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Technologies for Teaching Big Data Analytics

Richard S. Segall Arkansas State University

College of Business, Department of Computer & Information Technology State University, AR 72467-0130; Phone: 870-972-2989; E-mail: rsegall@astate.edu

ABSTRACT

This paper provides some insight into the use of current advances in technology for the teaching of Big Data analytics in the classroom from the personal experience of the author. Teaching Big Data involves different techniques and approaches than that used for traditional courses in database management and data mining with nominal sized data bases. Also these new technologies of Big Data allow the visual analysis of datasets with dimensionalities of millions of rows on classroom personal computers, tablets and other mobile devices. This is a new approach in teaching achieved with advances in technology and software that a few years ago was not even possible or envisioned to be done in a classroom setting for either an undergraduate or graduate course in data mining or business analytics. This paper discusses some background on teaching Big Data analytics, experiences of teaching “Big Data” at A-STATE College of Business, teaching Big Data using Tableau, and conclusions and future directions.

BACKGROUND Below are some of the experiences of others in teaching Big Data in academic environments. Table 1 below summaries some of the recent contributions to teaching Big Data that are discussed in more depth in this section and elsewhere in this paper.

Table 1: Some recent contributions to teaching Big Data

Sigman et a. (2014) Tabular summary of top master’s degree programs in Big Data Analytics

Hill and Kline (2014) Challenges of teaching Big Data

Wixom et al. (2014) Survey results of AIS (Association of Information Systems) Special Interest Group in Decision Support Systems (SIGDSS)

Ellaway et al.(2014) Teaching Big Data in context of Health Professionals

Eyon (2013) Cites reports on Potential of Big Data for Education

Siemens et al. (2011) Presents Society for Learning Analytics Research (SoLAR) for teaching, learning, training and development of Big Data.

Rayes (2015) Presents examples in Tabular Form for types of learning analytics resources

Data Warehouse Institute (2014) Checklist report of 8 considerations for utilizing Big Data analytics with Hadoop

Rossland (2015) SAS Course Notes on SAS Visual Analytics

Ravenna et al. (2015) SAS Course Notes on SAS Visual Statistics

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Sigman et al. (2014) provided experiences, lessons learned, and future directions of teaching Big Data including making a case for Big Data education and how universities are responding to this need. Sigman et al. (2014) also provided a tabular summary of the top 20 master’s degree programs in Big Data Analytics offered by universities in both US and Canada. Hill and Kline (2014) outlines the preparation necessary for the development of teaching Big Data in a Business School and initial delivery of this course with focus on the challenges that an instructor may face as well as hurdles that students may need to overcome to be successful in the class. Wixom et al. (2014) discussed the current state of business intelligence in academia with the arrival of Big Data and stated its purpose to serve as a “call to action” for universities to respond to the emerging market needs in business intelligence and business analytics to be including “Big Data.” Wixom et al. (2014) presented graphics providing visual summaries and selected written responses to a survey conducted by the Association of Information Systems (AIS) Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) with the Teradata University Network (TUN). Ellaway et al. (2014) published a research article in the Medical Teacher journal for developing a role of Big Data and analytics in the context of health professional education, and citing that there is much yet to be done before they can become part of the mainstream health professional health practice. Eyon (2013) cites that the US Government is producing reports on the potential of Big Data for education. Raghupathi and Raghupathi (2014) describe the promise and potential of Big Data analytics in health care. Lazer et al. (2014) discussed that Google Flu Trents (GFT) that was considered as an exemplary use of Big Data and built into Center for Disease Control (CDC) reports instead was predicting more than double the proportion of doctor visit for influenza-like illness (ILI) than the CDC which bases its estimates on surveillance reports from across the United States. Siemens et al. (2011) presented a proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques. The Project Overview of Siemens et al. (2011) presents that the Society for Learning Analytics Research (SoLAR) as an interdisciplinary network of leading international researchers who are exploring the role and impact of analytics on teaching, learning, training and development using Big Data. Siemens et al. (2011) also presented an open source learning management system named “Moodle” that shows the network of an integrated learning analytics system. Moodle contains an Analytics Engine that uses concept patterns, semantic analysis, sense-making models, discourse analysis, social network analysis and conceptual development. Siemens and Long (2011) discussed the use of analytics in learning and education.

