Data Mining / Image Mining / Cloud Computing

Data Mining/ Image Mining / Cloud Computing

Data mining is the process of mining patterns from big data sets by connecting approaches from statistics and artificial intelligence with database management. It is an interdisciplinary field of computer science. Data mining specialization courses edify data mining techniques for both structured and unstructured data. It includes the special types of courses including pattern discovery, text mining and analytics and data visualization. They are many course topics covered clustering, pattern discovery, text mining and analytics. This course extremely becomes popular and increasing importance among IT manager and data analyst to enhance their data management and analysis techniques. Particularly, professionals are doing the masters and doctorate in this interdisciplinary field in data mining program would accomplish professionally successful. Below list out the Table.1 some of the current Masters of Science data mining modules.

Table.1 Masters of Science in data mining program curriculum include

CORE COURSES (27 CREDITS)

ELECTIVE COURSES (6 CREDITS)

Introduction to Data Mining
Multivariate Analysis for Data Mining
Clustering and Affinity Analysis
Data Mining for Genomics and Proteomics
Predictive Analytics
Text Mining
Thesis

Applied Categorical Data Analysis
Artificial Intelligence: Machine Learning
Current Issues in Data Mining
Database Systems and Applications: Data Mining Experimental Design
Fundamentals of SAS
Nonparametric Statistics
Web Mining

Those professionals who have completed the masters will progress their advance research with the help of PhDs.

Below list of some of the potential research topics in PhDs Data mining

ALGORITHMS AND TECHNIQUES

DATA MINING AND DATABASES

FOUNDATIONS OF DATA MINING

INNOVATIVE APPLICATIONS

MINING DIFFERENT FORMS OF DATA

DATA PRE-PROCESSING

KDD PROCESS AND PROCESS-CENTRIC DATA MINING

PATTERN POST-PROCESSING

Statistical techniques and mixture models
Scalable algorithms
Rule discovery
Privacy preserving data mining
Multi-relational data mining
Incremental algorithms
Frequent patterns
Distributed and parallel algorithms
Constraint-based mining
Clustering
Classification

Data mining query languages and optimization
Database integration
Inductive databases

Statistical inference and probabilistic modeling
Logic for data mining
Knowledge (pattern) representation
Global vs. local patterns
Complexity issues

Web content, structure and usage mining
Semantic web mining
Personalization
Mining governmental data, mining for the public administration
Mining biomedical data
Invisible data mining
Adaptive data mining architectures

Text mining
Temporal, spatial, and spatiotemporal data mining
Semi-structured and XML data mining
Multimedia miningPattern post-processing
Graph, tree, sequence mining
Data stream mining

Dimensionality reduction
Data reduction
Discretization
Uncertain and missing information handling

Vertical data mining environments
Standards for the KDD process
Models of the KDD process
Collaborative data mining
Background knowledge integration

Visualization
Quality assessment
Knowledge interpretation and use

During this course of study, academic score plays a major role in completing the program. Particularly, most of the nations and universities PhDs dissertation and thesis/Master’s thesis score are an important one. Undoubtedly, the Ph.D. dissertation is not an easy writing task because it needed an extraordinary writing skill with proficiency and experience in that particular field as well as good reasoning and analytical skills to tackle this hard task. We are the one of the No.1 recommended and best assistant for any student’s research writing work. Eventually, this challenge worries that clearing the program as important as 50% as scoring dissertation/thesis to accomplish high credits in order to complete a research project.

Data Mining / Image Mining / Cloud Computing