COMP09015 2019 Research Methods for Data Science
Full Title
Research Methods for Data Science
Transcript Title
Research Methods for Data Scie
Code
COMP09015
Attendance
N/A %
Subject Area
COMP - 0613 Computer Science
Department
COEL - Computing & Electronic Eng
Level
09 - Level 9
Credit
05 - 05 Credits
Duration
Semester
Fee
€
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Author(s)
Saritha Unnikrishnan, Mary Loftus, Therese Hume
Programme Membership
SG_KDATA_M09 201900 Master of Science in Data Science
SG_KDATA_M09 202000 Master of Science in Data Science
SG_KDATA_M09 202100 Master of Science in Computing (Data Science)
SG_KCOMP_N09 202300 Postgraduate Certificate in Computing (Data Science)
Description
Data science involves exploring problems in particular knowledge domains, identifying how data can be analysed to help solve these problems and building and evaluating appropriate models to achieve insights. To ensure the integrity and accuracy of data science research, it is essential that the student develops a deeper understanding of the fundamentals of research methods and ethics. This module introduces skills needed to develop meaningful research questions within particular problem domains, and to design appropriate research strategies and designs to address these. A critical approach that considers and deals with issues such as methodology suitability, data quality, data validity, data ethics (e.g. data bias, algorithmic bias, privacy) and potential unintended consequences is emphasised. An understanding of both qualitative and quantitative research methods and how these can be applied in a data science context will be developed through examining how data can be gathered and critically analysed, and how the validity of conclusions drawn using particular models can be assessed. Mechanisms for dissemination and communication of research outcomes will also be covered.
Learning Outcomes
On completion of this module the learner will/should be able to;
1.
Undertake a literature search and generate a literature review on a research topic.
2.
Formulate an appropriate research question or hypothesis.
3.
Design and critically apply an appropriate research strategy.
4.
Identify any ethical issues that need to be addressed, and ensure these are integrated into research plans/activities.
5.
Devise and/or utilise methods for data collection and analysis incorporating qualitative and quantitative approaches.
6.
Devise a strategy for evaluation and critical appraisal of research outcomes.
Teaching and Learning Strategies
Lectures introducing key topics, illustrative case studies, demonstrations of tools.
Weekly worksheets, small group discussions, use of ethical toolkits.
Module Assessment Strategies
Detailed research proposal/design as a foundation for thesis to include, for example:
- Aims and Objectives of research
- Literature Review
- Research Question and how it will be addressed.
- Research Strategy and Rationale
- Methods of Data Sourcing and Preparation
- Model selection
- Ethical issues and how they will be addressed
- Evaluation and deployment considerations.
Repeat Assessments
As above - resubmit.
Indicative Syllabus
Introduction to Research Methods and Philosophy (LO 1,2,3)
- Types of research: social, scientific.
- Theories, epistemologies, ontologies.
- Qualitative and quantitative approaches.
- Research issues in data science.
LO1 Undertake a literature search and generate a literature review on a research topic.
- Identifying and exploring issues
- Conducting a literature review: tools and techniques, searching, referencing
- Exploring the problem domain - defining the problem
LO2 Formulate an appropriate research question or hypothesis.
- Asking the right question: how to formulate research questions/hypotheses
- Formulating a research question/hypothesis.
LO3 Design and critically apply an appropriate research strategy.
- Data Science and Research strategy
- Quantitative research strategies : introduction to approach, concepts, indicators, measurement, methods for analysis; identifying and evaluating data sources, sample evaluation; data analysis; evaluation: validity, reliability; causality, replicability generalisability.
- Qualitative research strategies: introduction to approach, concepts, reliability, identifying data sources, theorising from patterns, testing models.
LO4 Identify any ethical issues that need to be addressed, and ensure these are integrated into research plans/activities.
- Identifying ethical implications, ethical principles and toolkits.
- Privacy and consent, vulnerable subjects, GDPR
- IT Sligo's Research Ethics Policy and Procedure.
L05: Devise and/or utilise methods for data collection and analysis incorporating qualitative and quantitative approaches.
- Data sourcing and Preparation: Identifying, evaluating and validating data sources, data preparation issues (cleansing, integration), legal and ethical concerns
- Model selection/construction/adaptation
LO6: Devise a strategy for evaluation and critical appraisal of research outcomes.
