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Data Science: Home

Resources & Guides on Data Science

Welcome to the guide for Data Science. Here you'll find resources and information that will assist you with your research. Click on the tabs on this page to uncover detailed lists of information sources to help answer your questions.  Each is comprised of the best sources for finding articles and facts for topics of particular interest to faculty, students and other researchers. The University has a programme in Data Science based at the Faculty of Computer Science and Technology.

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What is Data Science?

Data science is the combination of informatics, mathematics, statistics, programming, problem-solving, capturing data in ingenious ways and the ability to look at things differently and the activity of cleansing, preparing and aligning the data. 

What is Big Data?

Big Data refers to huge volumes of data that cannot be processed effectively with traditional applications. The processing of Big Data begins with the raw data that is not aggregated and is most often impossible to store in the memory of a single computer. Big Data can be used to analyze insights which can lead to better decisions and strategic business moves.

What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about the information. It involves applying an algorithmic or mechanical process to derive insights. For instance, running through a number of data sets to look for meaningful correlations between each other. This technique is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.  

Best practices of data management?‚Äč

Good Data Management Practices :

Making data discoverable increases the impact, assist with the verification of results, more importantly allows others to reuse the data in new ways creating new research endeavors.

Samples of data sharing in Malaysia :

However, there are instances where data cannot be shared or may first need to be de-identified:
  • legal issues (IP & privacy) & contractual restrictions;
  • ethics & sensitivity issues;
  • confidentiality.