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Data Mining And Predictive Analytics - Using Current Data To Predict Future Trends

Dates:  21 November (8.30am - 5.00pm) 
Venue: Securities Commission Malaysia
Accreditation: SIDC CPE-approved: 10 CPE points


Data mining is a process and technique based on algorithms to extract and analyse specific information (text) and data (statistical) to automatically determine relationships and patterns from the information and data gathered.
 
Predictive analytics uses data patterns to predict future trends based on historical and current information. This includes customer behaviour, sales trends etc. These digital tools can produce invaluable business insights and could potentially increase competitive advantage, forecast emerging trends and identify new business opportunities very quickly.

  • Programme Delivery

    Programme Objective

    This programme examines the business potential and significance of data mining and predictive analysis. It explains how business intelligence and analytics can be used successfully in business organisations.

    Learning Outcomes

    Upon completion of this programme, participants will be able to:
    • Identify various applications of analytics and the analytics cycle
    • Examine the needs and methods of data preparation
    • Modify data with Base R
    • Utilise R predictive analytic models

    Methodology

    Interactive presentations, Discussions and Question-and-Answer (Q&A) sessions

    Competencies

    Functional (Technical Skills) – Digital Technology Application
  • Programme Outline

    8.30 am Registration
    9.00 am Introduction to Predictive Analytics
    • What is analytics and why is it so important?
    • Applications of analytics
    • Different kinds of analytics
    • Various analytics tools
    • Analytics project methodology
    • Business Analytics vs. Business Analysis
     
    10.30 am Coffee Break
    10.15am Data preparation
    • Needs & methods of data preparation
    • Handling missing values
    • Outlier treatment
    • Transforming variables
    • Derived variables
    • Modifying/Mutating data with Base R
     
    1.00 pm Lunch Break
    2.00 pm R Predictive Analytic Models
    • Correlation and linear regression
    • Logistic regression
    • Segmentation for marketing analytics
    • Time series forecasting
    • Decision trees
     
    3.30 pm Coffee Break
    3.45 pm R Predictive Analytic Models (cont.)
    • Segmentation for marketing analytics
    • Time series forecasting
    • Decision Trees
     
    5.00 pm End of Programme
  • Programme Fees

    Normal Price
    RM 950