Cleaning and editing of data involves reviewing collected data to remove errors and inconsistencies. This includes checking for missing answers, answers in the wrong place, or logically inconsistent responses. Coding converts qualitative data into quantitative data by assigning numerical values to response choices. It allows large amounts of information to be reduced to an easily handled form. Both cleaning and coding improve the quality and accuracy of collected data.
A ROBUST APPROACH FOR DATA CLEANING USED BY DECISION TREEijcsa
Now a day’s every second trillion of bytes of data is being generated by enterprises especially in internet.To achieve the best decision for business profits, access to that data in a well-situated and interactive way is always a dream of business executives and managers. Data warehouse is the only viable solution that can bring the dream into veracity. The enhancement of future endeavours to make decisions depends on the availability of correct information that is based on quality of data underlying. The quality data can only be produced by cleaning data prior to loading into data warehouse since the data collected from different sources will be dirty. Once the data have been pre-processed and cleansed then it produces accurate results on applying the data mining query. Therefore the accuracy of data is vital for well-formed and reliable decision making. In this paper, we propose a framework which implements robust data quality to ensure consistent and correct loading of data into data warehouses which ensures accurate and reliable data analysis, data mining and knowledge discovery.
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSINGIJCSEIT Journal
The quality is the key concept in each and every analysis as well as in computing applications. Today we
gather large volumes of information and store them in multidimensional mode that is in data warehouses
then analyze the data to be used in exact decision making in various fields. The studies proved that most of
the volumes of data are not useful for analysis due to lack of quality caused by improper data handling
techniques. This paper try to find out a solution to achieve the quality of data from the foundation of data
repositories and try to avoid quality anomalies at meta data level. This paper also proposes the new model
of Meta data architecture.
AUTO-CDD: automatic cleaning dirty data using machine learning techniquesTELKOMNIKA JOURNAL
Cleaning the dirty data has become very critical significance for many years, especially in
medical sectors. This is the reason behind widening research in this sector. To initiate the research, a
comparison between currently used functions of handling missing values and Auto-CDD is presented.
The developed system will guarantee to overcome processing unwanted outcomes in data Analytical
process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that
will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index
values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by
two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved
Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally,
it concludes that this process will help to get clean data for further analytical process.
Data Cleaning Service for Data Warehouse: An Experimental Comparative Study o...TELKOMNIKA JOURNAL
Data warehouse is a collective entity of data from various data sources. Data are prone to several
complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure
data quality. Data cleaning service involves identification of errors, removing them and improve the quality of
data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate
elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology,
involved stages and services within data warehouse environment. It also provides a comparison through
some experiments on local data with different cases, such as different spelling on different pronunciation,
misspellings, name abbreviation, honorific prefixes, common nicknames, splitted name and exact match. All
services are evaluated based on the proposed quality of service metrics such as performance, capability to
process the number of records, platform support, data heterogeneity, and price; so that in the future these
services are reliable to handle big data in data warehouse.
A ROBUST APPROACH FOR DATA CLEANING USED BY DECISION TREEijcsa
Now a day’s every second trillion of bytes of data is being generated by enterprises especially in internet.To achieve the best decision for business profits, access to that data in a well-situated and interactive way is always a dream of business executives and managers. Data warehouse is the only viable solution that can bring the dream into veracity. The enhancement of future endeavours to make decisions depends on the availability of correct information that is based on quality of data underlying. The quality data can only be produced by cleaning data prior to loading into data warehouse since the data collected from different sources will be dirty. Once the data have been pre-processed and cleansed then it produces accurate results on applying the data mining query. Therefore the accuracy of data is vital for well-formed and reliable decision making. In this paper, we propose a framework which implements robust data quality to ensure consistent and correct loading of data into data warehouses which ensures accurate and reliable data analysis, data mining and knowledge discovery.
