As a Senior Research and Data Scientist at IHBI, Dr. Imad Haidar is on the leading edge of those applications. Imad leads the IHBI team in designing and developing complex modeling strategies to predict business outcomes, and shares his expertise with IHBI students. IHBI has helped dozens of Fortune 500 companies solve complex, real world problems in manufacturing, retail, healthcare and more.
Imad holds a Ph.D. from the University of Queensland, Australia and has an extensive knowledge of machine learning methods; especially artificial neural networks, neuro-evaluation and multi-agent systems. His previous research also covered several aspects of the energy markets.
In addition, Imad, along with IHBI team members Jim Mentele, Joseph Pomerville, Luba Fishman, and Shar Tang, works with SAS to organize the annual Analytics Shootout competition, where student and faculty teams compete to solve a real-world analytics problem. It is a great way for students to demonstrate modeling skills, gain valuable experience, and earn recognition for their work. Now in its 10th year, the call for submissions has just gone out. Click here to learn more and register a team.
In an interview with Trent Smith, a government and education specialist at SAS, Imad imparts his philosophy for what makes an effective data scientist, and discusses the importance of students working with real-world data to keep up with the ever-evolving field of data science. He also shares what he believes are winning qualities of an Analytics Shootout team.
What does the analytics skills gap mean to you and IHBI?
Formal education provides students with the fundamental base knowledge to build their analytics skill. However, students tend to have limited exposure to practical experiences with a variety of real world data. In addition, the majority of problems we deal with on a daily basis are inter-disciplinary and require a diverse skill set. Students benefit from being exposed to several different types of data to address a variety of problems. This is how we view the skills gap at IHBI.
Data science skills must evolve quickly to keep pace with ever-changing technologies. What are the most important skills to have now, and five years from now?Because the data science field is evolving very fast, it’s difficult to say with certainty what technical skills student and professionals need to have five years from now. As an example, in the Internet of Things (IoT) era the increased use of sensor collected data and its quality will be of concern; another example is, in social media textual data the use of innuendo, sarcasm, analogy, and language skills will be extremely important to uncover what is really happening in the marketplace. Some technologies of importance right now include the handling of big data, cloud based analytics and open source software. However, in my opinion the most important skill is the ability to learn and adapt to the changes in the field.
What are the consistent skills and qualities data scientists should possess, regardless of changing technologies?
Data scientists should embrace a “philosophy” toward their work in thinking outside the box. Part of that should focus on being inquisitive and creative, and having the desire to learn and apply new things. Another important skill is the ability to think holistically: Be skeptical about the quality of data and preliminary results. Also, to break the problem down to the factors that affect the subject of interest, keeping in mind that there could be additional factors and relationships that may influence the outcomes.
What can organizations do to attract, retain, or foster more analytics talent?
In my opinion, for organizations to attract high caliber candidates they should embrace and have a positive view of data analytics. When companies display an enthusiasm about the application of data science, people may be more enticed to work for that particular organization. The same positive and supportive atmosphere, coupled with training opportunists and a diversity in work challenges, will go a long way to help companies retain and foster those talents.
What role do you think analytics companies should play in helping to close the gap?
An important thing employers could do is to reach out to universities and offer students more opportunities to have hands-on experience. This gives the students clearer understanding of what skills are required at organizations and give them direction about skills needed to be acquired.
What advice would you give students or adult learners interested in pursuing an analytics career?In my view to be successful in analytics and data science career, students as life learners, should develop a breadth of knowledge. Data scientists are frequently faced with cross-disciplinary business challenges that require cross-disciplinary skills. A single problem may have a business challenge, an economic aspect, an environmental consideration and health impacts all at once. A successful data scientist should be able to reach back and apply knowledge they accumulated through their past challenges to solve the current problem.
When grading the Shootout solutions, what criteria do you value most? What is the most impressive solution to a Shootout problem you’ve seen?
In my experience as a judge in the Analytics Shootout, there is not one correct solution to any of the Shootout problems. The best Shootout teams are the ones that show they have a good understanding of the problem, have looked for all possible data quality issues through thorough exploratory data analysis, and their creativity and justification for their approach toward their solution. Moreover, how well they relate their results to the question posed and propose actions to mitigate the problem are also very significant.
You’ve had diverse, wide-ranging experiences, both educationally and professionally. What’s the coolest or most impactful thing you’ve done, or seen done, using analytics?
During my career I’ve seen a number of great achievements brought in by advanced analytics. In my mind the most impressive applications are the ones that improve the quality of life for humans, such as embracing healthcare analytics, the advancement of analytics in education, and the application of analytics for water and energy sustainability.
Check out these previous posts in the Analytics Skills Gap Series
- Dudley Gwaltney, manager of the predictive analytics team in the Marketing Information Group of SunTrust Bank
- Maryanne Schretzman, executive director of the New York City Center for Innovation through Data Intelligence (CIDI)
- Edgar Enciso and Emmett Cox of BBVA Compass
- Kimberly Holmes, Senior Vice President, Strategic Analytics at XL Group
- Steve Doig, investigative reporter and educator at Arizona State University
- Jeremy TerBush, vice president of global analytics for Wyndham Exchange & Rentals
- MaryAnne DePesquo, a health analytics manager at BlueCross BlueShield of Arizona
- Mark Malchiodi, a SAS programmer for a large insurance carrier
- Tao Hong, lead of the Energy Analytics Research Laboratory within the Energy Production Infrastructure Center at the University of North Carolina at Charlotte
- Allison Jones-Farmer, a professor at Miami University of Ohio
- John Taylor, data analyst for the Inland Fisheries Division of Texas Parks and Wildlife