DDS-7250

Offermann, P., Levina, O., Schönherr, M., & Bub, U. (2009). Outline of a design science research process. In Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology (DESRIST '09). Association for Computing Machinery, New York, NY, USA, Article 7, 1–11. https://doi.org/10.1145/1555619.1555629

This paper presents a structured design science research process, providing practical guidance for researchers in information systems.

Vaishnavi, V. K., & Kuechler, W. (2015). Design science research methods and patterns (2nd ed.). CRC Press.
https://search.ebscohost.com/login.aspx?direct=true&AuthType=sso&db=edsool&AN=edsool.9781498715256&site=eds-live&scope=site&custid=natuniv

This book offers an in-depth exploration of design science research methods, including patterns for application across various domains.

Knauss, E. (2021). Constructive master's thesis work in industry: Guidelines for applying design science research. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), Madrid, ES, 110–121. https://doi.org/10.1109/ICSE-SEET52601.2021.00021

This paper provides guidelines for implementing design science research in the context of master's thesis projects within the industry.

Ruiz-Lopez, F., Perez-Ortega, J., Ortiz-Hernandez, J., Hernandez-Perez, Y., & Saenz-Sanchez, S. (2021). Systematic review of methodologies in data science. 2021 Mexican International Conference on Computer Science (ENC), Morelia, Mexico, 1–6. https://doi.org/10.1109/ENC53357.2021.9534813

This study offers a systematic review of methodologies used in data science, providing a comparative analysis of their applications and effectiveness.

Chatterjee, S., Sundarraj, R. P., & Dutta, K. (2023). Editorial special issue: Design science research in information systems and technology. IEEE Transactions on Engineering Management, 70(3), 803–805. https://doi.org/10.1109/TEM.2023.3235644

This editorial highlights advancements and discussions in design science research within information systems and technology.

Jones, J., & Goldring, J. (2021). Exploratory and descriptive statistics (Vols. 1-0). SAGE Publications Ltd. https://doi.org/10.4135/9781529682786

This resource provides foundational knowledge in exploratory and descriptive statistics, serving as an essential guide for data analysis.

Mienye, I. D., & Jere, N. (2024). A survey of decision trees: Concepts, algorithms, and applications. IEEE Access, 12, 86716–86727. https://doi.org/10.1109/ACCESS.2024.3416838

This article explores decision tree concepts, algorithms, and their applications, offering insights into their utility in various domains.

Cruz, E. F., & Cruz, A. M. R. D. (2020). Design science research for IS/IT projects: Focus on digital transformation. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), Seville, Spain, 1–6. https://doi.org/10.23919/CISTI49556.2020.9140972

This paper focuses on applying design science research methodologies in IS/IT projects, particularly within the context of digital transformation initiatives.

Singh, J., Singh, J., Singh, G., & Kaur, N. (2022). Exploratory data analysis for interpreting model prediction using Python. 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 1–6. https://doi.org/10.1109/SMARTGENCON56628.2022.10083533

This conference paper provides insights into exploratory data analysis techniques to interpret model predictions using Python, emphasizing practical applications.

Nokeri, T. C. (2021). Data science revealed: With feature engineering, data visualization, pipeline development, and hyperparameter tuning. Apress L.P. https://doi.org/10.1007/978-1-4842-6870-4

Key chapters:
Chapter 5: Logistic Regression Analysis
Chapter 6: Dimension Reduction and Multivariate Analysis Using Linear Discriminant Analysis
Chapter 8: Classification Using Decision Trees

This book serves as a comprehensive guide to data science, providing foundational knowledge and practical insights into machine learning and statistical methods.

Datar, R., & Garg, H. (n.d.). Hands-on exploratory data analysis with R: Become an expert in exploratory data analysis using R packages. Packt Publishing. ISBN: 978-1-78980-208-5; 978-1-78980-437-9

This book offers a practical guide to exploratory data analysis techniques using R packages, designed for beginners and experienced analysts alike.

Oluleye, A. (n.d.). Exploratory data analysis with Python cookbook: Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data. Packt Publishing. ISBN: 978-1-80323-110-5; 978-1-80324-613-0

This cookbook provides over 50 practical recipes for conducting exploratory data analysis with Python, covering diverse datasets and real-world applications.

Jones, B. (2019). Avoiding data pitfalls: How to steer clear of common blunders when working with data and presenting analysis and visualizations. John Wiley & Sons, Incorporated.

This book guides readers on how to avoid common mistakes in data analysis and visualization, offering practical tips for better communication of insights.

Meeks, E. (2022). The data visualization lifecycle [Video file]. Addison-Wesley Professional, 240 minutes.
https://go.oreilly.com/nationaluniversity/https://learning.oreilly.com/videos/the-data-visualization/9780138068509/

This video explores the complete lifecycle of data visualization, from data preparation to final presentation, with practical examples and techniques.

Setlur, V., & Cogley, B. (2022). Functional aesthetics for data visualization. Wiley.
https://go.oreilly.com/nationaluniversity/https://learning.oreilly.com/library/view/functional-aesthetics-for/9781119810087/?sso_link=yes&sso_link_from=NationalUniversity

This book merges design principles with data visualization techniques, focusing on the balance between functionality and aesthetics.

Jensen, E. A., Borkiewicz, K., Naiman, J. P., Levy, S., & Carpenter, J. (2023). Evidence-based methods of communicating science to the public through data visualization. Sustainability (Basel, Switzerland), 15(8), 6845. https://doi.org/10.3390/su15086845

This article presents evidence-based methods for using data visualization to effectively communicate scientific findings to the public.

Mohandoss, D. P., Shi, Y., & Suo, K. (2021). Outlier prediction using random forest classifier. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 27–33. https://doi.org/10.1109/CCWC51732.2021.9376077

This paper explores the use of random forest classifiers for detecting and predicting outliers in datasets, with a focus on performance and accuracy.

Bergmeir, C. (2023). Common pitfalls and better practices in forecast evaluation for data scientists. Foresight: The International Journal of Applied Forecasting, 70, 5–12.

This article highlights frequent mistakes in forecast evaluation and offers best practices for data scientists to improve their methodologies.

Rainio, O., Teuho, J., & Klén, R. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-56706-x

This paper discusses evaluation metrics and statistical tests used in machine learning, providing guidance on their selection and application.