Overview

Participants will be equipped with practical skills to implement predictive modelling using various data analysis methods such as linear regression model, logistic regression models and decision trees. Participants will also acquire the skills related to the implementation of unsupervised learning algorithms such as k-means clustering and other analytical methods such as correlation analysis and association rules to derive insights from their data.

Learning Outcome

At the end of this course, participants will:
  • Acquire knowledge on data analytics and its impact on enterprises with several use cases
  • Gain a solid understanding about statistical and analytical methods that make up the backbone of data science
  • Acquire knowledge on data mining & predictive modelling techniques and implementation using R Script / Rattle Package
  • Gain complete understanding of big data landscape and how SQL and NoSQL databases play a key role in analytics
  • Gain complete understanding of the technicalities of MongoDB as well as the Data Manipulation Language (DML) of MongoDB

Who should Attend?

  • IT Executives/Managers
  • Risk Analyst/Management
  • Business Analyst
  • Data Analyst
  • Banking Executives/Managers
  • Software Engineers
  • System Engineers

Eligibility Criteria

Recommended to have some basic programming knowledge in any languages (preferred R).

This course is endorsed underCritical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer toProgramme Support under CITREP+ page

Information as accurate as of 20 May 2021