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DeMauro et al. (2015) summarized key research areas related to Big Data for future development, identified emerging trends, suggested opportunities for future development, and concluded about the centrality of Google in the initiation of the current thinking about Big Data. Reyes (2015) discusses the skinny on Big Data in education and the way in which analytics information flows from students to other stakeholders whose challenges include the movement of traditional analytics to leaner-centered analytics and working with datasets across various settings, addressing issues with technology and resolving ethical concerns. Rayes (2015) presents examples in tabular form for types of learning analytics resources that include open-source learning platform “Moodle” and GISMO that is an interactive tracking system built for Moodle that displays data through a graphical interface. Reyes (2015) also provides in this table the Pittsburgh Science of Learning Center DataShop provided at htpp://www.learnlab,org/technologies/datashop/ that is a data repository and analysis service that provides access to intelligent tutoring systems datasets. The Data Warehouse Institute (TDWI) (2014) published a checklist report on eight considerations for utilizing Big Data analytics with Hadoop that includes examining Big Data exploration and insight discovery by utilizing such techniques as HiveQL of Hadoop ecosystem for performing queries that can work with MapReduce to distribute the running of a query. However according to TDWI (2014) HiveQL may take minutes or even hours to get query responses, and interactive query engine Cloudera Impala may speed up query times for Big Data. Rossland (2015) provides step-by-step instructions in SAS Notes for teaching SAS Visual Analytics and Ravenna et al. (2015) provides similar for teaching SAS Visual Statistics in the classroom using Big Data that is discussed in more depth in the following section.

EXPERIENCES OF TEACHING “BIG DATA” AT A-STATE COLLEGE OF BUSINESS One of the important differences that first needs to be understood in defference in the modeling approach between traditional data mining and Big Data Analysis. This is illustriated by the below figure from SAS Notes for SAS Visaul Statistics for Professors by Shelley (2014):

Figure 1: Modeling Approach Modification for Traditional Data Mining to Big Data Approach

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Copy r ight © 2014, SAS Insti tute I nc . All r i ghts r eserved.

Modeling Approach Modification

Traditional Approach (SEMMA) Big Data Approach (EMSMA)

Sample Explore

Explore Modify

Modify Segment

Model Model

Assess Assess

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Source: [Ravenna et al. (2015) page 1-22.] An undergraduate course CIT 3663 Data Mining is taught in fall of odd-years in the A-STATE College of Business. However in the 2-year span from teaching this course in fall 2013 to fall 2015 many new advances in available software for teaching Big Data in the classroom occurred. Instead of spending the last 5 weeks of a 14 week semester on a comprehensive project to again illustrate the use of standard data mining techniques, this time period was used to instead learn about Text Mining using SAS Text Miner and also Big Data by utilizing two new SAS software of SAS Visual Analytics and SAS Visual Statistics that is a module within SAS Visual Analytics. The topics in SAS Visual Analytics using Big Data covered Getting Started with SAS Visual Analytics, Using SAS Visual Analytics Explorer, and Designing Reports with SAS Visual Analytics. The topics in SAS Visual Statistics included Introduction to SAS Visual Statistics, Cluster Segmentation, Models with Continuous Targets, and Models with Categorical Targets. As shown in figure the SAS Mobile BI (Business Intelligence) app enable users to view and interact with SAS Visual Analytics reports on iPads, iPhones and Android mobile devices. Figure 2 below (Source: SAS (2015)) shows the component modules of SAS Visual Analytics architecture of Explorer, Designer, Web Viewer, Data Builder, Administrator, Mobile BI (Business Intelligence) and Graph Builder.

Figure 2: SAS Visual Analytics Architecture [Source: SAS (2015) Slide 5 of “Getting Started with SAS Visual Analytics” of Trainer’s Kit.]

The amazing part of the class was that the partnership of SAS with Teradata University (TUN) that allowed my students to register for user accounts on their supercomputer and Big Data databases for use in the class exercises that were of dimensionality consisting of over a million rows of data. In fact for the class exercise using the Insight Toy Company Demonstration from the Teradata

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University access, there were a total observations of 3,597,272 for that the SAS Visual Analytics and SAS Visual Statistics software was able to process in an execution time as if it were actually a normal sized database, and this could all have been done on a mobile device such as a tablet, iPad or iPhone. Figure 3 below provides a screen shot provided by SAS (2015a) of how to access the Big Data datasets from Teradata University Network.

