Datacleaning.ppt
•Download as PPTX, PDF•
0 likes•8 views
The document discusses various types of data quality issues that may be present in datasets, including invalid values, different data formats, attribute dependencies, lack of uniqueness, missing values, misspellings, and misfield values. It also presents several techniques that can be used to detect and fix these issues, such as data visualization, outlier analysis, validation code, indicator variables, data binning/bucketing, centering and scaling, and other techniques like grouping outliers or replacing values with frequencies. The overall goal is to clean raw data and ensure it is valid and consistent before use in analysis.
Report
Share
Report
Share
Recommended
E-R diagram in Database
An E-R diagram is a logical representation of data in an organization that views the entire system as entities related to one another. The key elements of an E-R diagram are entities, attributes, and relationships. Entities represent people, places, things, or events and have attributes with defined domains. Relationships connect different entities and can be one-to-one, one-to-many, or many-to-many. E-R diagrams were introduced in 1976 and provide advantages like a logical structure for modeling an organization's data.
D I T211 Chapter 3
The document provides an overview of entity relationship modeling including:
1. The key elements of an ER model are entities, attributes, and relationships. Entities can have attributes and be connected by relationships.
2. An ER diagram graphically represents the ER model using symbols and connectors to show entities, attributes, and relationships between entities.
3. The document describes the different types of relationships and provides examples of how to construct ER diagrams.
Er diagram
The document provides information on the key concepts of an entity-relationship (E-R) model, including:
1) Entities represent real-world objects like people, places, and things that are stored in a database. Attributes describe the properties of entities.
2) Relationships represent associations between entities. Relationships have properties like degree, cardinality, and existence.
3) Keys like primary keys and foreign keys uniquely identify entities and define relationships between entities.
4) Strong and weak entities differ in whether they have their own primary keys or rely on other entities.
5) E-R diagrams visually depict entities, attributes, relationships, keys and other concepts to model a database.
Entity Relationship Model
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
Entity relationship diagram (erd)
The document provides information on entity relationship diagrams (ERDs), including the objectives, components, and steps to create an ERD. It defines key ERD concepts like entities, attributes, relationships, and cardinality. It describes the entity modeling process and discusses how to recognize entities, attributes, relationships, and cardinalities in a database. It outlines the general steps to create an ERD, including identifying entities, finding relationships between entities, drawing a rough ERD, defining primary keys, identifying attributes, mapping attributes to entities, and drawing a fully attributed ERD. Sample ERDs are provided to illustrate concepts like cardinality constraints.
Er model ppt
The document discusses the entity relationship (ER) model used for conceptual database design. It describes the key components of an ER diagram including entities represented as rectangles, attributes described as ovals, and relationships shown as diamonds. Different types of relationships are also defined such as one-to-one, one-to-many, many-to-one, and many-to-many. The ER model provides a way to design and visualize the entities, attributes, and relationships within a database in a simple diagram.
ER Diagram
The document discusses entity-relationship (ER) modeling concepts including:
- Entity types such as EMPLOYEE, DEPARTMENT, PROJECT, and DEPENDENT that will be part of the example COMPANY database.
- Relationship types such as WORKS_FOR, MANAGES, WORKS_ON, CONTROLS, SUPERVISION, and DEPENDENTS_OF that relate the entity types.
- ER diagram notation used to visually represent the entities, attributes, and relationships between entities for the COMPANY schema.
Entity Relationship Diagram
Entity Relationship Diagrams (ERDs) are used to model relationships between entities in a database. The document discusses ERD components like entities, relationships, cardinality, and attributes. It provides an example of an ERD for a company with departments, supervisors, employees, and projects. Key entities are identified and their relationships and attributes are represented in the example ERD diagrams.
Recommended
E-R diagram in Database
An E-R diagram is a logical representation of data in an organization that views the entire system as entities related to one another. The key elements of an E-R diagram are entities, attributes, and relationships. Entities represent people, places, things, or events and have attributes with defined domains. Relationships connect different entities and can be one-to-one, one-to-many, or many-to-many. E-R diagrams were introduced in 1976 and provide advantages like a logical structure for modeling an organization's data.
D I T211 Chapter 3
The document provides an overview of entity relationship modeling including:
1. The key elements of an ER model are entities, attributes, and relationships. Entities can have attributes and be connected by relationships.
2. An ER diagram graphically represents the ER model using symbols and connectors to show entities, attributes, and relationships between entities.
3. The document describes the different types of relationships and provides examples of how to construct ER diagrams.
Er diagram
The document provides information on the key concepts of an entity-relationship (E-R) model, including:
1) Entities represent real-world objects like people, places, and things that are stored in a database. Attributes describe the properties of entities.
2) Relationships represent associations between entities. Relationships have properties like degree, cardinality, and existence.
3) Keys like primary keys and foreign keys uniquely identify entities and define relationships between entities.
4) Strong and weak entities differ in whether they have their own primary keys or rely on other entities.
5) E-R diagrams visually depict entities, attributes, relationships, keys and other concepts to model a database.
Entity Relationship Model
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
Entity relationship diagram (erd)
The document provides information on entity relationship diagrams (ERDs), including the objectives, components, and steps to create an ERD. It defines key ERD concepts like entities, attributes, relationships, and cardinality. It describes the entity modeling process and discusses how to recognize entities, attributes, relationships, and cardinalities in a database. It outlines the general steps to create an ERD, including identifying entities, finding relationships between entities, drawing a rough ERD, defining primary keys, identifying attributes, mapping attributes to entities, and drawing a fully attributed ERD. Sample ERDs are provided to illustrate concepts like cardinality constraints.
