Students usually face a lot of issues completing their data mining assignments. Assignment Achievers are here to help you release your burden and guide you with the best tips.
Business Intelligence and Analytics Unit-2 part-A .pptxRupaRani28
ย
This document provides an overview of data mining, including its definition, process, applications, and challenges. Data mining involves analyzing large datasets to extract useful patterns and trends. It has several key steps: data is collected and loaded into warehouses, analysts determine how to organize it, software sorts and organizes the data, and it is presented to end users. Data mining is used by organizations in retail, finance, marketing and other industries to determine customer preferences and behaviors to help with decisions. While powerful, data mining also faces challenges to do with performance, data issues, and selecting the right techniques.
Decoding the Role of a Data Engineer.pdfDatavalley.ai
ย
A data engineer is a crucial player in the field of big data. They are responsible for designing, building, and maintaining the systems that manage and process vast amounts of data. This requires a unique combination of technical skills, including programming, database management, and data warehousing. The goal of a data engineer is to turn raw data into valuable insights and information that can be used to support decision-making and drive business outcomes.
Prescriptive analytics is the process of analyzing data to provide recommendations on how to optimize business practices based on multiple predicted outcomes. It is the third and final tier of modern data processing, after descriptive analytics which analyzes current data, and predictive analytics which predicts future behavior based on models. Prescriptive analytics utilizes machine learning, business rules, AI and algorithms to simulate various approaches to numerous outcomes and suggest the best possible actions. Data mining is the process of analyzing raw data to identify patterns and extract useful information that can help companies improve marketing strategies and sales. Process mining involves analyzing event logs from enterprise systems to understand processes and identify inefficiencies.
Data analytics refers to the broad field of using data and tools to make business decisions, while data analysis is a subset that refers to specific actions within the analytics process. Data analysis involves collecting, manipulating, and examining past data to gain insights, while data analytics takes the analyzed data and works with it in a meaningful way to inform business decisions and identify new opportunities. Both are important, with data analysis providing understanding of what happened in the past and data analytics enabling predictions about what will happen in the future.
Study of Data Mining Methods and its ApplicationsIRJET Journal
ย
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
This document provides an overview of business intelligence and its key components. It defines business intelligence as processes, technologies, and tools that help transform data into knowledge and plans to guide business decisions. The key components discussed include data mining, data warehousing, and data analysis. Data mining involves extracting patterns from large databases, data warehousing focuses on data storage, and data analysis is the process of inspecting, cleaning, transforming, and modeling data to support decision making.
This document provides an overview of database concepts and information management systems. It discusses topics such as database definition, data warehousing, data mining, centralized vs distributed processing, security issues, and technical solutions for privacy protection. Databases are organized collections of data that allow for storage, retrieval and use of related information. Data warehousing involves integrating data from multiple sources to support decision making. Data mining is the process of extracting patterns and useful information from large datasets. Security measures like access control, encryption and backups are important for protecting information.
Business Intelligence and Analytics Unit-2 part-A .pptxRupaRani28
ย
This document provides an overview of data mining, including its definition, process, applications, and challenges. Data mining involves analyzing large datasets to extract useful patterns and trends. It has several key steps: data is collected and loaded into warehouses, analysts determine how to organize it, software sorts and organizes the data, and it is presented to end users. Data mining is used by organizations in retail, finance, marketing and other industries to determine customer preferences and behaviors to help with decisions. While powerful, data mining also faces challenges to do with performance, data issues, and selecting the right techniques.
Decoding the Role of a Data Engineer.pdfDatavalley.ai
ย
A data engineer is a crucial player in the field of big data. They are responsible for designing, building, and maintaining the systems that manage and process vast amounts of data. This requires a unique combination of technical skills, including programming, database management, and data warehousing. The goal of a data engineer is to turn raw data into valuable insights and information that can be used to support decision-making and drive business outcomes.
Prescriptive analytics is the process of analyzing data to provide recommendations on how to optimize business practices based on multiple predicted outcomes. It is the third and final tier of modern data processing, after descriptive analytics which analyzes current data, and predictive analytics which predicts future behavior based on models. Prescriptive analytics utilizes machine learning, business rules, AI and algorithms to simulate various approaches to numerous outcomes and suggest the best possible actions. Data mining is the process of analyzing raw data to identify patterns and extract useful information that can help companies improve marketing strategies and sales. Process mining involves analyzing event logs from enterprise systems to understand processes and identify inefficiencies.