Figure 3: Teradata University Network Webpage for access to SAS Visual Analytics

[Source: SAS (2015a) How to Access SAS Visual Analytic Tools] Several figures produced by student in the CIT 3363 Data Mining class that I taught at A-STATE in fall 2015 semester are also provided in this paper that replicated that in the SAS Notes on Visual Analytics. Figure 4 (modeled from page 1-11 of Source: SAS (2015)) shows the home page of SAS Visual Analytics with entry nodes for Data Explorer and Report Designer.

Figure 4: Entry Point for Data Explorer in SAS Visual Analytics [Source: SAS (2015): PowerPoints of Chapter 1: Getting Started with SAS Visual Analytics, p. 1-11.]

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Figure 5 shows the student assignment showing the correlation of selected measures for the Insight Toy data set of over 3-million data values obtained from the Teradata University Network (TUN) website as performed in my class of teaching CIT 3663 Data Mining in the College of Business at Arkansas State University.

Figure 5: Screen shot of Correlation of selected measures using SAS Visual Analytics

[Source: Student assignment of Richard White for CIT 3663 Data Mining of Fall 2015 semester at A-STATE-Jonesboro using Step 8 of Chapter 1 of SAS Notes on Visual Analytics.]

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Figure 6: Screen shot of the validation that 3,597,272 data values from Insight Toy Company inputted from the Teradata University Network (TUN) into SAS Visual Analytics

[Modeled from page 2-11 of Source: SAS(2015)]. Figure 7 (modeled from page 2-25 of Source: SAS (S015)) shows the actual versus target for each Toy Product Line. Figure 8 (modeled from Figure 2-59 of Source: SAS (2015)) shows the SAS Visual Analytics Dashboard. Figure 9 (Source: SAS (2015) slide 26 of Instructor’s Trainer Kit) shows the SAS Mobile Business Intelligence interface that enables users to view and interact with SAS Visual Analytics reports on iPads, iPhones, and Android mobile devices.

Figure 7: Production by Product Line of Insight Toy Company with over 3 million data items

[Source: (Figure 2-25) of SAS Notes on SAS Visual Analytics.]

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Figure 8: SAS Visual Analytic Dashboard. [Source: Student assignment of Richard White for CIT 3663 Data Mining of Fall 2015 semester at A-STATE-Jonesboro using Figure 2-59 of SAS Notes on Visual Analytics.]

Figure 9: SAS Mobile Business Intelligence Interface [Source: Source: SAS (2015) Slide26 of “Getting

Started with SAS Visual Analytics” PowerPoint Slides of Trainer’s Kit.]

SAS Visual Statistics is a module within SAS Visual Analytics similar to arrangement that SAS Text Miner is a module within SAS Enterprise Miner. SAS Visual Statistics performs for the analysis of Big Data using linear and logistic regression, generalized linear model, decision tree analysis, cluster analysis, model performance, and allows management of projects and models within the SAS Visual Statistics environment. Figure 10 below shows one of the pull-down tabs with the available plots and module available within SAS Visual Statistics.

Figure 10: Pull-down tab of Modules within SAS Visual Statistics

[Source:SAS Notes on SAS Visual Statistics (2015) page 1-19.]

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Figure 11 below shows Paraell Corrdinates Plot for different demographic categories of data relating to Home Value, and home income, and demographics of customer using cluster segmentation of SAS Visual Analytics software.

Figure 11: Parallel Coordinates Plot generated by SAS Visual Statistics.

[Source: PowerPoints of SAS (2015) Trainer’s Kit for SAS Visual Statistics, slide 38.]