Er model ppt
The document discusses the entity relationship (ER) model used for conceptual database design. It describes the key components of an ER diagram including entities represented as rectangles, attributes described as ovals, and relationships shown as diamonds. Different types of relationships are also defined such as one-to-one, one-to-many, many-to-one, and many-to-many. The ER model provides a way to design and visualize the entities, attributes, and relationships within a database in a simple diagram.
ER Diagram
The document discusses entity-relationship (ER) modeling concepts including:
- Entity types such as EMPLOYEE, DEPARTMENT, PROJECT, and DEPENDENT that will be part of the example COMPANY database.
- Relationship types such as WORKS_FOR, MANAGES, WORKS_ON, CONTROLS, SUPERVISION, and DEPENDENTS_OF that relate the entity types.
- ER diagram notation used to visually represent the entities, attributes, and relationships between entities for the COMPANY schema.
Entity Relationship Diagram
Entity Relationship Diagrams (ERDs) are used to model relationships between entities in a database. The document discusses ERD components like entities, relationships, cardinality, and attributes. It provides an example of an ERD for a company with departments, supervisors, employees, and projects. Key entities are identified and their relationships and attributes are represented in the example ERD diagrams.
E r diagram
An entity relationship (ER) diagram visually represents the relationships between different elements in a system. It identifies key entities, their attributes, and how they relate to each other. ER diagrams are used in software engineering during planning to understand system elements and relationships. They form the basis for data flow diagrams. Key elements in an ER diagram include entities, attributes, and relationships. Entities represent objects like people or things. Attributes are properties of entities or relationships. Relationships show how entities are connected.
ER-Model-ER Diagram
Here are the entity relationship diagrams and tables for the car insurance company database:
(a) E-R Diagram:
Person - owns - Car
(1:M)
Car - involved in - Accident
(0:M)
(b) Tables:
person
(driver_id primary key,
name, address)
car
(license primary key,
year, model, driver_id foreign key references person)
accident
(report_number primary key,
date, location)
participated
(driver_id foreign key references person,
license foreign key references car,
report_number foreign key references accident
Entity Relationship Diagrams
Entity Relationship Diagrams (ERDs) are conceptual data models used in software engineering to model information systems. ERDs represent entities as rectangles, attributes as ellipses, and relationships as diamonds connecting entities. Attributes can be single-valued, multi-valued, composite, or derived. Relationships have cardinality like one-to-one, one-to-many, many-to-one, or many-to-many. Participation constraints and Codd's 12 rules of relational databases are also discussed in the document.
Entity Relationship Diagram
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
Entity relation(1)
Peter Chen developed entity-relationship (ER) diagrams in 1976 to model databases. ER diagrams use entities (objects), attributes (data about entities), and relationships (connections between entities) to represent the structure and semantics of data. Developing an ER diagram helps database designers and analysts better understand the information to be stored in a database by modeling the entities, attributes, and relationships visually.
ER MODEL
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
ER Model in DBMS
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
Entity relationship modelling
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
Entity Relationship Modelling
This document discusses entity relationship (ER) modeling. It defines key concepts in ER modeling including entities, attributes, relationships, and ER diagram notations. Entities can be people, places, objects or concepts and are grouped into entity types. Attributes provide information about entities. Relationships define how entities are connected. Common relationship types are one-to-one, one-to-many, many-to-one, and many-to-many. ER diagrams use notations like boxes, lines, and crow's foot symbols to visually depict entities, attributes, and relationships in a database design. The document also covers entity classification, primary keys, foreign keys, and potential problems in ER modeling.
Erd examples
The document describes an entity relationship (ER) diagram for a university database. The ER diagram models entities such as students, departments, courses, and sections. It also models attributes of these entities and relationships between entities such as students having a major department, courses being offered by departments, and students receiving grades in sections.
Data Models
This document discusses data models and the entity relationship model. It defines a data model as a representation of real world objects and events that hides low-level storage details. The entity relationship model represents entities as objects with attributes, and relationships between entity sets. Entities are grouped into entity sets and represented as rectangles. Relationships are represented as diamonds connected to entity sets. Attributes are represented as ellipses connected to entity sets. The document describes key attributes, simple attributes, composite attributes, derived attributes, and single and multi-value attributes. It provides an example of a student entity with attributes like roll number, name, and date of birth.
Database - Entity Relationship Diagram (ERD)
The document provides an overview of entity relationship diagrams (ERDs) including their basic components, different notations, and how to implement various relationship types in a relational database. ERDs depict entities, attributes, and relationships in a conceptual database design. Key points covered include the three main notations of ERDs, solving multi-valued attributes and many-to-many relationships, and how to implement one-to-one, one-to-many, and many-to-many relationships through primary and foreign key constraints.
Presentation of saad on e-r diagram.
The document presents an overview of entity-relationship (E-R) diagrams. It defines E-R diagrams as visual representations of entities and relationships within a system. The key components of E-R diagrams are described as entities, attributes, and relationships. Entities represent objects, attributes are properties of entities, and relationships show interactions between entities. The document also provides examples of different types of relationships, instructions for creating E-R diagrams, and applications of using E-R diagrams for database design and software engineering.
DBMS UNIT1
The document summarizes key concepts of the Entity-Relationship (E-R) model including:
- Entity sets represent groups of related entities and can have subclasses. Relationships connect entity sets and can be binary, ternary or higher.
- Weak entities depend on other entities, and their existence is defined by an identifying relationship. Discriminators uniquely identify weak entities.
- Keys uniquely identify entities in an entity set and must be designated. Relationships can have attributes and roles to distinguish relationships.