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSINGIJCSEIT Journal
The quality is the key concept in each and every analysis as well as in computing applications. Today we
gather large volumes of information and store them in multidimensional mode that is in data warehouses
then analyze the data to be used in exact decision making in various fields. The studies proved that most of
the volumes of data are not useful for analysis due to lack of quality caused by improper data handling
techniques. This paper try to find out a solution to achieve the quality of data from the foundation of data
repositories and try to avoid quality anomalies at meta data level. This paper also proposes the new model
of Meta data architecture.
AUTO-CDD: automatic cleaning dirty data using machine learning techniquesTELKOMNIKA JOURNAL
Cleaning the dirty data has become very critical significance for many years, especially in
medical sectors. This is the reason behind widening research in this sector. To initiate the research, a
comparison between currently used functions of handling missing values and Auto-CDD is presented.
The developed system will guarantee to overcome processing unwanted outcomes in data Analytical
process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that
will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index
values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by
two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved
Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally,
it concludes that this process will help to get clean data for further analytical process.
Data Cleaning Service for Data Warehouse: An Experimental Comparative Study o...TELKOMNIKA JOURNAL
Data warehouse is a collective entity of data from various data sources. Data are prone to several
complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure
data quality. Data cleaning service involves identification of errors, removing them and improve the quality of
data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate
elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology,
involved stages and services within data warehouse environment. It also provides a comparison through
some experiments on local data with different cases, such as different spelling on different pronunciation,
misspellings, name abbreviation, honorific prefixes, common nicknames, splitted name and exact match. All
services are evaluated based on the proposed quality of service metrics such as performance, capability to
process the number of records, platform support, data heterogeneity, and price; so that in the future these
services are reliable to handle big data in data warehouse.
A simplified approach for quality management in data warehouseIJDKP
Data warehousing is continuously gaining importance as organizations are realizing the benefits of
decision oriented data bases. However, the stumbling block to this rapid development is data quality issues
at various stages of data warehousing. Quality can be defined as a measure of excellence or a state free
from defects. Users appreciate quality products and available literature suggests that many organization`s
have significant data quality problems that have substantial social and economic impacts. A metadata
based quality system is introduced to manage quality of data in data warehouse. The approach is used to
analyze the quality of data warehouse system by checking the expected value of quality parameters with
that of actual values. The proposed approach is supported with a metadata framework that can store
additional information to analyze the quality parameters, whenever required.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
Real Time Web-based Data Monitoring and Manipulation System to Improve Transl...CSCJournals
The use of the internet technology and web browser capabilities of the internet has provided researchers/scientists with many advantages, which includes but not limited to ease of access, platform independence of computer systems, relatively low cost of web access etc. Hence online collaboration like social networks and information/data exchange among individuals and organizations can now be done seamlessly. In practice, many investigators rely heavily on different data modalities for studying and analyzing their research/study and also for producing quality reports. The lack of coherency and inconsistencies in data sets can dramatically reduce the quality of research data. Thus to prevent loss of data quality and value and provide the needed functionality of data, we have proposed a novel approach as an ad-hoc component for data monitoring and manipulation called RTWebDMM (Real Time Web-based Data Monitoring and Manipulation) system to improve the quality of translational research data. The RTWebDMM is proposed as an auditor, monitor, and explorer for improving the way in which investigators access and interact with the data sets in real time using a web browser. The performance of the proposed approach was evaluated with different data sets from various studies. It is demonstrated that the approach yields very promising results for data quality improvement while leveraging on a web-enabled environment.
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
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 .
A simplified approach for quality management in data warehouseIJDKP
Data warehousing is continuously gaining importance as organizations are realizing the benefits of
decision oriented data bases. However, the stumbling block to this rapid development is data quality issues
at various stages of data warehousing. Quality can be defined as a measure of excellence or a state free
from defects. Users appreciate quality products and available literature suggests that many organization`s
have significant data quality problems that have substantial social and economic impacts. A metadata
based quality system is introduced to manage quality of data in data warehouse. The approach is used to
analyze the quality of data warehouse system by checking the expected value of quality parameters with
that of actual values. The proposed approach is supported with a metadata framework that can store
additional information to analyze the quality parameters, whenever required.
