Work-in-Progress: Towards Designing a Multidisciplinary Big Data Analytics (BDA) Course

Haroon Malik; David A Dampier

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Conference: 2023 ASEE North Central Section Conference
Location: Morgantown, West Virginia
Publication Date: March 24, 2023
Start Date: March 24, 2023
End Date: March 25, 2023
Page Count: 13
DOI: 10.18260/1-2--44925
Permanent URL: https://strategy.asee.org/44925
Download Count: 163

Paper Authors

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Haroon Malik Marshall University

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Dr. Malik is an Associate Professor at the Department of Computer Sciences and Electrical Engineering, Marshall University, WV, USA.

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David A Dampier Marshall University

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Dr. Dave Dampier is Dean of the College of Engineering and Computer Sciences and Professor in the Department of Computer Sciences and Electrical Engineering at Marshall University. In that position, he serves as the university lead for engineering.

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Abstract

As the availability of data analytics and data science programs grows based on market demands, foundational technical skills are essential to equip graduates for readily available entry-level jobs in the field. Every CS and IT undergraduate student must have foundational knowledge and competency in this collective paradigm. The students should be provided with hands-on experience in managing big data applications, conducting analytics (diagnostic, descriptive, prescriptive, and predictive) and acquiring skills necessary to meet current and future industry demands and enable them to carry out applied research. However, the challenge is that many of the tools and techniques of the big data and cloud computing paradigm have emerged only in the last few years. Besides, ACM or ABET provides no standard guidelines to integrate big data topics into the CS curriculum.

Furthermore, only the draft versions of other curriculum efforts, such as the Data Science Task Force and NSF/IEEE-TCPP Curriculum Initiative on PDC are currently available. Since many four years, institutes have developed their curriculum around these guidelines, i.e., provided by ACM/IEE join curriculum recommendations and ABET; hence undergraduate students lack the necessary Data Science knowledge. Therefore, it is imperative to equip the student entering the Data Science graduate program with the background knowledge in the introductory Data Analytics course.

The Work-in-Progress (WIP) reports on our experience in offering such background knowledge by constructing five modules as part of the Data Analytics course. These include: 1. Big Data Systems ⸺ Focuses on MapReduce programming framework and analytic engines such as Hadoop and Spark 2. Data Analytics Took-kit ⸺ Focus on (a) the design of preprocessing pipelines and data transformation that results in the representation of data that can support effective machine learning and (b) Analytic libraries (Pandas, Numpy, Scitkitlearn and Kerass) 3. Mining Ultra-Large-Scale Repositories ⸺ Focus on programmatically accessing version control systems (e.g., SourceForge (700k+projects), GitHub (7M+ projects), and Google Code (300k+projects)), data storage and retrieval, methods for reproducible experimental design and dynamic report generation (Jupyter Notebook/Pandoc and workflows). 4. Machine Learning ⸺ Focus on data parallelism, model/parameter parallelism and distributed machine learning. 5. Big Data Visualization ⸺ Focus on data management and analysis, spatiotemporal data, human perception and cognition, and Matplotlib and Seaborn libraries.

The Work-in-Progres also discuss the competency outcomes, module content, assessment instruments, and student assessment and survey results for each of the five modules. The objective of the WIP is to share our experience with the instructors who aim to incorporate Big Data Analytics in the context of higher education.

Citation
Format

Malik, H., & Dampier, D. A. (2023, March), Work-in-Progress: Towards Designing a Multidisciplinary Big Data Analytics (BDA) Course Paper presented at 2023 ASEE North Central Section Conference, Morgantown, West Virginia. 10.18260/1-2--44925

TY  - CPAPER
AB  - As the availability of data analytics and data science programs grows based on market demands, foundational technical skills are essential to equip graduates for readily available entry-level jobs in the field.  Every CS and IT undergraduate student must have foundational knowledge and competency in this collective paradigm. The students should be provided with hands-on experience in managing big data applications, conducting analytics (diagnostic, descriptive, prescriptive, and predictive)  and acquiring skills necessary to meet current and future industry demands and enable them to carry out applied research. However, the challenge is that many of the tools and techniques of the big data and cloud computing paradigm have emerged only in the last few years. Besides, ACM or ABET provides no standard guidelines to integrate big data topics into the CS curriculum.

Furthermore,  only the draft versions of other curriculum efforts, such as the Data Science Task Force and NSF/IEEE-TCPP Curriculum Initiative on PDC are currently available. Since many four years, institutes have developed their curriculum around these guidelines, i.e.,  provided by ACM/IEE join curriculum recommendations and ABET; hence undergraduate students lack the necessary Data Science knowledge. Therefore, it is imperative to equip the student entering the Data Science graduate program with the background knowledge in the introductory Data Analytics course. 

The Work-in-Progress (WIP) reports on our experience in offering such background knowledge by constructing five modules as part of the Data Analytics course. These include: 
1.	Big Data Systems ⸺ Focuses on MapReduce programming framework and analytic engines such as Hadoop and Spark 
2.	Data Analytics Took-kit ⸺ Focus on (a) the design of preprocessing pipelines and data transformation that results in the representation of data that can support effective machine learning  and (b) Analytic libraries (Pandas, Numpy, Scitkitlearn and Kerass)
3.	Mining Ultra-Large-Scale Repositories  ⸺  Focus on programmatically accessing version control systems (e.g., SourceForge (700k+projects), GitHub (7M+ projects), and Google Code (300k+projects)), data storage and retrieval, methods for reproducible experimental design and dynamic report generation (Jupyter Notebook/Pandoc and workflows).
4.	Machine Learning ⸺ Focus on data parallelism, model/parameter parallelism and distributed machine learning.
5.	Big Data Visualization ⸺ Focus on data management and analysis, spatiotemporal data, human perception and cognition, and Matplotlib and Seaborn libraries.

The Work-in-Progres also discuss the competency outcomes, module content, assessment instruments, and student assessment and survey results for each of the five modules. The objective of the WIP is to share our experience with the instructors who aim to incorporate Big Data Analytics in the context of higher education. 

AU  - Haroon Malik
AU  - David A Dampier
CY  - Morgantown, West Virginia
DA  - 2023/03/24
PB  - ASEE Conferences
TI  - Work-in-Progress: Towards Designing a Multidisciplinary Big Data Analytics (BDA) Course
UR  - https://strategy.asee.org/44925
DO  - 10.18260/1-2--44925
ER  -