Data analytics refers to the broad field of using data and tools to make business decisions, while data analysis is a subset that refers to specific actions within the analytics process. Data analysis involves collecting, manipulating, and examining past data to gain insights, while data analytics takes the analyzed data and works with it in a meaningful way to inform business decisions and identify new opportunities. Both are important, with data analysis providing understanding of what happened in the past and data analytics enabling predictions about what will happen in the future.
Study of Data Mining Methods and its ApplicationsIRJET Journal
ย
This document discusses data mining methods and their applications. It begins by defining data mining as the process of extracting useful patterns from large amounts of data. The document then outlines the typical steps in the knowledge discovery process, including data selection, preprocessing, transformation, mining, and evaluation. It classifies data mining techniques into predictive and descriptive methods. Specific techniques discussed include classification, clustering, prediction, and association rule mining. Finally, the document discusses applications of data mining in fields like healthcare, biology, retail, and banking.
This document provides an overview of business intelligence and its key components. It defines business intelligence as processes, technologies, and tools that help transform data into knowledge and plans to guide business decisions. The key components discussed include data mining, data warehousing, and data analysis. Data mining involves extracting patterns from large databases, data warehousing focuses on data storage, and data analysis is the process of inspecting, cleaning, transforming, and modeling data to support decision making.
This document provides an overview of database concepts and information management systems. It discusses topics such as database definition, data warehousing, data mining, centralized vs distributed processing, security issues, and technical solutions for privacy protection. Databases are organized collections of data that allow for storage, retrieval and use of related information. Data warehousing involves integrating data from multiple sources to support decision making. Data mining is the process of extracting patterns and useful information from large datasets. Security measures like access control, encryption and backups are important for protecting information.
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
ย
Data Analytics has emerged has one of the central aspects of business operations. Consequently, the quest to grab professional positions within the Data Analytics domain has assumed unimaginable proportions. So if you too happen to be someone who is desirous of making through a Data Analyst .
This document provides an overview of big data, including its definition, characteristics, categories, sources, storage, analytics, challenges and opportunities. Big data is large and complex datasets that are difficult to process using traditional database management tools. It is characterized by the 5 V's - volume, variety, velocity, value and veracity. Big data comes from both internal and external sources and can be structured, unstructured or semi-structured. It requires specialized storage technologies like Hadoop and NoSQL databases. Analytics on big data uses techniques like machine learning, regression analysis and social network analysis to gain insights. The growth of big data presents both challenges in processing diverse and voluminous data as well as opportunities to generate value.
Data mining involves analyzing large datasets to discover patterns and extract useful information. It has evolved from early methods like regression analysis and involves techniques from machine learning, statistics, and databases. Data mining is used for applications like market analysis, fraud detection, customer retention, and science exploration by performing descriptive tasks like frequent pattern mining and associations or classification/prediction tasks. It involves preprocessing data, extracting patterns, and evaluating and presenting results.
The document provides an overview of key concepts in data science including data types, the data value chain, and big data. It defines data science as extracting insights from large, diverse datasets using tools like machine learning. The data value chain involves acquiring, processing, analyzing and using data. Big data is characterized by its volume, velocity and variety. Common techniques for big data analytics include data mining, machine learning and visualization.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
ย
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
Data mining is the process of analyzing data from different perspectives to extract useful information that can be used to increase revenue or reduce costs. It allows companies to analyze large datasets to find relationships between internal factors like price and staffing and external factors like customer demographics and economic indicators. Data mining software extracts, transforms, and loads transaction data into a data warehouse where it can then be stored, accessed, analyzed using various techniques, and presented in a useful format to users. Common techniques include classification, clustering, association rule mining, and sequential pattern analysis.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
MS SQL SERVER: Introduction To Datamining Suing Sql Serversqlserver content
ย
Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and web log analysis.
MS Sql Server: Introduction To Datamining Suing Sql ServerDataminingTools Inc
ย
Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and analyzing web server logs.
The document provides an overview of data analysis. It discusses the core components of data analysis including descriptive, diagnostic, predictive, prescriptive, and cognitive analysis. It describes the roles of a data analyst including preparing, modeling, visualizing, analyzing, and managing data. The tasks of a data analyst are preparing data, modeling the data, visualizing results, analyzing the visualizations, and managing the information. Descriptive statistics, Excel, and Power BI are highlighted as important tools for data analysts. The document is an introductory lecture on data analysis concepts and the data analyst's job.