Types of database processing,OLTP VS Data Warehouses(OLAP), Subject-oriented
Integrated
Time-variant
Non-volatile,
Functionalities of Data Warehouse,Roll-Up(Consolidation),
Drill-down,
Slicing,
Dicing,
Pivot,
KDD Process,Application of Data Mining
Real Time Web-based Data Monitoring and Manipulation System to Improve Transl...CSCJournals
The use of the internet technology and web browser capabilities of the internet has provided researchers/scientists with many advantages, which includes but not limited to ease of access, platform independence of computer systems, relatively low cost of web access etc. Hence online collaboration like social networks and information/data exchange among individuals and organizations can now be done seamlessly. In practice, many investigators rely heavily on different data modalities for studying and analyzing their research/study and also for producing quality reports. The lack of coherency and inconsistencies in data sets can dramatically reduce the quality of research data. Thus to prevent loss of data quality and value and provide the needed functionality of data, we have proposed a novel approach as an ad-hoc component for data monitoring and manipulation called RTWebDMM (Real Time Web-based Data Monitoring and Manipulation) system to improve the quality of translational research data. The RTWebDMM is proposed as an auditor, monitor, and explorer for improving the way in which investigators access and interact with the data sets in real time using a web browser. The performance of the proposed approach was evaluated with different data sets from various studies. It is demonstrated that the approach yields very promising results for data quality improvement while leveraging on a web-enabled environment.
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1
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 .
The process of data cleaning involves the process of transformation of data from a raw format to a format that is compatible with your and use case.
Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
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 Cleaning and Preprocessing: Ensuring Data Qualitypriyanka rajput
data cleaning and preprocessing are foundational steps in the data science and machine learning pipelines. Neglecting these crucial steps can lead to inaccurate results, biased models, and erroneous conclusions. By investing time and effort in /data cleaning and preprocessing, data scientists and analysts ensure that their analyses and models are built on a solid foundation of high-quality data.
Optimize Your Healthcare Data Quality Investment: Three Ways to Accelerate Ti...Health Catalyst
Healthcare organizations increasingly rely on data to inform strategic decisions. This growing dependence makes ensuring data across the organization is fit for purpose more critical than ever. Decision-making challenges associated with pandemic-driven urgency, variety of data, and lack of resources have further highlighted the critical importance of healthcare data quality and prompted more focus and investment. However, many data quality initiatives are too narrow in focus and reactive in nature or take longer than expected to demonstrate value. This leaves organizations unprepared for future events, like COVID-19, that require a rapid enterprise-wide analytic response.
What are some actionable ways you can help your organization guard against the data quality challenges uncovered this past year and better prepare to respond in the future? Join Taylor Larsen, Director of Data Quality for Health Catalyst, to learn more.
What You’ll Learn
- How data profiling and data quality assessments, in combination with your data catalog, can increase data quality transparency, expedite root cause analysis, and close data quality monitoring gaps.
- How to leverage AI to reduce data quality monitoring configuration and maintenance time and improve accuracy.
- How defining data quality based on its measurable utility (i.e., data represents information that supports better decisions) can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.
Machine learning topics machine learning algorithm into three main parts.DurgaDeviP2
Machine learning topics
machine learning algorithm into three main parts.
A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.
Similar to Editing, cleaning and coding of data in Business research methodology (20)
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
LA HUG - Video Testimonials with Chynna Morgan - June 2024Lital Barkan
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
1. How to capture video testimonials that convert from your audience 🎥
2. How to leverage your testimonials to boost your sales 💲
3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Premium MEAN Stack Development Solutions for Modern BusinessesSynapseIndia
Stay ahead of the curve with our premium MEAN Stack Development Solutions. Our expert developers utilize MongoDB, Express.js, AngularJS, and Node.js to create modern and responsive web applications. Trust us for cutting-edge solutions that drive your business growth and success.
