ZHCAIHUA / research-design

This course will cover the fundamental steps and implementation on developing the initial ideas to formal academic writing accordingly. Students will be given the mechanisms on how to transform and digest the literature reviews that leads to the proposed title.

Research Design and Analysis in Data Science (MCSD1043)

Course Synopsis

This course will cover the fundamental steps and implementation on developing the initial ideas to formal academic writing accordingly. Students will be given the mechanisms on how to transform and digest the literature reviews that leads to the proposed title. The theoretical and practical aspects of implementing draft project proposal will be the milestone of this course. Ordered, Critical and Reasoning Exposition of knowledge through students efforts.

🔥 Important Things

• Student Information: Access personal and academic information relevant to your student profile.

• Course Information: Find detailed course content, schedules, and requirements for the current semester.

• Assignment: View and download current assignments, submission guidelines, and deadlines.

• Exercise: Engage with exercises designed to complement your coursework and enhance learning.

• E-Learning UTM: Connect to the University's e-learning platform for course materials, discussions, and updates.

Notes

No.	Content	File
1.	Research Gaps
2.	Challenges of Manual Literature Reviews
3.	The Efficient Literature Search

AI Tools

No.	Content	File
1.	Top Computer Science Literature Databases
2.	Identifying Research Gaps
3.	Paper Discovery
4.	Paper Visualization
5.	Chatbot and Assistance
6.	Text Summarizers
7.	Citation Management

Data Science Resources

• Data Science - A Complete Introduction: This resource provides a comprehensive overview of data science, including its importance and applications in various industries.

• What is Data Science? Definition, Examples, Tools & More: DataCamp's guide offers a clear definition of data science, supplemented with practical examples and a list of essential tools.

• Intro to data science on Google Cloud: Google Cloud's blog introduces the basics of data science and how their platform can be utilized for data science projects.

• Modernizing data science lifecycle management with AWS and Wipro: This article discusses the collaboration between AWS and Wipro to enhance data science lifecycle management through modern solutions.

• Deconstructing Data Science: Breaking The Complex Craft Into It’s Simplest Parts: A Medium article that simplifies the complex field of data science into its fundamental components.

• AI and Data Scientist Roadmap: A step-by-step guide to becoming an AI and Data Scientist in 2024, outlining the skills and knowledge required.

• Why Is Data Science Different than Software Development? It Starts with Data…Lots o’ DATA!!: An exploration of the unique challenges and requirements of data science compared to traditional software development.

• Conceptual Framework for Solving Data Analysis Problems: GeeksforGeeks provides a structured approach to tackling data analysis challenges.

• Battle of the Data Science Venn Diagrams: A visual comparison of various Venn diagrams that attempt to define the data science field.

• ScienceGate: Features a collection of the latest research papers in data science, covering a wide range of topics from environmental impact to business applications.

• Harvard Data Science Review: Offers a platform for foundational thinking, research milestones, and educational innovations in data science.

• DiscoverDataScience.org: Provides articles written by data science professors, experts, and practitioners, based on professional research of the industry and related trends.

• The Conversation: Has a section dedicated to data science, with articles discussing the impact of data science on various fields.

Github Repository: Literature Review

• The Daily Life of a PhD Student: Offers insights into the routine and experiences of a PhD student, providing valuable tips for navigating academic research.

• UTM - Systematic Literature Review using AI: A repository by UTM that explores the use of AI in conducting systematic literature reviews.

• Obsidian Vault for Systematic Literature Reviews in Computer Science: This vault contains resources and templates for conducting literature reviews in computer science using Obsidian.md.

• Systematic Literature Review - Gitbook: A Gitbook dedicated to guiding readers through the process of conducting systematic literature reviews.

• Obsidian.md for Academic Writing: This repository provides tools and tips for using Obsidian.md to enhance academic writing and research.

• AI tools: A collection of AI tools and resources compiled to assist researchers in various tasks related to data science and machine learning.

Github Repository: Data Science

• Learn Github: A tutorial repository designed to help beginners understand and master the functionalities of GitHub.

• Big Data Management: This repository focuses on strategies and technologies for managing large datasets effectively.

• High Performance Data Processing: A collection of resources dedicated to processing data at high speeds and efficiency.

• Special Topic in Data Engineering: A repository covering advanced topics and discussions in the field of data engineering.

• Python for beginners: Offers tutorials and exercises for those new to programming, specifically in the Python language.