TEACHING BIG DATA USING TABLEAU

Another new software for teaching Big Data is that of Tableau manufactured by Tableau.com. One of the keys in Big Data is the ability to connect to data of all types. Tableau has over 45 named connections, these connections range from file based sources to complete web flexibility. File base sources include "Statistical file” options, this gives us the ability to read SAS, SPSS, and R file sets (R server be use to run live R models). On the Server side we have connectivity to all major relational data bases (MSFT, Oracle, IBM, etc), all major MPP databases (Teradata, Netezza, Greenplum, Vector, Matrix, etc), major cloud platforms (AWS, MSFT, Google), Cubes (MSFT, Hyperion), Web sources (Odata and WDC (ability to define custom web connections), cloud db (Redshift, Snowflake, RDS, etc.), all major Hadoop instances (Cloudera, Hortonworks, Presto, MapR, and a SparkSQL driver), virtualization technologies (Cisco Information Server (formerly Composite), Kognitio), NoSQL (MarkLogic, DataStax), and we even have the ability to connect to thousands of other sources using other ODBC (Open Database Connectivity) that is anything with a SQL interface and Web Data Connector (any middleware or web source). These options in Tableau are offered standard with LIVE connectivity with an option for choice to extract. Figure 12 below shows the actual window of Tableau connectivity options for saved data sources.

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Figure 12: Window for Tableau options for Big Data analysis

[Source: Provided by e-mail from Representative from Tableau.com cited in Acknowledgements.]

Tableau provided me with a WebEx conference in September 2016 in which the following figures 13, 14 and 15 that were provided in a live demonstration from Representative Paul Lilford from Tableau.com. Figure 13 illustrates the clustering of Big Data using map of United States, figure 14 illustrates the use of Big Data on a world map with color-coded bar charts for segmentation and clustering characteristics, and figure 15 provides some key indicators for Big data. Tableau Desktop is free for instructors around the world as stated on company web page at www.tableau.com/academcic/teaching [Tableau (2016b)] which also provides a video with a professor’s story of helping students learn Tableau and get relevant internships. An instructor’s resource page at https://community.tableau.com/community/teachers/overview [Tableau (2016c)] for self-service learning, curriculum materials and license support. Tableau provides a large library of on-demand tutorials and a dedicated support community for teaching Big Data analysis. According to the above URL on Tableau website [Tableau (2016b)], thousands of schools around the globe are using Tableau in the classroom including schools such as Nanyang Polytechnic in Singapore, Thailand [Tableau (2016e)], Cranfield University in UK [Tableau (2016d)], and New York University [Tableau (2016f)] in USA. There is a private group for instructors in the “Tableau for Teaching” program for which you must be signed in to request access.

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Figure 13: Clustering of Big Data using map of United States and Heat Map. [Source: WebEx conference call of September 9, 2016 with Paul Lilford from Tableau.com.]

Figure 14: Big Data for the World Map and Color-coded Bar charts for Segmentation and Clustering Characteristics [Source: WebEx conference call of September 9, 2016 with Tableau Representative cited in Acknowledgements.]

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Figure 15: Some Key Indicators for Big Data Analysis using Tableau. [Source: WebEx conference call of September 9, 2016 with Tableau representative cited in Acknowledgements.]

CONCLUSIONS AND FUTURE DIRECTIONS This paper presents some of the experiences of the author in exploring and applying new technologies in the classroom for Big Data that is quite different from those in teaching a course in traditional data mining or database management. It was unthinkable in recent past that datasets of multiple million rows of data could be used for a class assignment done during class lab time on both desktop computers and mobile devices. The area of Big Data has rapidly expanded in a new realm of dimensionality that now mandates new semester course(s) on this topic alone. Looking on the web this trend has already taken place. The future directions of research include the continuation of study of enhancements in SAS Visual Analytics, SAS Visual Statistics and of how other Big Data software could also be incorporate in teaching such as Tableau, and possible design for a separate course on Big Data.

ACKNOWLEDGEMENTS Appreciation needs to be stated to SAS Institute for the workshops they provided to me free-of-charge at the World Headquarters campus in Cary, NC in their 2015 Summer Program for Professors and the accompanying complementary Trainer Kits that SAS provided me. I also need to acknowledge Paul Lilford, the Senior Director of Technology & Market Intelligence at Tableau Software for the extremely informative WebEx conference demonstration of Tableau software for Big Data for potential for use in the both the classroom and research and his numerous other

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communications, and also acknowledgement and appreciation from Tableau software for a generous Travel Award for part of expenses to present this paper at SWDSI 2017.

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