- Model Evaluation, Applicability, Deployment issues
- Data visualisation
Coursework & Assessment Breakdown
Coursework & Continuous Assessment
100 %
Coursework Assessment
| Title | Type | Form | Percent | Week | Learning Outcomes Assessed | |
|---|---|---|---|---|---|---|
| 1 | Research Proposal | Project | Essay | 100 % | Week 13 | 1,2,3,4,5,6 |
Full Time Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Lecture | Lecture Theatre | Research methods and background theory | 1 | Weekly | 1.00 |
| Workshop / Seminar | Flat Classroom | Tutorial/workshops on selected topics. | 2 | Weekly | 2.00 |
| Independent Learning | Not Specified | - self-directed learning | 4 | Weekly | 4.00 |
Total Full Time Average Weekly Learner Contact Time 3.00 Hours
Online Learning Mode Workload
| Type | Location | Description | Hours | Frequency | Avg Workload |
|---|---|---|---|---|---|
| Online Lecture | Online | Lecture and guidance for directed learning. | 1.5 | Weekly | 1.50 |
| Independent Learning | Not Specified | - self directed learning | 5.5 | Weekly | 5.50 |
Total Online Learning Average Weekly Learner Contact Time 1.50 Hours
Required & Recommended Book List
Recommended Reading
2017-12-22 Research Design
ISBN 1506386768 ISBN-13 9781506386768
2017-12-22 Research Design
ISBN 1506386768 ISBN-13 9781506386768
This best-selling text pioneered the comparison of qualitative, quantitative, and mixed methods research design. For all three approaches, John W. Creswell and new co-author J. David Creswell include a preliminary consideration of philosophical assumptions, key elements of the research process, a review of the literature, an assessment of the use of theory in research applications, and reflections about the importance of writing and ethics in scholarly inquiry. New to this Edition: Updated discussion on designing a proposal for a research project and on the steps in designing a research study Additional content on epistemological and ontological positioning in relation to the research question and chosen methodology and method Additional updates on the transformative worldview Expanded coverage on specific approaches such as case studies, participatory action research, and visual methods Additional information about qualitative and quantitative data analysis, social media, online qualitative methods, and mentoring and reflexivity in qualitative methods Incorporation of action research and program evaluation in mixed methods and coverage of the latest advances in the mixed methods field Additional information about causality and its relationship to statistics in quantitative methods Incorporation of writing discussion sections into each of the three methodologies An invaluable guide for students and researchers across the social and behavioural sciences.
Recommended Reading
2015-05-30 Innovations in Digital Research Methods Sage Publications Limited
ISBN 1446203093 ISBN-13 9781446203095
2015-05-30 Innovations in Digital Research Methods Sage Publications Limited
ISBN 1446203093 ISBN-13 9781446203095
Vast amounts of digital data are now generated daily by people as they go about their lives, yet social researchers are struggling to exploit it. At the same time, the challenges faced by society in the 21st century are growing ever more complex, and demands research that is bigger in scale, more collaborative and multi-disciplinary than ever before. This cutting-edge volume provides an accessible introduction to innovative digital social research tools and methods that harness this 'data deluge' and successfully tackle key research challenges. Contributions from leading international researchers cover topics such as: Qualitative, quantitative and mixed methods research Data management Social media and social network analysis Modeling and simulation Survey methods Visualizing social data Ethics and e-research The future of social research in the digital age This vibrant introduction to innovative digital research methods is essential reading for anyone conducting social research today.
Recommended Reading
2018-12-17 Data Science Morgan Kaufmann
ISBN 012814761X ISBN-13 9780128147610
2018-12-17 Data Science Morgan Kaufmann
ISBN 012814761X ISBN-13 9780128147610
Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Nave Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner
Recommended Reading
10/07/2019 A-Z of Digital Research Methods Taylor Francis
10/07/2019 A-Z of Digital Research Methods Taylor Francis
Module Resources
Non ISBN Literary Resources
Kelleher and Tierney (2018) Data Science, MIT essentials series(2018) ISBN: 978-0262535434
Journal Resources
Big Data Research, Elsevier
Data Mining and Knowledge Discovery, Springer
URL Resources
Sample Articles/Papers
- Cao L. Data Science:Challenges and Directions, Communications of the ACM, August 2017[URL: https://cacm.acm.org/magazines/2017/8/219605-data-science/abstract accessed 8-9-2019]
- The Qualitative Data Scientist [ https://towardsdatascience.com/the-qualitative-data-scientist-e0eb1fb1ceb9 accessed 8-9-2019]
- Mills, KA What are the threats and potentials of big data for qualitative research?, Qualitative Research, November 30, 2017 [https://doi.org/10.1177/1468794117743465 accessed 8-9-2019]