Hi buddy I want to do MSC IT from Mumbai University and for the same here looking for eligibility criteria for this program along with its syllabus so would you plz provide me same here??

As you want to do MSC IT from Mumbai University and for the same here looking for eligibility criteria, so on your demand I am providing require info :

Admission criteria:
Student must have passed the B.Sc. degree in Information Technology of the University of Mumbai or any recognized University with minimum 45% marks or B.Sc. Comp Sci.) with minimum 45% marks or BE degree in any branch with 45% marks or B.Sc.(Maths) with minimum 45% marks or B.Sc.(Physics) with minimum 45% marks or B.Sc. (Stats) with minimum 45% marks or B.Sc.(Electronics) with minimum 45% marks


Mumbai University Master of Science (Information Technology) syllabus
Introduction: Basics of data mining, related concepts, Data mining
techniques. Data: Introduction, Attributes, Data Sets, and Data Storage, Issues
Concerning the Amount and Quality of Data, Knowledge Representation:
Data Representation and their Categories: General Insights, Categories of
I Knowledge Representation, Granularity of Data and Knowledge
Representation Schemes, Sets and Interval Analysis, Fuzzy Sets as Human-
Centric Information Granules, Shadowed Sets, Rough Sets, Characterization of
Knowledge Representation Schemes, Levels of Granularity and Perception
Perspectives, The Concept of Granularity in Rules.
Data Preprocessing: Descriptive Data Summarization, Data Cleaning, Data
Integration and Transformation, Data Reduction, Data Discretization and
Concept Hierarchy Generation.
II Mining Frequent Patterns, Associations, and Correlations: Basic Concepts,
Efficient and Scalable Frequent Itemset Mining Methods, Mining Various
Kinds of Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining
III Classification and Prediction: What Is Classification?, What Is Prediction?, Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Rule-Based Classification, Classification by Back-propagation, Support Vector Machines, Associative Classification: Classification by Association Rule Analysis, Lazy Learners, Other Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the Accuracy of a Classifier or Predictor, Ensemble MethodsIncreasing the Accuracy, Model Selection.
IV Cluster Analysis:What Is Cluster Analysis?, Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data, Constraint-Based Cluster Analysis, Outlier Analysis
V Graph Mining, Social Network Analysis, and Multirelational Data Mining: Graph Mining, Social Network Analysis, Multirelational Data Mining.Mining Object, Spatial, Multimedia, Text, and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web.

Mumbai University Master of Science (Information Technology) syllabus

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