- WELCOME MESSAGE
Welcome to the 3rd Series of Computer Science Courses.
My name is Professor Francisca Oladipo .....................
- STUDY UNIT 1: STATISTICAL THINKING
Statistical thinking can align one’s thoughts with the fundamental principle of statistics to make better decisions under uncertainty. In other words, understanding statistics is important for anyone that wants to make a good decision since it is applicable in every field of human activity.
With the understanding of basic statistical methods, you will know when to apply the right tool to a given problem and think statistically. This course will help you understand those statistical concepts and apply them to solve a life problem.
- STUDY UNIT 2: MACHINE LEARNING
Over the past years, we all wondered whether a computer might be made to learn and improve with experience - the impact would be dramatic. Imagine a world where a computer could be made to learn about the treatments that are most effective for new diseases from the medical records or a piece of knowledge about a client that can default a loan when given. The study unit focus on the development of models that can learn from data for analysts to derive useful information.
- STUDY UNIT 3: TRAINING AND TESTING MACHINE LEARNING MODELS
Scikit-learn is a library in Python that provides much supervised learning and unsupervised algorithms. It is built upon some of the packages you already familiar with, like NumPy, Pandas, and Matplotlib. With Scikit-learn module, you can train different machine learning models such as regression and classification and check their performance using any of the metrics discussed in unit 2.
- STUDY UNIT 4 - INTRODUCTION TO REGULATORY FRAMEWORK & FAIR DATA
The emergence of the Internet as a global telecommunications network has had a huge impact on how we view and apply data protection and regulations. Before the massive expansion of the Internet, data was a minority interest that did not generate significant global interest. However, over the past decades, the use of and processes for data evolved significantly — both in terms of technology and use cases. Data is now considered the raw material for digital transformation. Thus, there is a need for a form of regulation to avoid chaos and misuse.
This Study Unit will provide you with an understanding of what a regulatory framework is and what it is used for. You will learn about general data protection principles including your country's data regulations. Likewise, you will get to know why we need FAIR data policies and its benefits to your country. Finally, the basics of a FAIR policies will be explored.
- STUDY UNIT 5 - FAIR DATA MANAGEMENT
This unit focus on FAIR Data Management and its core principles. The requirements for a good data management as well as the platform for creating FAIR data is covered.
You will learn what kind of questions you need to refer to make a good Data Management Plan and which tools you might use for creating a FAIR Data Management Plan? Along with that, you will have the practice of creating a FAIR Data Management plan yourself.
- STUDY UNIT 6 - SEMANTIC DATA
This Study Unit covers the basic concepts of semantic web, linked data, the semantic web stack and technologies like SKOS, RDF, OWL and SPARQL. It also explains sematic modelling and compare it with other data models.
Other topics covered includes how to use eCRF and CEDAR to create and explore metadata and how to use them as FAIR tool.
- STUDY UNIT 7 - FAIR DATA POINT (FDP) INSTALLATION
This Study Unit will show you how to deploy FDP locally through designing a Semantic Data Model and publishing it to FDP. The objective of this module is to illustrate how a non - FAIR can be assigned machine-readable metadata to enable them to be discoverable by individuals and machines.
- STUDY UNIT 8 - FAIR DATA FOR HEALTH
Data-driven technologies are changing business, our daily lives, and the way we conduct research more than ever. In recent years, more and more data have been generated in the healthcare ecosystem. The data contain potential knowledge to transform health care delivery and life sciences. Advanced analytics could potentially power the data collected from numerous sources to improve prevention, diagnosis and treatment of diseases, as well as supporting individuals and societies to maintain their health and well-being. The era of exponential growth of data has also witnessed the increase of risk involved in sharing them
This Study Unit teach the importance of FAIR Data Principles in Healthcare research. How FAIR Data Principles can facilitate knowledge discovery from health data. How linked health data drives research, better use and learning from data, and further contributions to patient care.