Erd1
This document discusses entity relationship modeling and conceptual database design. It describes how conceptual modeling involves describing the data, relationships between data, and constraints. The output is a conceptual data model and data dictionary. Key aspects of ER modeling covered include defining entities and attributes, relationships between entities, and cardinalities. Different types of relationships like one-to-one, one-to-many, and many-to-many are explained. The document provides guidelines for effectively modeling data and common mistakes to avoid, like modeling business processes instead of data.
Relational Databases 2
The document discusses relationships in relational databases. There are three main types of relationships: one-to-one, where each record in one table matches only one in another; one-to-many, where a record in one table can match many in another, which is the most common; and many-to-many, where records can match with many in both tables. Entity relationship (ER) diagrams are used to graphically represent entities as boxes, and relationships as lines between them with arrows indicating the relationship type. The process of creating ER diagrams involves identifying entities, defining relationships between them, specifying the relationship type, and defining the table structure.
Symbol of e r diagram presentation
The document discusses an ER diagram model that describes the logical structure of a database using entities, attributes, and relationships. It defines the key components of an ER diagram as entity sets and relationship sets. It also outlines the symbols used in an ER diagram such as strong and weak entities, attributes like key and multi-valued attributes, relationship degrees like binary and n-ary, and different types of keys. An example ER diagram is included to demonstrate these concepts.
RDBMS ERD
The document discusses the Entity Relationship Model and its key concepts including entities, attributes, relationships, keys, and cardinalities. It explains how ER diagrams visually depict these concepts through symbols like rectangles for entities and diamonds for relationships. The ER model is used for conceptual database design and captures the logical properties and meanings within an organization's domain.
Er model
The document discusses key concepts in entity-relationship (ER) modeling including:
- Entities can represent real-world objects and have attributes that identify them. Common entities in a school database may be students, teachers, classes, and courses.
- Relationships define associations between entities, such as a student enrolling in a course. Relationships have cardinalities that specify how many entities can be connected.
- Attributes of entities can be simple, composite, derived or multi-valued. Keys uniquely identify entities and can be candidate or primary keys.
- ER diagrams visually represent these concepts through entities, relationships, and attributes.
Ch 3 E R Model
The document discusses key concepts in entity-relationship modeling for database design including:
1. Entities should represent real-world objects like people, places, things while avoiding outputs or users.
2. Relationships define connections between entities and can have attributes. Cardinality constraints specify how many entities can connect.
3. Weak entities rely on strong entities for their identifiers. Associative entities act as both relationships and entities.
3. Chapter Three.pdf
The document discusses the relational data model and ER model for conceptual database design. It covers key concepts such as entities, attributes, relationships, constraints, and ER diagrams. The relational data model uses tables made up of rows and columns to store data, with each table representing an entity. Relationships between entities can be one-to-one, one-to-many, many-to-one, or many-to-many. The ER model is used to design the conceptual schema and represent entities, attributes, and relationships visually using diagrams. The conceptual schema is later transformed into a logical schema for a specific database implementation.
EXPLAIN SPSS-part1.pdf
Are you eager to unlock the full potential of SPSS for data analysis and research? Look no further! This SlideShare presentation is your ultimate guide to mastering SPSS, equipping you with the knowledge and skills to harness the power of this versatile statistical software.
Overview:
In this comprehensive presentation, we delve into the fundamental concepts of SPSS and guide you through its various features, functions, and practical applications. Whether you're a student, researcher, analyst, or professional seeking to elevate your data analysis capabilities, this presentation is tailored for all skill levels.
Key Topics Covered:
Introduction to SPSS: Get acquainted with the interface, workspace, and essential tools to kickstart your SPSS journey.
Data Preparation: Learn best practices for data entry, cleaning, and transforming, ensuring the accuracy and reliability of your analysis.
Descriptive Statistics: Explore various methods to summarize and present data, including measures of central tendency, dispersion, and graphical representations.
Inferential Statistics: Dive into hypothesis testing, t-tests, ANOVA, regression, and other techniques to draw meaningful conclusions from your data.
Advanced Analysis: Uncover the power of multivariate analysis, factor analysis, and cluster analysis for complex research scenarios.
Data Visualization: Master the art of creating compelling charts, graphs, and visualizations to communicate your findings effectively.
Reporting and Interpretation: Learn how to interpret SPSS output and craft clear, insightful reports for diverse audiences.
Why Attend?
Gain Confidence: Build your confidence in using SPSS through step-by-step tutorials and real-world examples.
Enhance Research Skills: Acquire the skills to conduct robust and in-depth data analysis for your research projects.
Career Advancement: Enhance your professional profile and open doors to new opportunities with strong SPSS proficiency.
Join a Learning Community: Connect with like-minded professionals, researchers, and enthusiasts to exchange knowledge and insights.
More Related Content
What's hot
E r diagram
An entity relationship (ER) diagram visually represents the relationships between different elements in a system. It identifies key entities, their attributes, and how they relate to each other. ER diagrams are used in software engineering during planning to understand system elements and relationships. They form the basis for data flow diagrams. Key elements in an ER diagram include entities, attributes, and relationships. Entities represent objects like people or things. Attributes are properties of entities or relationships. Relationships show how entities are connected.
ER-Model-ER Diagram
Here are the entity relationship diagrams and tables for the car insurance company database:
(a) E-R Diagram:
Person - owns - Car
(1:M)
Car - involved in - Accident
(0:M)
(b) Tables:
person
(driver_id primary key,
name, address)
car
(license primary key,
year, model, driver_id foreign key references person)
accident
(report_number primary key,
date, location)
participated
(driver_id foreign key references person,
license foreign key references car,
report_number foreign key references accident
Entity Relationship Diagrams
Entity Relationship Diagrams (ERDs) are conceptual data models used in software engineering to model information systems. ERDs represent entities as rectangles, attributes as ellipses, and relationships as diamonds connecting entities. Attributes can be single-valued, multi-valued, composite, or derived. Relationships have cardinality like one-to-one, one-to-many, many-to-one, or many-to-many. Participation constraints and Codd's 12 rules of relational databases are also discussed in the document.