• Web Scraping: Contains code and documentation for scraping data from the web using Python.

• Exploratory Data Analysis (EDA): This repository provides examples and best practices for performing exploratory data analysis using Python.

• Big data processing: Focuses on techniques and code examples for processing big data using Python.

• Django: A guide for learning Django, a high-level Python web framework that encourages rapid development and clean, pragmatic design.

Articles

1. Koons, G.L., Schenke-Layland, K., & Mikos, A.G. (2019). Why, when, who, what, how, and where for trainees writing literature review articles. Annals of Biomedical Engineering, 47(11), 2334–2340. https://doi.org/10.1007/s10439-019-02290-5
2. Jaidka, K., Khoo, C. S. G., & Na, J.-C. (2013). Literature review writing: How information is selected and transformed. Aslib Proceedings, 65(3), 5. https://doi.org/10.1108/00012531311330665
3. Barrasso, A. P., & Spilios, K. E. (2021). A scoping review of literature assessing the impact of the learning assistant model. International Journal of STEM Education, 8(1), Article 12.
4. Salazar-Reyna, R., Gonzalez-Aleu, F., Granda-Gutierrez, E.M.A., Diaz-Ramirez, J., Garza-Reyes, J.A. and Kumar, A. (2022), "A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems", Management Decision, Vol. 60 No. 2, pp. 300-319. https://doi.org/10.1108/MD-01-2020-0035
5. Espinoza Mina, M. A., & Gallegos Barzola, D. D. P. (2018). Data Scientist: A Systematic Review of the Literature. Technology Trends, 476–487. doi:10.1007/978-3-030-05532-5_35
6. Memarian, B., Doleck, T. Data science pedagogical tools and practices: A systematic literature review. Educ Inf Technol (2023). https://doi.org/10.1007/s10639-023-12102-y
7. Sandaruwan, I.P.T., Janardana, J.A.B. and Waidyasekara, K.G.A.S., 2023. Data science applications for carbon footprint management in buildings: A systematic literature review. In: Sandanayake, Y.G., Waidyasekara, K.G.A.S., Ramachandra, T. and Ranadewa, K.A.T.O. (eds). Proceedings of the 11th World Construction Symposium, 21-22 July 2023, Sri Lanka. [Online]. pp. 446-459. DOI: https://doi.org/10.31705/WCS.2023.37. Available from: https://ciobwcs.com/papers/446
8. Arruda, H. M., Bavaresco, R. S., Kunst, R., & Barbosa, J. (2023). Data science methods and tools for Industry 4.0: A systematic literature review and taxonomy. Sensors, 23(11), 5010. https://doi.org/10.3390/s23115010
9. Saltz, J. S., & Krasteva, I. (2022). Current approaches for executing big data science projects: A systematic literature review. PeerJ Computer Science, 8(January 2019), e862. https://doi.org/10.7717/peerj-cs.862
10. Reddy, R. C., Bhattacharjee, B., Mishra, D., & Mandal, A. (2022). A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy. Information Systems and e-Business Management, 20(3). https://doi.org/10.1007/s10257-022-00550-x
11. Alonso-Fernandez, C., Calvo-Morata, A., Freire, M., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers & Education, 141(1). https://doi.org/10.1016/j.compedu.2019.103612
12. Saltz, J. S., & Dewar, N. (2019). Data science ethical considerations: A systematic literature review and proposed project framework. Ethics and Information Technology, 21(3), 197-208. https://doi.org/10.1007/s10676-019-09502-5
13. Al-Tashi, Q., Abdulkadir, S. J., Rais, H. M., Mirjalili, S., & Alhussian, H. (2020). Approaches to Multi-Objective Feature Selection: A Systematic Literature Review. IEEE Access, 8, 125076-125096. https://doi.org/10.1109/ACCESS.2020.3007291
14. Wube, H. D., Esubalew, S. Z., Weldesellasie, F. F., & Debelee, T. G. (2022). Text-Based Chatbot in Financial Sector: A Systematic Literature Review. Data Science in Finance and Economics, 2(3), 209-236. https://doi.org/10.3934/DSFE.2022011
15. Aguilar-Esteva, V., Acosta-Banda, A., Carreño Aguilera, R., & Patiño Ortiz, M. (2023). Sustainable Social Development through the Use of Artificial Intelligence and Data Science in Education during the COVID Emergency: A Systematic Review Using PRISMA. Sustainability, 15(8), 6498. https://doi.org/10.3390/su15086498

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