Overview

The Predictive Analytics Modeler career path prepares learners to learn the essential analytics models to collect and analyse data efficiently. This will require skills in predictive analytics models, such as data mining, data collection and integration, nodes, and statistical analysis. The Predictive Analytics Modeler will use tools for market research and data mining in order to predict problems and improve outcomes.

Learning Outcome

After completing this course, you should be able to:
  • The importance of analytics and how its transforming the world today
  • Understand how analytics provided a solution to industries using real case studies
  • Explain what is analytics, the various types of analytics, and how to apply it
  • Improve efficiency, sample records, and work with sequence data
  • Explain data transformations, and functions
  • Understand modeling, relationships, derive and reclassify fields
  • Integrate and collect data
  • Understand the principles of data mining
  • Use the user interface of modeler to create basic program streams
  • Read a statistics data file into modeler and define data characteristics
  • Review and explore data to look at data distributions and to identify data problems, including missing values
  • Use the automated data prep node to further prepare data for modeling
  • Use a partition node to create training and testing data subsets

Who should Attend?

  • New entrants to the industry who want to pick up a working knowledge on how to use predictive analytics for market research and data mining

Eligibility Criteria

Course attendees are expected to have:
  1. Academic Level of at least GCE O Level or equivalent
  2. English language proficiency of at least IELTS 5.0 or equivalent
  3. Basic Internet and web browser usage experience
  4. Basic analytics experience
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page


Information is accurate as of 10 June 2019