Entity Relationship Diagram
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
Entity relation(1)
Peter Chen developed entity-relationship (ER) diagrams in 1976 to model databases. ER diagrams use entities (objects), attributes (data about entities), and relationships (connections between entities) to represent the structure and semantics of data. Developing an ER diagram helps database designers and analysts better understand the information to be stored in a database by modeling the entities, attributes, and relationships visually.
ER MODEL
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
ER Model in DBMS
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
Entity relationship modelling
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
Entity Relationship Modelling
This document discusses entity relationship (ER) modeling. It defines key concepts in ER modeling including entities, attributes, relationships, and ER diagram notations. Entities can be people, places, objects or concepts and are grouped into entity types. Attributes provide information about entities. Relationships define how entities are connected. Common relationship types are one-to-one, one-to-many, many-to-one, and many-to-many. ER diagrams use notations like boxes, lines, and crow's foot symbols to visually depict entities, attributes, and relationships in a database design. The document also covers entity classification, primary keys, foreign keys, and potential problems in ER modeling.
Erd examples
The document describes an entity relationship (ER) diagram for a university database. The ER diagram models entities such as students, departments, courses, and sections. It also models attributes of these entities and relationships between entities such as students having a major department, courses being offered by departments, and students receiving grades in sections.
Data Models
This document discusses data models and the entity relationship model. It defines a data model as a representation of real world objects and events that hides low-level storage details. The entity relationship model represents entities as objects with attributes, and relationships between entity sets. Entities are grouped into entity sets and represented as rectangles. Relationships are represented as diamonds connected to entity sets. Attributes are represented as ellipses connected to entity sets. The document describes key attributes, simple attributes, composite attributes, derived attributes, and single and multi-value attributes. It provides an example of a student entity with attributes like roll number, name, and date of birth.
Database - Entity Relationship Diagram (ERD)
The document provides an overview of entity relationship diagrams (ERDs) including their basic components, different notations, and how to implement various relationship types in a relational database. ERDs depict entities, attributes, and relationships in a conceptual database design. Key points covered include the three main notations of ERDs, solving multi-valued attributes and many-to-many relationships, and how to implement one-to-one, one-to-many, and many-to-many relationships through primary and foreign key constraints.
Presentation of saad on e-r diagram.
The document presents an overview of entity-relationship (E-R) diagrams. It defines E-R diagrams as visual representations of entities and relationships within a system. The key components of E-R diagrams are described as entities, attributes, and relationships. Entities represent objects, attributes are properties of entities, and relationships show interactions between entities. The document also provides examples of different types of relationships, instructions for creating E-R diagrams, and applications of using E-R diagrams for database design and software engineering.
DBMS UNIT1
The document summarizes key concepts of the Entity-Relationship (E-R) model including:
- Entity sets represent groups of related entities and can have subclasses. Relationships connect entity sets and can be binary, ternary or higher.
- Weak entities depend on other entities, and their existence is defined by an identifying relationship. Discriminators uniquely identify weak entities.
- Keys uniquely identify entities in an entity set and must be designated. Relationships can have attributes and roles to distinguish relationships.
Erd1
This document discusses entity relationship modeling and conceptual database design. It describes how conceptual modeling involves describing the data, relationships between data, and constraints. The output is a conceptual data model and data dictionary. Key aspects of ER modeling covered include defining entities and attributes, relationships between entities, and cardinalities. Different types of relationships like one-to-one, one-to-many, and many-to-many are explained. The document provides guidelines for effectively modeling data and common mistakes to avoid, like modeling business processes instead of data.
Relational Databases 2
The document discusses relationships in relational databases. There are three main types of relationships: one-to-one, where each record in one table matches only one in another; one-to-many, where a record in one table can match many in another, which is the most common; and many-to-many, where records can match with many in both tables. Entity relationship (ER) diagrams are used to graphically represent entities as boxes, and relationships as lines between them with arrows indicating the relationship type. The process of creating ER diagrams involves identifying entities, defining relationships between them, specifying the relationship type, and defining the table structure.
Symbol of e r diagram presentation
The document discusses an ER diagram model that describes the logical structure of a database using entities, attributes, and relationships. It defines the key components of an ER diagram as entity sets and relationship sets. It also outlines the symbols used in an ER diagram such as strong and weak entities, attributes like key and multi-valued attributes, relationship degrees like binary and n-ary, and different types of keys. An example ER diagram is included to demonstrate these concepts.
RDBMS ERD
The document discusses the Entity Relationship Model and its key concepts including entities, attributes, relationships, keys, and cardinalities. It explains how ER diagrams visually depict these concepts through symbols like rectangles for entities and diamonds for relationships. The ER model is used for conceptual database design and captures the logical properties and meanings within an organization's domain.
Er model
The document discusses key concepts in entity-relationship (ER) modeling including:
- Entities can represent real-world objects and have attributes that identify them. Common entities in a school database may be students, teachers, classes, and courses.
- Relationships define associations between entities, such as a student enrolling in a course. Relationships have cardinalities that specify how many entities can be connected.
- Attributes of entities can be simple, composite, derived or multi-valued. Keys uniquely identify entities and can be candidate or primary keys.
- ER diagrams visually represent these concepts through entities, relationships, and attributes.