Know more: https://www.synapseindia.com/technology/mean-stack-development-company.html
What are the main advantages of using HR recruiter services.pdfHumanResourceDimensi1
HR recruiter services offer top talents to companies according to their specific needs. They handle all recruitment tasks from job posting to onboarding and help companies concentrate on their business growth. With their expertise and years of experience, they streamline the hiring process and save time and resources for the company.
VAT Registration Outlined In UAE: Benefits and Requirementsuae taxgpt
Vat Registration is a legal obligation for businesses meeting the threshold requirement, helping companies avoid fines and ramifications. Contact now!
https://viralsocialtrends.com/vat-registration-outlined-in-uae/
2. Simply means that, Information gathered during data collection may be
incomplete, contain errors and lack uniformity.
Example : Data collected through questionnaire and schedules may have many
errors and inconsistencies such as,
Some answers may not be ticked at proper places
some questions may be left unanswered
some questions may be wrongly answered and some answers may be
logically inconsistent
EDITING OF DATA
Data editing is defined as the process involving the review and adjustment of
collected survey data.
3. Data editing is defined as the process involving the review and adjustment of collected data
It is the process of examining the collected survey data to remove remove errors and
inconsistencies.
The purpose of editing is to remove errors and to improve th quality of the collected data
Editing ....continuing
4. Coding is a solution to the data entry issue of research.
It is the process of converting qualitatitve data into quatitative data
coding is translatinng responce choices of a questionaire into numerical values
In coding numbers are assigned to the qualitative attributes of vriables to facilitate data
entry and analysis.
Coding allow the researchers to reduce large quantities of information into an easy handled
form
Example : Attributes of the variable 'Gender' may be codes as '1'for male, '2' for Female & '3'
for Transgender
CODING
Data coding is the process of driving codes from the observed data. In qualitative research the
data is either obtained from observations, interviews or from questionnaires. The purpose of
data coding is to bring out the essence and meaning of the data that respondents have
provided.
5. PRE - CODING : It is the process of assigning codes before going into the field
Coding is done at the time of construction of questionnaire or after it is constructed but
before going into the field for survey
POST- CODING : it is the process of assigning codes after data collection
Here the coding is done after completing the data collection process of the survey
Qualitative data can be converted into quatitative data
Large quantities of information can be reduced into easy handled form (Helpfull for
summarisation)
it helps for computer data entry of the collected data.
It facilitates analysis of data with the help of statistical software
TYPES OF CODING
BENEFITS OF CODING DATA
6. Cleaning of data
Data cleaning, data cleansing, or data scrubbing is the process of improving the
quality of data by correcting inaccurate records from a record set. The term
specifically refers to detecting and modifying, replacing, or deleting incomplete,
incorrect, improperly formatted, duplicated, or irrelevant records, otherwise
referred to as “dirty data,” within a database. Data cleaning also includes
removing duplicated data within a database.
Data provided for communication research often rely on manual data entry,
performed by humans, and therefore are subject to error introduction. Because
of this manual process, the data require cleaning. The need for such cleaning
increases when data come from multiple sources and a standard schema was
not used across sources. The goal of data cleaning is to provide a data ...
7. Removal of Unwanted observations :
Duplicate Observations :
Irrelevant Observations :
Fix Data Structure :
Handle Missing Data :
Improved Decision Making
Revenue Booster
increase productivity
Boost reputation
Data Cleaning Steps
Understanding the what and why behind data cleaning is one, going ahead to implement it is another.
Therefore, this section will be covering the steps involved in data cleaning, and further explanations on how
each of these steps is carried out.
Advantages of Data Cleaning
8. CONCLUSION
Cleaning, coding and editing of data is the different kind of methods that is used to
evalute the datas which is collected by different kind of methods