COMP07189 2020 Applications in Big Data

General Details

Full Title
Applications in Big Data
Transcript Title
Applications in Big Data
N/A %
Subject Area
COMP - Computing
HEAL - Health & Nutritional Sciences
07 - NFQ Level 7
05 - 05 Credits
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Padraig McGourty, Thomas Smyth, Richeal Burns, Dr. Sasirekha Palaniswamy Lecturer
Programme Membership
SG_SINFO_B07 202000 Bachelor of Science in Health and Medical Information Science SG_SDATA_E07 202000 Certificate in Health Data Analytics

This module aims to provide a theoretical and practical introduction to Big Data, its analysis and relevant challenges associated with Big Data. Big data, open data and various data infrastructures including the rapidly changing data landscape and data revolution will be discussed. An in depth analysis of the implications of the Big Data and its revolution in various areas (applications of Big Data) will be explored. Technological advancements in storing, accessing, sharing and the cost models will be examined and reviewed. It will also provide an overview of the technical aspects of Big Data analysis along with practical exercises (No prior knowledge of programming would be required as applications for data analysis or scripts can be provided where required).

Learning Outcomes

On completion of this module the learner will/should be able to;


To understand the fundamentals of big data analysis, the strengths and limitations of big data research with real-world examples.


To develop competence in handling and processing of Big data, Identify the opportunities and challenges of incorporating big data analytics to improve the development and testing of precision medicine eg. genomic data, electronic medical records.


To understand the methodological challenges and problems and gain understanding of the principles of reproducible research (data sharing/version controls etc - e.g GIT)


To apply Health Information Technology and Machine learning techniques to implement relevant workflow for data analysis. Undertake Data Analytics, Data Wrangling and Exploratory Data Analysis (e.g. R, Python)

Teaching and Learning Strategies

Teaching and learning for this module will be carried out through a combination of online lectures, computer based critical appraisal and online practical's. Blended learning approaches will be adapted consistent with digital learning paradigms. 

Online delivery of 1 lecture per week with self directed learning. Guidance provided on relevant areas for self directed learning.

Online delivery of 2 hour workshop weekly, where students will be directed to complete interactive type activities to enhance their study skills and knowledge.

Question and answer sessions provided in the live classroom.

A variety of methods of instruction such as discussion, group work, interactive exercises, use of online resources and/or use of audio/visual material will be provided. Core skills will be embedded into all modules to ensure all students have an equal opportunity to succeed. This may include academic writing, oral presentations, reading techniques or research abilities. Accessible materials will be provided to students, including slides, documents, audio/visual material and textbooks enabling students slow down speed up recordings etc in accordance with universal distance learning.

All module content will be based on the principles of UDL to ensure equitable access to content and learning.

Module Assessment Strategies

This module will be assessed by both a final exam (50%) and continuous assessment (50%)

Repeat Assessments

Repeat examination will follow a similar format as applicable

Indicative Syllabus

  • Big Data- Electronic medical records, Electronic Health Records (EHRs), National registers, Health and Social care records, genomic data, social media data etc

  • Use of big data in research/drug development etc. and strategies to improve the health of the population, potential for Health IT to improve the health of the population (eg epidemiology - monitoring outbreaks)

  • Big data examples in healthcare - Use of EHRs, real time alerting, using health data for strategic planning, pattern analysis (spot trends and patterns over time - identify potential bottlenecks, general assessment of the situation), predictive analytics, practice telemedicine, prevent unnecessary hospital visits, integrate medical imaging and clinical data for a broader diagnosis. 

  • Public Health Programs - Better-informed decisions that will improve the overall operations performance with the goal of treating patients better and having the right staffing resources. Epidemiology, Surveillance (public health / clinical trials/treatment outcomes)

  • Integrated healthcare delivery systems - telemedicine, m-medicine, access to multiple systems/sources to provide an integrated health record (e-Health record).

  • Healthcare analytics - Health System and Service research and the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life. 

