Data Science -Foundations & Advanced: Data Mining and Predictive Analytics

26 - 29 Mar 2019 | Kuala Lumpur 25 - 28 June 2019 | Kuala Lumpur
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Course Objectives

This two days course prepare analyst to take the knowledge gained and apply it to their own respective data mining problems, solving them quickly and easily. The lessons learnt will be applicable to areas such as customer analytics, targeted marketing, social media analytics, fraud detection, predictive maintenance, resource management, etc. This course is suggested for analysts and data scientists.

What Will You Learn?

  • Deploy analytical predictive models
  • Utilize more complex functionality of RapidMiner Studio
  • Apply more sophisticated analytical approaches

Target Audience

This course is suggested for analysts and data scientists.

Training Methodology

Hands-on exercise, lecture, group discussion, and case study.

Training Outline

Advanced

Overview
  • Business case
  • Intro course review
  • Loading new data
EDA: Exploratory Data Analysis
  •  Multiple sources
  • Joins & Set Theory
  • Understanding new attributes
Data Preparation
  • Advanced Data ETL (Extract, Transform, and Load)
  • Aggregation & Multi-level aggregation
  • Pivot & De-Pivot
  • Calculated values
  • Regular Expressions
  • Changing value types
  • Feature Generation and Feature Engineering
  • Loops
  • Macros
Predictive Model’s Algorithms
  • Support Vector Machines
  • K-Means Clustering
  • Neural Networks
  • Logistic Regression
Model Construction and Evaluation
  • Advanced performance criteria
  • ROC plots
  • Comparison between models
  • Sampling
  • Weighting
  • Feature Selection: Forward Selection
  • Feature Selection: Backward Elimination
  • Validation of preprocessing and preprocessing models
  • Optimization & Logging results
Additional Workshops
  • Principal Components Analysis
  • Logistic Regression
  • Performance (Cost) Model Optimization

Prerequisite

RapidMiner & DataScience : Foundations