Data Mining

Data is available enormously across all industry, and this data serves as the source of information. From this data source, we need to analyze and extract information by converting this available into a useful form.  Obtaining information from the data involves processes such as Data Mining, Data Cleaning, Data Integration, Data Transformation, Data Presentation and Pattern Evaluation.

Data Mining serves as the natural evolution of Information Technology. The Process of extracting information or pattern within massive data sets by using the methods of Artificial Intelligence and Statistics. The useful information received by data mining is useful for many applications such as Market Analysis, Production Control, Fraud Detection, Customer Retention, and Science Exploration.

Data Mining techniques are widely used for both structured and unstructured data. The process of Data Mining helps to confirm structured data to a defined schema and unstructured information into a natural language text.

Data Scientists or analysts use data mining procedures to drill down into this transactional data to determine pricing, product positioning, impact on sales, customer preferences and satisfaction and corporate profits. With data mining, the company can use records of data or patterns or information available to develop and promote products to appeal to specific customer segments.

Business or Data analysts make use of data mining model(mathematical algorithms and techniques, relationship or set of examples)on different data situations to get an answer from unknown data sets by the application of the model. Analysts Using, data Mining technology can automate prediction of trends and behaviors of information in an extensive database. Moreover, data Mining tools sweep through databases to discover previously unknown hidden facts in historical data.

Data Mining uses analytical techniques such as mathematical algorithms, to increase the availability of data, less cost storage and processing power. Business experts use graphical interface tools such as artificial neural networks, decision trees, Rule induction, Genetic algorithms and nearest neighbor for business decision making.

Why Data Mining?

The procedure of mining knowledge from data and discovering patterns is the heart of successful visualization of data for better business decisions. Data mining by using predictive models and algorithms for Automated Merchandizing, Dynamic Pricing, Product Search, Smart Personalization, Proteomics, Genetics, Social Network Analysis, Technology-based Solutions for Business problems.

It also helps data analyst and companies to spot sales trends, develop smarter marketing campaigns and accurately predict customer loyalty.

Why Should I learn Data Mining?

Data Mining system is applicable in diverse areas such as Telecommunication, Information Technology, Biological Data Analysis, Retail Industry, Financial Data Analysis, Intrusion Detection and other Scientific Applications.  Data Mining is an inventive method used to extract data patterns based on databases mined, knowledge mined, techniques used and applications adapted.  Analysts learn to classify and visualize data mining by data model and to have a transactional, relational,object-relational, object-oriented, data warehouse mining system.

What will I learn in Data Mining?

• Learn to integrate mining information from various heterogeneous data sources and global information systems
• Understand Mining Methodology and user interaction, Performance Issues, and Diverse Data Types Issues
• Learn to use Statistical methods and algorithms to extract knowledgeable information from large datasets
• Learn to present and visualize data mining results
• Handling noisy or incomplete datasets
• Complete understanding of Pattern evaluation to visualize data importance and its future into business intelligence
• Determine the efficiency and scalability of data mining algorithms by parallel, distributed and incremental  mining algorithms
• Understand handling of relational and complex types of data

Requirements:

• Understanding of basic database concepts
• Basic knowledge of Data Warehousing concepts

Audience:

• Data analysts, Business analysts and IT managers looking to improve data management, processes, and tools of data mining and analysis techniques
• Computer science graduates interested in basic-to-advanced concepts related to data mining

What is covered in Data Mining Tutorial?

Course Curriculum:

Lesson 1: Introduction to Data Mining 

This chapter explains you the fundamentals of  Data Mining wich is the process of extracting information from massive data sets.This lesson provides an overview of basic terminologies involved in data mining.

Class 1:

• Data Mining Overview
• Data Mining Terminologies
• Data Mining Tasks
• Data Mining Applications

Lesson 2: Data Mining Systems

This session explains the different criteria based on which Data Mining is classified. Apart from the list provided below, Data mining system can be categorized based on the kind of databases mined, knowledge mined, techniques utilized, and applications adapted.

Class 1:

• Database Technology
• Statistics
• Machine Learning
• Information Science
• Visualization
• Other Disciplines

Lesson 3: Data Mining Tasks

Data Mining Tasks make use of two methods namely Prediction Methods which uses some variables to predict unknown or future values of other variables and Description Methods which finds human-interpretable patterns to describe the data.

Class 1:

• Prediction Methods
  • Classification
  • Regression
  • Deviation Detection
  • Description Methods
    • Clustering  
    • Association Rule Discovery 
    • Sequential Pattern Discovery  

Lesson 4: Data Mining Classification

In this class, you learn to use the Bayesian classification which is based on Bayes' Theorem. Bayesian classifiers are the statistical classifiers. Bayesian classifiers can predict class membership probabilities such as the probability that a given tuple belongs to a particular class.

Class 1:

• Baye’s Theorem
• Baye’s Belief Network
  • Directed acyclic graph
  • Conditional probability tables
  • Rules based classification
  • Genetic Algorithms

Lesson 5: Clustering

A cluster is a group of objects which belong to the same class. Similar objects are grouped in one cluster and objects that are not similar are grouped in another cluster. Clustering is the process of making a group of abstract objects into classes of similar objects, and this chapter allows to follow the clustering methods like Partitioning Method, Hierarchical Method, Density-based Method, Grid-Based Method, Model-Based Method and Constraint-based Method

Class 1:

• Clustering Overview
• Clustering Analysis
• Clustering Methods

Lesson 6: Text Mining and Web Mining

This experience helps you learn all the procedures in Text Mining and Web Mining Web content. The World Wide Web contains significant amounts of information that provides a rich source for data mining. The Text Mining includes Text databases with a vast collection of documents, and the Text information is collected from several sources such as news articles, books, digital libraries, e-mail messages, and web pages  

Class 1:

• Text Mining Methods
• Text Mining Procedures
• Web Mining Methods
• Web Mining Procedures

Lesson 7: Data Mining Challenges

This chapter handles the challenges in Data Mining. Efficient data mining in large databases carries various requirements and significant challenges to researchers and developers. Some of the issues include data mining methodology, user interaction, performance and scalability, and the processing of a large variety of data types.

Class 1:

• Scalability
• Dimensionality
• Complex and Heterogeneous Data
• Data Quality
• Data Ownership and Distribution
• Privacy Preservation
• Streaming Data