Ch 3 E R Model
The document discusses key concepts in entity-relationship modeling for database design including:
1. Entities should represent real-world objects like people, places, things while avoiding outputs or users.
2. Relationships define connections between entities and can have attributes. Cardinality constraints specify how many entities can connect.
3. Weak entities rely on strong entities for their identifiers. Associative entities act as both relationships and entities.
What's hot (20)
Similar to Datacleaning.ppt
3. Chapter Three.pdf
The document discusses the relational data model and ER model for conceptual database design. It covers key concepts such as entities, attributes, relationships, constraints, and ER diagrams. The relational data model uses tables made up of rows and columns to store data, with each table representing an entity. Relationships between entities can be one-to-one, one-to-many, many-to-one, or many-to-many. The ER model is used to design the conceptual schema and represent entities, attributes, and relationships visually using diagrams. The conceptual schema is later transformed into a logical schema for a specific database implementation.
EXPLAIN SPSS-part1.pdf
Are you eager to unlock the full potential of SPSS for data analysis and research? Look no further! This SlideShare presentation is your ultimate guide to mastering SPSS, equipping you with the knowledge and skills to harness the power of this versatile statistical software.
Overview:
In this comprehensive presentation, we delve into the fundamental concepts of SPSS and guide you through its various features, functions, and practical applications. Whether you're a student, researcher, analyst, or professional seeking to elevate your data analysis capabilities, this presentation is tailored for all skill levels.
Key Topics Covered:
Introduction to SPSS: Get acquainted with the interface, workspace, and essential tools to kickstart your SPSS journey.
Data Preparation: Learn best practices for data entry, cleaning, and transforming, ensuring the accuracy and reliability of your analysis.
Descriptive Statistics: Explore various methods to summarize and present data, including measures of central tendency, dispersion, and graphical representations.
Inferential Statistics: Dive into hypothesis testing, t-tests, ANOVA, regression, and other techniques to draw meaningful conclusions from your data.
Advanced Analysis: Uncover the power of multivariate analysis, factor analysis, and cluster analysis for complex research scenarios.
Data Visualization: Master the art of creating compelling charts, graphs, and visualizations to communicate your findings effectively.
Reporting and Interpretation: Learn how to interpret SPSS output and craft clear, insightful reports for diverse audiences.
Why Attend?
Gain Confidence: Build your confidence in using SPSS through step-by-step tutorials and real-world examples.
Enhance Research Skills: Acquire the skills to conduct robust and in-depth data analysis for your research projects.
Career Advancement: Enhance your professional profile and open doors to new opportunities with strong SPSS proficiency.
Join a Learning Community: Connect with like-minded professionals, researchers, and enthusiasts to exchange knowledge and insights.
Categorical DataCategorical data represents characteristics..docx
Categorical Data
Categorical data represents characteristics. Therefore it can represent things like a person’s gender, language etc. Categorical data can also take on numerical values (Example: 1 for female and 0 for male). Note that those numbers don’t have mathematical meaning.
Nominal Data
Nominal values represent discrete units and are used to label variables, that have no quantitative value. Just think of them as „labels“. Note that nominal data that has no order. Therefore if you would change the order of its values, the meaning would not change. You can see two examples of nominal features below:
The left feature that describes a persons gender would be called „dichotomous“, which is a type of nominal scales that contains only two categories.
Ordinal Data
Ordinal values represent discrete and ordered units. It is therefore nearly the same as nominal data, except that it’s ordering matters. You can see an example below:
Note that the difference between Elementary and High School is different than the difference between High School and College. This is the main limitation of ordinal data, the differences between the values is not really known. Because of that, ordinal scales are usually used to measure non-numeric features like happiness, customer satisfaction and so on.
Numerical Data
1. Discrete Data
We speak of discrete data if its values are distinct and separate. In other words: We speak of discrete data if the data can only take on certain values. This type of data can’t be measured but it can be counted. It basically represents information that can be categorized into a classification. An example is the number of heads in 100 coin flips.
You can check by asking the following two questions whether you are dealing with discrete data or not: Can you count it and can it be divided up into smaller and smaller parts?
2. Continuous Data
Continuous Data represents measurements and therefore their values can’t be counted but they can be measured. An example would be the height of a person, which you can describe by using intervals on the real number line.
Interval Data
Interval values represent ordered units that have the same difference. Therefore we speak of interval data when we have a variable that contains numeric values that are ordered and where we know the exact differences between the values. An example would be a feature that contains temperature of a given place like you can see below:
The problem with interval values data is that they don’t have a „true zero“. That means in regards to our example, that there is no such thing as no temperature. With interval data, we can add and subtract, but we cannot multiply, divide or calculate ratios. Because there is no true zero, a lot of descriptive and inferential statistics can’t be applied.
Ratio Data
Ratio values are also ordered units that have the same difference. Ratio values are the same as interval values, with the difference that they do have an absolute zero. Good e ...
Unit 2-Data Modeling.pdf
The document discusses conceptual data modeling using the entity-relationship (ER) model. It begins by explaining how database designers interview users to understand data requirements and create a conceptual schema using a high-level ER model. This conceptual schema is then transformed into an implementation model using a commercial database system. The document also provides examples of an entity, attribute, relationship, and developing an ER diagram for a sample company database that tracks employees, departments, and projects.
Exploratory Data Analysis Unit 1 ppt presentation.pptx
This document discusses various concepts related to data processing and statistics including data collection, cleaning, visualization, and analysis. It covers basic statistical measures like mean, median, mode, variance and standard deviation. It also discusses outlier detection methods using z-scores and interquartile range. Missing data handling techniques include dropping rows, imputing continuous variables with mean/median/mode and categorical variables with most frequent value.
