The Knowledge Academy

Data Mining Training - Hong Kong

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Course Information

Schedules
  • 11 Nov 2021 (Thu) - 12 Nov 2021 (Fri) 9:00 AM - 5:00 PM
  • 9 Dec 2021 (Thu) - 10 Dec 2021 (Fri) 9:00 AM - 5:00 PM
Registration period
10 Sep 2021 (Fri) - 8 Dec 2021 (Wed)
Price
HKD 18,495
Course Level
Study Mode
Duration
2 Day(s)
Language
English
Location
-
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Course Overview

Prerequisites

There are no formal prerequisites for attending this course. However, basic knowledge of the IT industry will be beneficial.

Audience

Anyone who is interested in learning the data mining can attend this course. This course is best-suited for IT managers aiming to improve data management and analysis techniques.

Data Mining Training​ Course Overview

Data mining is the method of detecting patterns in large data sets by making use of statistics, machine learning and database systems. It includes analysing large amounts of data and converting it into useful information. The insights gained from data mining can be used for fraud detection, marketing, scientific discovery, etc.

This Data Mining Training course will provide delegates with extensive knowledge on data mining. This course will cover the main concepts of data mining including data objects, data visualisation, measuring data similarity, and data preprocessing. Delegates will also learn about data transformation and data discretization. Data warehousing and online analytical processing will also be crucial concepts of this course including basic data warehousing concepts, data cube, and OLAP.

In addition, this 2-day training course will cover mining frequent patterns, associations, and correlations including pattern evaluation methods. Delegates will acquire knowledge on advanced pattern mining that comprises constraint-based frequent pattern mining, mining high-dimensional data and colossal patterns, and pattern exploration and application. By the end of this course, delegates will have gained comprehensive knowledge on classification methods, cluster analysis, and outlier detection.

  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor

 

 

 

What You’ll Learn

Data Mining Training​ Course Outline

Getting Started with Data Mining

  • What is Data Mining?
  • What Kinds of Data Can Be Mined?
  • Data Objects and Attribute Types
  • Data Visualisation
  • Measuring Data Similarity and Dissimilarity

Data Preprocessing

  • Data Cleaning and Data Integration
  • Data Reduction
  • Data Transformation and Data Discretization

Data Warehousing and Online Analytical Processing

  • Basic Concepts of Data Warehousing
  • Data Cube and OLAP
  • Design, Usage, and Implementation of Data Warehouse

Data Cube

  • Preliminary Concepts
  • Data Cube Computation Methods
  • Multidimensional Data Analysis in Cube Space

Mining Frequent Patterns, Associations, and Correlations

  • Frequent Itemset Mining Methods
  • Pattern Evaluation Methods

Advanced Pattern Mining

  • Pattern Mining in Multilevel and Multidimensional Space
  • Constraint-Based Frequent Pattern Mining
  • Mining High-Dimensional Data and Colossal Patterns
  • Mining Compressed or Approximate Patterns
  • Pattern Exploration and Application

Classification

  • What is Classification?
  • Decision Tree Induction
  • Bayes Classification Methods
  • Rule-Based Classification
  • Model Evaluation and Selection

Advanced Methods of Classification

  • Bayesian Belief Networks
  • Backpropagation
  • Classification Using Frequent Patterns
  • Lazy Learners
  • Genetic Algorithms, Rough Set Approach, and Fuzzy Set Approaches

Cluster Analysis

  • What is Cluster Analysis?
  • Partitioning and Hierarchical Methods
  • Density-Based and Grid-Based Methods

Advanced Cluster Analysis

  • Probabilistic Model-Based Clustering
  • Clustering High-Dimensional and Graph Data
  • Clustering with Constraints

Outlier Detection

  • Outlier Analysis
  • Outlier Detection Methods
  • Statistical and Proximity-Based Approaches
  • Clustering-Based and Classification-Based Approaches
  • Outlier Detection in High-Dimensional Data


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