Data Warehousing and Data Mining - Syllabus

Overview of E-commerce and its significance in the digital age.

Understanding the differences between E-business and E-commerce.

Key characteristics and benefits of E-commerce.

Types of E-commerce models and their applications.

Evolution and development of E-commerce over time.

Understanding the components of E-commerce framework including People, Public Policy, Marketing and Advertisement, Support Services, and Business Partnerships.

Overview of different types of E-commerce including B2C, B2B, C2B, C2C, M-Commerce, U-commerce, Social-Ecommerce, and Local E-commerce.

Common obstacles and difficulties faced in E-commerce.

Current state and trends of E-commerce in Nepal.

Understanding the legal framework governing E-commerce in Nepal.

Ethical considerations and principles in software engineering, including accountability, privacy, and intellectual property.

Exploring the role of distributed computing in grid and cloud environments, including its applications and benefits.

Current and emerging trends in data warehousing, including big data, cloud computing, and real-time analytics.

An overview of computers and their significance in today's world. This topic sets the stage for understanding the basics of computers.

Understanding the difference between digital and analog computers, their characteristics, and applications.

Exploring the key characteristics of computers, including input, processing, storage, and output.

A brief history of computers, from their inception to the present day, highlighting key milestones and developments.

Understanding the different generations of computers, including their features, advantages, and limitations.

Categorizing computers based on their size, functionality, and application, including desktops, laptops, and mobile devices.

An in-depth look at the components of a computer system, including hardware and software.

Introduction to databases, including examples and basic concepts.

Introduction to Database Management Systems (DBMS), including advantages and examples.

Types of database users, including actors on the scene and workers behind the scene.

Advantages and benefits of using databases.

Types of data models, including hierarchical, network, ER, relational, and object models.

Introduction to databases, including examples and basic concepts.

Introduction to Database Management Systems (DBMS), including advantages and examples.

Types of database users, including actors on the scene and workers behind the scene.

Advantages and benefits of using databases.

Types of data models, including hierarchical, network, ER, relational, and object models.

Three-schema architecture, including internal, conceptual, and external views.

This topic covers the concept of control words and microprograms in microprogrammed control, including their roles in controlling the flow of data and instructions in a computer system.

This topic explains how address sequencing and conditional branching are used to control the flow of instructions in a microprogrammed control unit, including the use of conditional branch instructions and subroutines.

This topic covers the format of microinstructions and the use of symbolic microinstructions to represent complex control sequences in a microprogrammed control unit.

This topic covers the design principles and considerations for building a control unit using microprogrammed control, including the organization of control memory and the role of the sequencer.

Association rules are statements that describe the relationship between different items in a dataset. They are used to identify patterns and correlations between items.

There are different types of association rules, including single dimensional, multidimensional, multilevel, and quantitative rules. Each type has its own characteristics and applications.

Finding frequent itemsets involves using algorithms such as Apriori and FP-growth to identify patterns in a dataset.

Generating association rules involves using frequent itemsets to create rules that describe the relationships between items.

The Apriori algorithm has limitations, such as being computationally expensive. Improvements can be made by using techniques such as sampling and parallel processing.

Association mining can be extended to correlation analysis, which involves identifying relationships between continuous variables.

Lift is a measure of the strength of an association rule. It helps to evaluate the usefulness and relevance of the rule.

This topic covers the fundamental technologies used on the client-side of web development, including HTML, CSS, and JavaScript.

This topic explores the use of JQuery, a popular JavaScript library, for client-side scripting and DOM manipulation.

This topic discusses the importance of form validation and how to implement it using ASP.NET Core, including client-side and server-side validation techniques.

This topic introduces Single Page Application (SPA) frameworks, including Angular and React, and their role in building dynamic and interactive client-side applications.

SaaS implementation issues, key characteristics of SaaS, benefits of the SaaS model.

A cloud service model framework.

Creating custom objects with properties and methods.

Handling events and validating form data with JavaScript.

Catching and handling errors in JavaScript code.

Storing and retrieving data with cookies in JavaScript.

Graphic presentation involves using graphs such as histograms, frequency polygons, and frequency curves to present data. It is a visual way of presenting data, making it easy to understand and analyze.

A histogram is a type of graph that uses bars to represent the frequency of different ranges of values. It is commonly used to display continuous data.

A frequency polygon is a type of graph that uses lines to connect the points representing the frequency of different ranges of values. It is commonly used to display continuous data.

This topic covers the fundamental technologies used on the client-side of web development, including HTML, CSS, and JavaScript.

This topic explores the use of JQuery, a popular JavaScript library, for client-side scripting and DOM manipulation.

This topic discusses the importance of form validation and how to implement it using ASP.NET Core, including client-side and server-side validation techniques.

This topic introduces Single Page Application (SPA) frameworks, including Angular and React, and their role in building dynamic and interactive client-side applications.

SaaS implementation issues, key characteristics of SaaS, benefits of the SaaS model.

A cloud service model framework.

Introduction to optimization problems and the concept of optimal solutions, with an overview of greedy algorithms and their elements.

Exploration of various applications of greedy algorithms, including fractional knapsack, job sequencing with deadlines, Kruskal's algorithm, Prim's algorithm, and Dijkstra's algorithm.

Introduction to Huffman coding, including its purpose, prefix codes, and the Huffman coding algorithm, along with its analysis.

Social network analysis is the process of examining social structures, relationships, and interactions within a network. It involves using graph theory and statistical methods to understand social behavior and patterns.

Link mining is a subfield of graph mining that focuses on the analysis of links between nodes in a graph. It involves discovering patterns and relationships between entities in a network.

Friends of friends is a concept in social network analysis that refers to the friends of an individual's friends. It is used to study social relationships and network structures.

Degree assortativity is a measure of the tendency of nodes in a network to be connected to other nodes with similar degrees. It is used to study network structures and patterns.

Signed networks are graphs that contain both positive and negative edges, representing friendships and antagonisms between nodes. It involves using theories such as structured balance and status to analyze signed networks.

Trust in a network refers to the level of confidence or reliability between nodes. It involves using algorithms such as atomic propagation and iterative propagation to predict trust and distrust in a network.

This topic involves using machine learning and graph mining techniques to predict the formation of positive and negative links in a network, such as friendships and antagonisms.

Association mining can be extended to correlation analysis, which involves identifying relationships between continuous variables.

Lift is a measure of the strength of an association rule. It helps to evaluate the usefulness and relevance of the rule.

Spatial data mining involves discovering patterns and relationships in spatial data, such as geographic information. It includes techniques for mining spatial association and spatial data cubes.

A spatial data cube is a multidimensional representation of spatial data, allowing for efficient querying and analysis of spatial relationships.

Mining spatial association involves discovering relationships between spatial objects, such as proximity, distance, and orientation.

Multimedia data mining involves discovering patterns and relationships in multimedia data, such as images, videos, and audio files.

Similarity search in multimedia data involves finding similar multimedia objects based on their features and attributes.

Mining association in multimedia data involves discovering relationships between multimedia objects, such as co-occurrence and correlation.

Text mining involves discovering patterns and relationships in unstructured text data, using techniques from natural language processing and information extraction.

Web mining involves discovering patterns and relationships in web data, including web content, structure, and usage.

Web content mining involves extracting useful information from web pages, such as text, images, and links.