E_R-Diagram (2).pptx
The document discusses the Entity Relationship (ER) model and ER diagrams. The ER model is a conceptual data modeling technique that is used to produce a database design. It displays entities, attributes, and relationships between entities. ER diagrams help visualize these components and the logical structure of databases. Some key benefits of ER diagrams include defining database terms, providing a preview of how tables connect, and allowing communication of the database structure. Common components of ER diagrams are entities, attributes, and relationships.
measurement and scaling techniques
This document discusses different types of measurement scales used in research. There are four main categories of measurement scales: nominal, ordinal, interval, and ratio scales. Nominal scales simply classify items into categories, while ordinal scales rank items along a continuum. Interval scales have equal distances between categories to allow comparisons, and ratio scales have a true zero point to enable calculations of ratios. The document provides examples of different types of variables that can be measured on each scale, such as qualitative vs. quantitative variables. It also discusses techniques for developing scales, such as rating and ranking scales.
Entity Relationship Model
The entity-relationship (E-R) model is a way to graphically represent entities and relationships between entities to create a database. The E-R model employs three basic concepts: entity sets, relationship sets, and attributes. An entity set is a set of entities of the same type that share attributes. Attributes are descriptive properties of each member of an entity set. There are different types of attributes including simple/composite, single/multi-valued, derived, key, and null attributes.
Introduction to Statistics and Arithmetic Mean
This document provides an overview of probability and statistics concepts. It defines statistics as the science dealing with the collection, presentation, analysis, and interpretation of numerical data. Descriptive statistics are used to summarize and describe data through graphical and numerical methods like finding the maximum, minimum, and average of a data set. There are different types of data (qualitative, quantitative) and measurement scales (nominal, ordinal, interval, ratio) used in statistics. Measures of central tendency like the average/mean are used to represent an entire data set with a single value.
Data Types
This document discusses different types of data in statistics and R. It describes descriptive statistics, which involves collecting, presenting, and describing data, and inferential statistics, which draws conclusions about populations based on sample data. It then defines qualitative, quantitative, discrete, continuous, and time series data. Finally, it outlines the different data types that can be used in R, including numeric, integer, character, factor, and logical data as well as dates and times.
er-models.pptx
The document discusses data modeling and the entity-relationship (ER) model. It defines key concepts like the ER model, entities, attributes, relationships and keys. The ER model is used to develop a conceptual design for a database through entity-relationship diagrams. These diagrams show entities, attributes and relationships. Entities can have primary keys, foreign keys and other types of keys to uniquely identify records. The ER model provides a high-level view of data that is later mapped to relational database schemas.
FDS PPT_Unit-5.pptx fundamentals of data science
This document outlines different types of data attributes including nominal, binary, ordinal, interval-scaled, ratio-scaled, discrete and continuous attributes. It discusses measuring central tendency using the mean, median and mode of data and measuring dispersion. Data objects and their attributes in databases and how they are represented as rows and columns are also covered. Basic statistical descriptions of data are important for data preprocessing.
Introduction to Data (1).pptx
Brief introduction to Data and its types.
There are different types of data in Statistics, that are collected, analysed, interpreted and presented. The data are the individual pieces of factual information recorded, and it is used for the purpose of the analysis process. The two processes of data analysis are interpretation and presentation. Statistics are the result of data analysis. Data classification and data handling are important processes as it involves a multitude of tags and labels to define the data, its integrity and confidentiality. In this article, we are going to discuss the different types of data in statistics in detail.
The data is classified into majorly four categories:
Nominal data
Ordinal data
Discrete data
Continuous data
Qualitative or Categorical Data
Qualitative data, also known as the categorical data, describes the data that fits into the categories. Qualitative data are not numerical. The categorical information involves categorical variables that describe the features such as a person’s gender, home town etc. Categorical measures are defined in terms of natural language specifications, but not in terms of numbers.
Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense. Examples of the categorical data are birthdate, favourite sport, school postcode. Here, the birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.
Nominal Data
Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value. Nominal data is also called the nominal scale. It cannot be ordered and measured. But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender etc.
The nominal data are examined using the grouping method. In this method, the data are grouped into categories, and then the frequency or the percentage of the data can be calculated. These data are visually represented using the pie charts.
Ordinal Data
Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined. This variable is mostly found in surveys, finance, economics, questionnaires, and so on.
The ordinal data is commonly represented using a bar chart. These data are investigated and interpreted through many visualisation tools. The information may be expressed using tables in which each row in the table shows the distinct category.
Quantitative or Numerical Data
Quantitative data is also known as numerical data which represents the numerical value (i.e., how much, how often, how many). Numerical data gives information about the quantities of a specific thing. Some examples of numerical data are height, length, size, weight, and so on. The quantitative data can be classified into two different types based on the data
Data modeling
The document discusses Relational Database Management Systems (RDBMS) and Entity-Relationship (ER) modeling. It describes the key components of an ER diagram including entities, attributes, relationships and relationship types. It provides examples of how to model real-world systems, such as modeling students and addresses in a school database. It also discusses entity keys like primary keys and foreign keys and how they are used to uniquely identify records and link tables in a relational database.
data processing.pdf
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling and analysis. The document discusses several key aspects of data preprocessing including:
- Why data preprocessing is important to improve data quality and ensure accurate analysis results.
- Common data issues like missing values, noise, inconsistencies that require cleaning. Techniques for cleaning include filling in missing data, identifying and handling outliers, and resolving inconsistencies.
- Methods for reducing data like binning, regression, clustering, sampling to obtain a smaller yet representative version of the data.