This document provides an overview of big data, including its key characteristics and importance. It discusses the volume, velocity, variety, veracity, variability, visualization, value, and operations/tools associated with big data. Big data refers to large amounts of structured and unstructured data that require innovative processing to gain insights. It is important because it enables cost reduction, time reduction, new product development, optimized offerings, and smart decision making through analysis of data from any source.
This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
The document discusses enhancing security in data mining through integrating cryptographic and data mining algorithms. It proposes hiding sensitive data within images using a Hill cipher algorithm before storing it in a database. A data mining iterative algorithm would then extract the hidden information from the encrypted images. This approach aims to protect sensitive data from unauthorized access, such as from man-in-the-middle attacks, by only revealing encrypted images even if an attack is successful. The methodology, research design, and results of encrypting data into images and then extracting the information using data mining techniques are described.
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
Data Mining is the process of discovering new correlations, patterns, and trends by digging into (mining) large amounts of data stored in warehouses, using artificial intelligence, statistical and mathematical techniques. Data mining can also be defined as the process of extracting knowledge hidden from large volumes of raw data i.e. the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The alternative name of Data Mining is Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, etc.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!Polestar Solutions
ย
Empower your organization with the right analytics approachโGuided Analytics or Self-Service Business Intelligence (BI)โto unlock the true potential of your data. Discover the benefits and find your perfect fit, whether you prefer expert-guided insights or self-exploration, enabling your team to make data-driven decisions and drive transformative outcomes.
The document provides an overview of data warehousing, decision support, online analytical processing (OLAP), and data mining. It discusses what data warehousing is, how it can help organizations make better decisions by integrating data from various sources and making it available for analysis. It also describes OLAP as a way to transform warehouse data into meaningful information for interactive analysis, and lists some common OLAP operations like roll-up, drill-down, slice and dice, and pivot. Finally, it gives a brief introduction to data mining as the process of extracting patterns and relationships from data.
Data analytics and visualization tools are increasingly being used in accounting and auditing to analyze large datasets, identify anomalies, and detect fraud. Descriptive, diagnostic, predictive, and prescriptive analytics help analyze financial and operational data. Techniques like regression analysis, decision trees, and clustering can be used to identify patterns and predict outcomes. AI is also being applied through automation, contract analysis, and machine learning algorithms to process data and transactions at large scale. Internal audits now leverage analytics to examine 100% of data rather than just samples, improving fraud detection.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
ย
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
ย
Data Analytics has emerged has one of the central aspects of business operations. Consequently, the quest to grab professional positions within the Data Analytics domain has assumed unimaginable proportions. So if you too happen to be someone who is desirous of making through a Data Analyst .
This document provides an overview of big data, including its definition, characteristics, categories, sources, storage, analytics, challenges and opportunities. Big data is large and complex datasets that are difficult to process using traditional database management tools. It is characterized by the 5 V's - volume, variety, velocity, value and veracity. Big data comes from both internal and external sources and can be structured, unstructured or semi-structured. It requires specialized storage technologies like Hadoop and NoSQL databases. Analytics on big data uses techniques like machine learning, regression analysis and social network analysis to gain insights. The growth of big data presents both challenges in processing diverse and voluminous data as well as opportunities to generate value.
Data mining involves analyzing large datasets to discover patterns and extract useful information. It has evolved from early methods like regression analysis and involves techniques from machine learning, statistics, and databases. Data mining is used for applications like market analysis, fraud detection, customer retention, and science exploration by performing descriptive tasks like frequent pattern mining and associations or classification/prediction tasks. It involves preprocessing data, extracting patterns, and evaluating and presenting results.
The document provides an overview of key concepts in data science including data types, the data value chain, and big data. It defines data science as extracting insights from large, diverse datasets using tools like machine learning. The data value chain involves acquiring, processing, analyzing and using data. Big data is characterized by its volume, velocity and variety. Common techniques for big data analytics include data mining, machine learning and visualization.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
ย
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
Data mining is the process of analyzing data from different perspectives to extract useful information that can be used to increase revenue or reduce costs. It allows companies to analyze large datasets to find relationships between internal factors like price and staffing and external factors like customer demographics and economic indicators. Data mining software extracts, transforms, and loads transaction data into a data warehouse where it can then be stored, accessed, analyzed using various techniques, and presented in a useful format to users. Common techniques include classification, clustering, association rule mining, and sequential pattern analysis.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
MS SQL SERVER: Introduction To Datamining Suing Sql Serversqlserver content
ย
Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and web log analysis.