  • Clinical research - Data-driven findings to predict and solve a problem eg. genomic data to identify patterns in rare diseases, cancer

  • Use of big data in healthcare - improved patients experience, improved quality of treatment and satisfaction, improvement of the overall health of the population, general cost reduction.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
50 %
End of Semester / Year Formal Exam
50 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Applications in Big Data - Assessment Continuous Assessment Assessment 50 % OnGoing 1,3

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Applications in Health Data - Exam Final Exam Closed Book Exam 50 % End of Semester 2,4

Online Learning Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Online Applications in Big Data Lectures 1 Weekly 1.00
Problem Based Learning Online Applications in Big Data - PBL 2 Weekly 2.00
Independent Learning Not Specified Independent study 4 Weekly 4.00
Total Online Learning Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Required Reading
2014-09-03 Big Data CRC Press
ISBN 9781466592384 ISBN-13 1466592389

Big Data: A Business and Legal Guide supplies a clear understanding of the interrelationships between Big Data, the new business insights it reveals, and the laws, regulations, and contracting practices that impact the use of the insights and the data. Providing business executives and lawyers (in-house and in private practice) with an accessible primer on Big Data and its business implications, this book will enable readers to quickly grasp the key issues and effectively implement the right solutions to collecting, licensing, handling, and using Big Data. The book brings together subject matter experts who examine a different area of law in each chapter and explain how these laws can affect the way your business or organization can use Big Data. These experts also supply recommendations as to the steps your organization can take to maximize Big Data opportunities without increasing risk and liability to your organization. Provides a new way of thinking about Big Data that will help readers address emerging issues Supplies real-world advice and practical ways to handle the issues Uses examples pulled from the news and cases to illustrate points Includes a non-technical Big Data primer that discusses the characteristics of Big Data and distinguishes it from traditional database models Taking a cross-disciplinary approach, the book will help executives, managers, and counsel better understand the interrelationships between Big Data, decisions based on Big Data, and the laws, regulations, and contracting practices that impact its use. After reading this book, you will be able to think more broadly about the best way to harness Big Data in your business and establish procedures to ensure that legal considerations are part of the decision.

Required Reading
2017-08-14 Internet of Things and Big Data Analytics Toward Next-Generation Intelligence Springer
ISBN 9783319604350 ISBN-13 331960435X

This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking peoples imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.

Required Reading
2018-12-21 Fundamentals of Clinical Data Science Springer
ISBN 9783319997131 ISBN-13 3319997130

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The books promise is no math, no codeand will explain the topics in a style that is optimized for a healthcare audience.

Required Reading
2017-10-20 Healthcare and Big Data Management Springer
ISBN 9789811060410 ISBN-13 981106041X

The book addresses the interplay of healthcare and big data management. Thanks to major advances in big data technologies and precision medicine, healthcare is now becoming the new frontier for both scientific research and economic development. This volume covers a range of aspects, including: big data management for healthcare; physiological and gut microbiota data collection and analysis; big data standardization and ontology; and personal data privacy and systems level modeling in the healthcare context. The book offers a valuable resource for biomedical informaticians, clinicians, health practitioners and researchers alike.

Required Reading
2017-09-18 Big Data in Healthcare Springer
ISBN 9783319629902 ISBN-13 3319629905

This book reviews a number of issues including: Why data generated from POC machines are considered as Big Data. What are the challenges in storing, managing, extracting knowledge from data from POC devices? Why is it inefficient to use traditional data analysis with big data? What are the solutions for the mentioned issues and challenges? What type of analytics skills are required in health care? What big data technologies and tools can be used efficiently with data generated from POC devices? This book shows how it is feasible to store vast numbers of anonymous data and ask highly specific questions that can be performed in real-time to give precise and meaningful evidence to guide public health policy.

Required Reading
2018-07-23 Applications of Big Data Analytics Springer
ISBN 9783319764726 ISBN-13 3319764721

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research Almaden, San Jose, CA, USA.

Module Resources

Other Resources