Data Mining Training in Kingston upon Hull

Data Mining Training​ Course Outline

Day 1: Foundations and Data Preparation 
Module 1: Introduction to Data Mining 

• What is Data Mining?
• Importance and Applications of Data Mining
• Types of Data (Structured, Semi-structured, Unstructured)
• Data Objects and Attribute Types (Nominal, Ordinal, Numeric)
• Measuring Data Similarity and Dissimilarity
• Introduction to Data Visualisation Techniques

Module 2: Data Preprocessing Essentials 

• Overview of Data Preprocessing
• Data Integration and Aggregation Techniques
• Data Cleaning Strategies (Missing Values, Noisy Data)
• Data Reduction Techniques (Sampling, PCA, Feature Selection)
• Data Transformation (Normalisation, Scaling, Encoding Categorical Data)
• Data Discretisation Methods (Binning, Histogram Analysis)

Module 3: Data Warehousing and Online Analytical Processing (OLAP) 

• Basic Concepts of Data Warehousing
• Introduction to Data Cubes and OLAP Operations
• Multidimensional Data Models (Star Schema, Snowflake Schema)
• Data Warehouse Design and Architecture
• Implementing and Managing Data Warehouses
• Practical Uses and Applications of Data Warehousing and OLAP

Module 4: Mining Frequent Patterns and Associations 

• Concept of Frequent Pattern Mining
• Frequent Itemset Mining Algorithms (Apriori Algorithm, FP-Growth)
• Association Rule Generation and Interpretation
• Evaluation of Patterns (Support, Confidence, Lift, Conviction)
• Real-world Application Scenarios and Examples (Market Basket Analysis)

Day 2: Advanced Analytics & Real-world Applications
Module 5: Classification Techniques and Algorithms

• Introduction and Purpose of Classification
• Decision Tree Induction (CART, ID3, C4.5)
• Bayesian Classification (Naïve Bayes Classifier)
• Rule-Based Classification Methods
• Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1-Score
• Practical examples using real-world datasets

Module 6: Advanced Classification Methods 

• Introduction to Neural Networks and Backpropagation (brief overview)
• Frequent Pattern-based Classification Concepts
• Lazy Learners (k-Nearest Neighbours - kNN)
• Overview: Genetic Algorithms, Rough Set Approach, Fuzzy Logic (brief, conceptual coverage)
• Strengths and Weaknesses of Advanced Classification Techniques

Module 7: Cluster Analysis Methods 

• Understanding Cluster Analysis and Applications
• Partitioning Methods (k-Means and k-Medoids Clustering)
• Hierarchical Clustering (Agglomerative, Divisive)
• Density-based Clustering Methods (DBSCAN)
• Grid-based Clustering Methods (Conceptual overview)
• Evaluating and Visualising Cluster Results

Module 8: Outlier Detection & Advanced Clustering Techniques 

• Understanding Outliers and Importance of Detection
• Statistical Approaches to Outlier Detection
• Proximity-based Outlier Detection (Distance-based, Density-based)
• Clustering-based and Classification-based Outlier Detection
• Overview of Advanced Clustering: Probabilistic Model-Based Clustering (Gaussian Mixtures)
• High-dimensional Data Clustering and Challenges (brief overview)

Who should attend this Data Mining Training?

The Data Mining Course is tailored to impart knowledge on the process of extracting patterns, correlations, and information from large amounts of data stored in databases, data warehouses, or other information repositories. This course can be beneficial for various professionals aiming to derive insights from data, including:

• Data Scientists
• Business Analysts
• Database Administrators
• Marketing Analysts
• Researchers
• Statisticians
• Machine Learning Engineers

Prerequisites of the Data Mining Training

There are no formal prerequisites required for attending this Data Mining Course. Although, a prior understanding of IT concepts can be helpful.

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 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 discretisation. 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 offered by The Knowledge Academy 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.

Course Objectives

• To comprehend Data Mining concepts and its practical applications
• To acquire data preprocessing skills for effective analysis
• To grasp data transformation and discretisation techniques
• To understand data warehousing and Online Analytical Processing (OLAP)
• To become proficient in frequent pattern mining and associations
• To develop expertise in advanced pattern mining, classification, clustering, and outlier detection

Upon completing this Data Mining Training Course, delegates will gain advanced skills in extracting valuable insights from large datasets, enhancing their ability to make informed business decisions. Additionally, the course equips them with practical knowledge of cutting-edge data mining techniques.

What’s included in this Data Mining Training?

• World-Class Training Sessions from Experienced Instructors
• Data Mining Certificate
• Digital Delegate Pack