MS Sql Server: Introduction To Datamining Suing Sql ServerDataminingTools Inc
ย
Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and analyzing web server logs.
The document provides an overview of data analysis. It discusses the core components of data analysis including descriptive, diagnostic, predictive, prescriptive, and cognitive analysis. It describes the roles of a data analyst including preparing, modeling, visualizing, analyzing, and managing data. The tasks of a data analyst are preparing data, modeling the data, visualizing results, analyzing the visualizations, and managing the information. Descriptive statistics, Excel, and Power BI are highlighted as important tools for data analysts. The document is an introductory lecture on data analysis concepts and the data analyst's job.
This document provides an overview of big data, including its key characteristics and importance. It discusses the volume, velocity, variety, veracity, variability, visualization, value, and operations/tools associated with big data. Big data refers to large amounts of structured and unstructured data that require innovative processing to gain insights. It is important because it enables cost reduction, time reduction, new product development, optimized offerings, and smart decision making through analysis of data from any source.
This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
The document discusses enhancing security in data mining through integrating cryptographic and data mining algorithms. It proposes hiding sensitive data within images using a Hill cipher algorithm before storing it in a database. A data mining iterative algorithm would then extract the hidden information from the encrypted images. This approach aims to protect sensitive data from unauthorized access, such as from man-in-the-middle attacks, by only revealing encrypted images even if an attack is successful. The methodology, research design, and results of encrypting data into images and then extracting the information using data mining techniques are described.
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
Data Mining is the process of discovering new correlations, patterns, and trends by digging into (mining) large amounts of data stored in warehouses, using artificial intelligence, statistical and mathematical techniques. Data mining can also be defined as the process of extracting knowledge hidden from large volumes of raw data i.e. the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The alternative name of Data Mining is Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, etc.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!Polestar Solutions
ย
Empower your organization with the right analytics approachโGuided Analytics or Self-Service Business Intelligence (BI)โto unlock the true potential of your data. Discover the benefits and find your perfect fit, whether you prefer expert-guided insights or self-exploration, enabling your team to make data-driven decisions and drive transformative outcomes.
The document provides an overview of data warehousing, decision support, online analytical processing (OLAP), and data mining. It discusses what data warehousing is, how it can help organizations make better decisions by integrating data from various sources and making it available for analysis. It also describes OLAP as a way to transform warehouse data into meaningful information for interactive analysis, and lists some common OLAP operations like roll-up, drill-down, slice and dice, and pivot. Finally, it gives a brief introduction to data mining as the process of extracting patterns and relationships from data.
Data analytics and visualization tools are increasingly being used in accounting and auditing to analyze large datasets, identify anomalies, and detect fraud. Descriptive, diagnostic, predictive, and prescriptive analytics help analyze financial and operational data. Techniques like regression analysis, decision trees, and clustering can be used to identify patterns and predict outcomes. AI is also being applied through automation, contract analysis, and machine learning algorithms to process data and transactions at large scale. Internal audits now leverage analytics to examine 100% of data rather than just samples, improving fraud detection.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
ย
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
ย
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
ย
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
ย
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
ย
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the bodyโs response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
4. Types of Data Mining
Descriptive Data Mining Analysis
Descriptive data mining analysis
involves the process if converting
provided data into useful information.
Descriptive Data mining is further
classified into summarization analysis,
sequence discovery analysis, association
rules analysis and clustering analysis.
5. Predictive Data Mining Analysis
As the name manifest, predictive data
mining is the analysis of all the data that
can be helpful to predict the future
outcomes.It is further classified into
regression analysis, prediction analysis,
time-serious analysis and classification
analysis.
6. Elements of Data Mining:
Extraction, Transformation and Loading
Data mining extracts, transform and
loads the data towards data warehouse
system.
Storing and Managing data
It stores and manages the extracted
data in a multidimensional data system.
7. Data Access
It provides data access to information
technology professionals and data
analysts so to work further.
Data Analyzing
It analyses the data through the
application software and present data
into useful structure.
8. Conclusion
Data mining is the process which helps
the businesses to analyze consumer's
behaviour and their insights. It also
enhances the quality of decisions and
improves security. It is considered as
one of the most effective ways to
improve forecasting and planning.
9. Contact Us
+61 280 113 341
support@assignmentachievers.com
www.assignmentachievers.com
Need assistance
with your Data Mining
Assignments?
We are here to assist
you!!