- The major tasks in preprocessing like data cleaning, integration, transformation, reduction and discretization which are aimed at handling real-world data issues.
DATA MODEL PRESENTATION UNIT I-BCA I.pptx
The document discusses different types of data models including conceptual, physical, and implementation models. It describes key aspects of data models such as their structure, constraints, and operations. Specific models covered include the entity-relationship model, network model, object-oriented model, and relational model. Key components of the entity-relationship model like entities, attributes, relationships, and ER diagrams are defined. The network and object-oriented models are also briefly explained.
Data Models & Introduction to UML
The importance of data models, Basic building blocks, Business rules, The evolution of data models, Degrees of data abstraction
Database design and Introduction to UML
Unit-1-DBMS-SUN-4 everything you need to know.pptx
This document provides an introduction to database management systems (DBMS) and structured query language (SQL). It defines key DBMS concepts like data models, database architecture, and SQL data types. The main data models covered are relational, network, and hierarchical. It describes the components of entity relationship (ER) diagrams like entities, attributes, and relationships. It also gives examples of SQL data types and discusses how data types are specified when creating database tables in SQL.
Data Mining DataLecture Notes for Chapter 2Introduc
Data Mining: Data
Lecture Notes for Chapter 2
Introduction to Data Mining
by
Tan, Steinbach, Kumar
What is Data?Collection of data objects and their attributes
An attribute is a property or characteristic of an objectExamples: eye color of a person, temperature, etc.Attribute is also known as variable, field, characteristic, or featureA collection of attributes describe an objectObject is also known as record, point, case, sample, entity, or instance
Attributes
Objects
Attribute ValuesAttribute values are numbers or symbols assigned to an attribute
Distinction between attributes and attribute valuesSame attribute can be mapped to different attribute values Example: height can be measured in feet or meters
Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are integers But properties of attribute values can be different
ID has no limit but age has a maximum and minimum value
Types of Attributes There are different types of attributesNominalExamples: ID numbers, eye color, zip codesOrdinalExamples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short}IntervalExamples: calendar dates, temperatures in Celsius or Fahrenheit.RatioExamples: temperature in Kelvin, length, time, counts
Properties of Attribute Values The type of an attribute depends on which of the following properties it possesses:Distinctness: = Order: < > Addition: + - Multiplication: * /
Nominal attribute: distinctnessOrdinal attribute: distinctness & orderInterval attribute: distinctness, order & additionRatio attribute: all 4 properties
Attribute Type
Description
Examples
Operations
Nominal
The values of a nominal attribute are just different names, i.e., nominal attributes provide only enough information to distinguish one object from another. (=, )
zip codes, employee ID numbers, eye color, sex: {male, female}
mode, entropy, contingency correlation, 2 test
Ordinal
The values of an ordinal attribute provide enough information to order objects. (<, >)
hardness of minerals, {good, better, best},
grades, street numbers
median, percentiles, rank correlation, run tests, sign tests
Interval
For interval attributes, the differences between values are meaningful, i.e., a unit of measurement exists.
(+, - )
calendar dates, temperature in Celsius or Fahrenheit
mean, standard deviation, Pearson's correlation, t and F tests
Ratio
For ratio variables, both differences and ratios are meaningful. (*, /)
temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical current
geometric mean, harmonic mean, percent variation
Attribute Level
Transformation
Comments
Nominal
Any permutation of values
If all employee ID numbers were reassigned, would it make any difference?
Ordinal
An order preserving change of values, i.e.,
new_value = f(old_value)
where f is a monotonic function.
An attribut ...
Chapter 2.pdf
1. Data exploration involves describing data using statistical and visualization techniques in order to identify important aspects for further analysis. It is done before data mining. Types of attributes include nominal, binary, ordinal, and numeric attributes which can be discrete or continuous.
2. Basic statistical descriptions of data include measures of central tendency (mean, median, mode), measuring dispersion (range, quartiles, variance, standard deviation), and graphic displays (histograms, scatter plots). These help identify properties of the data and highlight outliers.
3. The document then provides details on calculating and interpreting various statistical measures like mean, median, mode, range, quartiles, interquartile range, and variance. It also describes plots like quantile
Similar to Datacleaning.ppt (20)
Categorical DataCategorical data represents characteristics..docx
Categorical DataCategorical data represents characteristics..docx
Exploratory Data Analysis Unit 1 ppt presentation.pptx
Exploratory Data Analysis Unit 1 ppt presentation.pptx
Unit-1-DBMS-SUN-4 everything you need to know.pptx
Unit-1-DBMS-SUN-4 everything you need to know.pptx
Data Mining DataLecture Notes for Chapter 2Introduc
Data Mining DataLecture Notes for Chapter 2Introduc
More from amuthadeepa
Edgelinking
This document discusses edge detection and edge linking algorithms. It notes that edge detection identifies pixels on edges but not full edges due to noise. Edge linking then assembles these edge pixels into meaningful edges and boundaries. There are three fundamental approaches to edge linking: local processing which analyzes each pixel's strength and direction, regional processing which links pixels based on regional membership, and requiring specification of starting points and ordering of points to form connected paths for regional boundary approximation.
Handover
This document discusses different types of handovers in UMTS networks. It describes hard handovers, soft handovers, and inter-RAT handovers between technologies like UMTS, GSM, and LTE. Soft handovers allow simultaneous communication between a UE and multiple base stations for a seamless transition. Inter-RAT handovers are needed when changing between different radio access technologies, for example from UMTS to GSM, due to coverage limitations or network capacity issues. Compressed mode and blind handovers are techniques used for handovers between UMTS and GSM networks.
