Data Mining Training in Chennai | Data Mining Course in Chennai

30 SEATS

COURSE INSTRUCTOR

Anushya

Data Mining

5+ Years of Experience

BASIC INFORMATION

Lessons : 12
Length : 6 Months
Level : Basic
Category : Software Training

Started : 04-04-2019
Shift : 02
Class : 120

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Data Mining Training and Course Description

We are the best providers of Data Mining Training in Chennai with excellent placements. Our training program is very much mixed both practical and interview point of questions. It will helpful for the companies and students who are willing to learn Data Mining with live projects. We already build a perfect solution of Data Mining Placements exclusively for our students. We always schedule a demo class for each course for the new inquired students. It is completely FREE. It takes your 45mins of time. Once you attended the demo session you got the idea about what you are going to learn in Data Mining with us. Simply, we are the best Data Mining Training Institute in Chennai where you can learn the technology to core.

Our Data Mining Syllabus is crafted by many MNC HR’s and Experts which already satisfied many of the corporate. We are also having a separate dynamic division for client projects. By placement, course syllabus and practicals we are the BEST DATA Mining TRAINING INSTITUTE IN CHENNAI.

Data Mining Training and Course Syllabus

Module 1: Introduction to Data Mining

What is data mining?
Related technologies - Machine Learning, DBMS, OLAP, Statistics
Data Mining Goals
Stages of the Data Mining Process
Data Mining Techniques
Knowledge Representation Methods
Applications
Example: weather data

Module 2: Data Warehouse and OLAP

Data Warehouse and DBMS
Multidimensional data model
OLAP operations
Example: loan data set

Module 3: Data preprocessing

Data cleaning
Data transformation
Data reduction
Discretization and generating concept hierarchies
Installing Weka 3 Data Mining System
Experiments with Weka - filters, discretization

Module 4: Data mining knowledge representation

Task relevant data
Background knowledge
Interestingness measures
Representing input data and output knowledge
Visualization techniques
Experiments with Weka - visualization

Module 5: Attribute-oriented analysis

Attribute generalization
Attribute relevance
Class comparison
Statistical measures
Experiments with Weka - using filters and statistics

Module 6: Data mining algorithms: Association rules

Motivation and terminology
Example: mining weather data
Basic idea: item sets
Generating item sets and rules efficiently
Correlation analysis
Experiments with Weka - mining association rules

Module 7: Data mining algorithms: Classification

Basic learning/mining tasks
Inferring rudimentary rules: 1R algorithm
Decision trees
Covering rules
Experiments with Weka - decision trees, rules

Module 8: Data mining algorithms: Prediction

The prediction task
Statistical (Bayesian) classification
Bayesian networks
Instance-based methods (nearest neighbor)
Linear models
Experiments with Weka - Prediction

Module 9: Evaluating what's been learned

Basic issues
Training and testing
Estimating classifier accuracy (holdout, cross-validation, leave-one-out)
Combining multiple models (bagging, boosting, stacking)
Minimum Description Length Principle (MLD)
Experiments with Weka - training and testing

Module 10: Mining real data

Preprocessing data from a real medical domain (310 patients with Hepatitis C).
Applying various data mining techniques to create a comprehensive and accurate model of the data.

Module 11: Clustering

Basic issues in clustering
First conceptual clustering system: Cluster/2
Partitioning methods: k-means, expectation maximization (EM)
Hierarchical methods: distance-based agglomerative and divisible clustering
Conceptual clustering: Cobweb
Experiments with Weka - k-means, EM, Cobweb

Module 12: Advanced techniques, Data Mining software and applications

Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
Bayesian approach to classifying text
Web mining: classifying web pages, extracting knowledge from the web
Data Mining software and applications

COURSE INSTRUCTOR

Anushya

BASIC INFORMATION

Data Mining Training and Course Description

Data Mining Training and Course Syllabus

Module 1: Introduction to Data Mining

Module 2: Data Warehouse and OLAP

Module 3: Data preprocessing

Module 4: Data mining knowledge representation

Module 5: Attribute-oriented analysis

Module 6: Data mining algorithms: Association rules

Module 7: Data mining algorithms: Classification

Module 8: Data mining algorithms: Prediction

Module 9: Evaluating what's been learned

Module 10: Mining real data

Module 11: Clustering

Module 12: Advanced techniques, Data Mining software and applications

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Data Mining Training and Course in Chennai

COURSE INSTRUCTOR

Anushya

BASIC INFORMATION

Data Mining Training and Course Description

Data Mining Training and Course Syllabus

Module 1: Introduction to Data Mining

Module 2: Data Warehouse and OLAP

Module 3: Data preprocessing

Module 4: Data mining knowledge representation

Module 5: Attribute-oriented analysis

Module 6: Data mining algorithms: Association rules

Module 7: Data mining algorithms: Classification

Module 8: Data mining algorithms: Prediction

Module 9: Evaluating what's been learned

Module 10: Mining real data

Module 11: Clustering

Module 12: Advanced techniques, Data Mining software and applications

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