Msc in Big Data Analytics

DESCRIPTION OF FOUNDATION MODULE S IN MASTER OF SCIENCE IN BIG DATA ANALYTICS

MSDA 9111 Advanced Research Methodology              10 Credits

This Module is designed to provide students with the necessary skills and knowledge to determine the information necessary to address an identified research problem (basic or applied) and, by using this understanding, develop and use an actionable research proposal. In this process, the students will gain an understanding of relevant approaches and elements of undertaking a research enquiry specifically to provide insights in order for them to solve a relevant problem. They will develop critical core competencies and skills required to carry out such an enquiry. These competencies and skills include: defining research questions; setting appropriate research objectives; study design that incorporates research objectives and budgetary constraints; secondary and primary data collection and instruments; sampling and analysis methods; and effective reporting of results. It also aims to provide a basis for informed judgments about research methods and evidence those members of the research-led profession need to make.

MSDA 9112 Philosophy of Christian Ethics       10 Credits

This Module surveys Christian philosophy and beliefs in accordance with the Holy Bible. It covers the holistic philosophy of education which encompasses the development of physical, spiritual, social, and intellectual aspects, the nature of man, the law of God, and Christian life in terms of marriage and family issues, such as marriage, family planning practices, drug abuse, and polygamy, among others. In addition, the concept of Business philosophies and values was passed down from the early management thinkers: Tuz Sun, Machiavellian, Cato the Elder, Code of Hammurabi, Peter Drucker, and some of the 20th century thinkers. The aforementioned are infused with the philosophies of Kant, Plato, Aristotle, Socrates, Jeremy, Thomas Aquinas, and Gandhi etc.

MSDA 9113 R. Programing for Data Science    10 Credits

The aim of this module is to provide the students with an opportunity to learn and explore the powerful programming tools and facilities of R in the world of data science. In fact, the module allows the learner to acquire the skill of using the statistical software package Upon the completion of the module, the learner would have been exposed to enough study materials and practical lessons so that to become competent in the field of big data science. The content of the module will appeal to the learner who wants to develop competent skills in R programming with the intent of statistical data analysis in many fields.

MSDA 9114 Python for Data Science             3 Credits

The aim of this module is to equip students with a full package of problem solving, to give students the opportunity to learn how to formulate problems, to think creatively and to express or to communicate the solution clearly and accurately. In fact, Python is the language for data science, in this Module learners will be exposed to the most important libraries such as Numpy, Pandas, matplotlib, Scikit-learn) that will enable them not only to deal with data analytics problem but also to do data science using python. Through the practical programming skills, learners will be able to formulate problems, data exploration, modelling, model evaluation and communication of the results.

MSDA 9115 Machine Learning                                4 Credits

Machine learning is the science of getting computers to act without being explicitly programmed. This Module provides a broad introduction to machine learning and statistical pattern recognition. The Module begins with the mathematical foundations needed for machine learning and progresses to supervised (linear models, kernel methods, decision trees, neural networks) and unsupervised (clustering, dimensionality reduction) approaches, as well as the theoretical foundations of machine learning (learning theory, optimization). In this Module, students will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work. Students not only learn the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. The Module will also discuss recent applications of machine learning, such as, data mining, autonomous navigation, speech recognition, and text and web data processing. The Module will also draw from numerous case studies and applications, so students also learn how to apply learning algorithms to building gesture-based and multimodal interfaces, text and speech understanding (web search, anti-spam), smart robots, medical informatics, audio, database mining and other areas.

Core Module / Modules