Cookies
Cookies are small files that are stored on a user's computer. Each time the user visits a website, their browser sends the cookie back to the server. PHP allows you to create and retrieve cookie values. Cookies are created using the setcookie() function, which takes parameters like name, value, and expiration. An example shows setting a cookie called "user" with a value of "John Doe" that expires in 30 days, and then retrieving the cookie value.
Critical system
The document discusses system reliability and ensuring it through reliability growth modeling. It explains how safety cases are used to validate safety and make formal arguments that critical systems meet dependability requirements. The costs of failure for critical systems are significantly higher, so reliability modeling and safety validation are important to remove faults and prevent costly system failures.
Excellencein visualization
This document discusses data visualization in the context of big data. It notes that data visualization is important for making sense of large datasets and gaining insights. However, visualizing big data presents challenges related to scalability, heterogeneity, and speed. Effective visualization of big data requires tools that can handle its scale and complexity through techniques like cloud computing and advanced user interfaces. The document also provides examples of different visualization techniques like word clouds and line charts that can be used to display different data types.
Protocol.ppt
This document discusses authentication protocols used for validating user identity when accessing resources. It describes the Challenge Handshake Authentication Protocol (CHAP) and Password Authentication Protocol (PAP) in detail. CHAP is more secure than PAP as the password is never sent online. It uses a three-way handshake involving a challenge value. PAP requires the user to enter username and password in clear text. The document also outlines the Point to Point Protocol (PPP) framework, including its frame format, use of the Link Control Protocol (LCP) to establish and configure the link, and how different network control protocols can carry network layer data over PPP.
Datacleaning.ppt
The document discusses various types of data quality issues that may be present in datasets, including invalid values, different data formats, attribute dependencies, lack of uniqueness, missing values, misspellings, and misfield values. It also presents several techniques that can be used to detect and fix these issues, such as data visualization, outlier analysis, validation code, indicator variables, data binning/bucketing, centering and scaling data, and other techniques like grouping outliers. The overall goal is to clean raw data and ensure it is of high quality before use in analysis.
Database.ppt
The document discusses different forms of database normalization including 1st normal form (1NF), 2nd normal form (2NF), 3rd normal form (3NF), and Boyce-Codd normal form (BCNF). It explains that normalization is the process of reducing data redundancy and improving data integrity by organizing data in tables and eliminating anomalies like insertion, update, and deletion anomalies. The document provides examples to illustrate how data can be normalized to each form through decomposition of tables and attributes.
Network security.ppt
The document discusses various types of network security devices. It describes active devices like firewalls and antivirus software that block unwanted traffic. Passive devices like intrusion detection appliances identify and report unwanted traffic. Preventative devices scan networks to identify potential security issues. Unified threat management devices act as all-in-one security solutions. Specific devices discussed in more detail include firewalls, antivirus software, content filtering, and intrusion detection systems.
Smart.ppt
The document discusses various algorithms and data structures. It describes search, sort, insert, update, and delete algorithms. It then discusses hash tables and how hashing works to store key-value pairs in an array. Graphs can be represented using arrays of vertices and edges. AVL trees use rotations like left, right, left-right and right-left to maintain balance during insertions and deletions.
Perceptron.ppt
The document provides an introduction to the perceptron model. It discusses how the perceptron was originally invented in 1958 as a machine for image recognition, with an array of photocells randomly connected to neurons. Weights were encoded using potentiometers, and weight updates during learning were performed by electric motors. It then discusses how multiple perceptrons can be combined to solve non-linearly separable problems like XOR. Finally, it provides details on perceptron weight calculation and the use of an activation function to produce the output in a nonlinear way similar to biological neurons.
More from amuthadeepa (12)
Recently uploaded
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityAkanksha trivedi rama nursing college kanpur.
Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcHow to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
clinical examination of hip joint (1).pdf
described clinical examination all orthopeadic conditions .
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.How to Add Chatter in the odoo 17 ERP Module
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdDigital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaRecently uploaded (20)
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Datacleaning.ppt
- 1. S.PARANIPUSHPA DEPARTMENT OF CS AND IT NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE
- 2. Invalid values: Some datasets have well-known values, e.g. gender must only have “F” (Female) and “M” (Male). In this case it’s easy to detect wrong values. Formats: The most common issue. It’s possible to get values in different formats like a name written as “Name, Surname” or “Surname, Name”. Attribute dependencies: When the value of a feature depends on the value of another feature. For example, if we have some school data, the “number of students” is related to whether the person “is teacher?”. If someone is not a teacher he/she can’t have any students.
- 3. Uniqueness: It’s possible to find repeated data in features that only allow unique values. For example, we can’t have two products with the same identifier. Missing values: Some features in the dataset may have blank or null values. Misspellings: Incorrectly written values. Misfielded values: When a feature contains the values of another.
- 5. Visualisation: Visualising all the values of each feature, or taking a random sample to see if it’s right. Outlier analysis: Analysing if data can be a human error. E.g. a 300 year old person in the “age” feature. Validation code: It’s possible to create a code that checks if the data is right. For example, in uniqueness, checking if the length of the data is the same as the length of the vector of unique values. We can apply many methods to fix the different
- 6. Indicator variables: This technique converts categorical data into boolean values by creating indicator variables. If we have more than two values (n) we have to create n-1 columns.
- 7. Data Binning or Bucketing: A pre-processing technique used to reduce the effects of minor observation errors. The sample is divided into intervals and replaced by categorical values.
- 8. Centering & Scaling: We can Centre the data of one feature by substracting the mean to all values. To scale the data, we should divide the centered feature by the standard deviation:
- 9. Other techniques: For example, we can group the outliers with the same value or replace the value with the number of times that it appears in the feature:
- 11. THANK YOU