Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

Abstract

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large-scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post-course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.

Findability, Accessibility, and Usability of Data Portals in Education

“…Since the term was just coined in 2008 by Patil and Hammerbacher to describe their job functions at Facebook and Linkedin (Kim et al, 2016), the data scientist is the newest specialization in the information management field and is in high demand (Strawn, 2016). The fact that the specialization of a data scientist is a recent occurrence is corroborated by Asamoah et al (2017) as they describe a design for a big data analytics course for their university"s business school in the Management Information System program. Strawn (2016) further speculates that the data scientist is "not only in demand in Silicon Valley, but also in company headquarters around the world."…”

“…The individual who does have that skillset is the data scientist (Kim, et al 2016). The Global Institute predicted that just for the United States alone, by 2018 there will be shortage of 1.5 million data scientists who are fully trained to meet the job market demand for those individuals (Asamoah, Sharda, Zadeh, & Kalgotra, 2017). Therefore, data portals need to be designed so that authorized users who "have different expertise and know-how on using electronic media" can find, access, and use the data portal (Wimmer & Holler,p.…”

Analytics Curriculum for Undergraduate and Graduate Students

“…Asamoah, Sharda, Zadeh, and Kalgotra () present an experiential perspective in connection to design and delivery of a big data analytics course at a major Midwestern university. They highlight how big data analytics can be delivered to students with different backgrounds by providing details of the course objectives, pedagogical approach, module design, and concepts covered in the course.…”

“…These they posit can be summarized as business (e.g., independent learning ability), analytical (e.g., ability to integrate analysis from multiple sources into a business solution), and technical skills (e.g., Excel). Asamoah, Sharda, Zadeh, and Kalgotra (2017) present an experiential perspective in connection to design and delivery of a big data analytics course at a major Midwestern university. They highlight how big data analytics can be delivered to students with different backgrounds by providing details of the course objectives, pedagogical approach, module design, and concepts covered in the course.…”

Creating Impactful Student Learning in a Business Analytics Program through Leveraging Scholar‐Practitioner Engagement

“…Experiential learning may be particularly critical for analytics programs given the increasing number of and enhancements to tools, techniques, and processes (Rienzo & Chen, 2018). Others have highlighted the importance of applied practice—specifically applying technical concepts and techniques in a specific business/industry domain (Asamoah, Sharda, Zadeh, & Kalgotra, 2017), knitting together detailed knowledge of data and processes with analytics techniques. Several recent papers have highlighted the implementation of applied projects as an effective means to create experiential learning (Hilton, Cai‐Hillon, & Brammer, 2012; Konrad, 2019; Salo, 2012; Speier‐Pero, 2017; Wilder & Ozgur, 2016).…”