File Name: data processing and analysis .zip
- Data processing
- Data Collection, Processing and Analysis
- Data Processing
- Quantitative Data: Definition, Types, Analysis and Examples
Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making. Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same.
Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making.
Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same. The data required for analysis is based on a question or an experiment.
Based on the requirements of those directing the analysis, the data necessary as inputs to the analysis is identified e. Specific variables regarding a population e. Data may be numerical or categorical. Data Collection is the process of gathering information on targeted variables identified as data requirements. The emphasis is on ensuring accurate and honest collection of data. Data Collection ensures that data gathered is accurate such that the related decisions are valid.
Data Collection provides both a baseline to measure and a target to improve. Data is collected from various sources ranging from organizational databases to the information in web pages. The data thus obtained, may not be structured and may contain irrelevant information.
Hence, the collected data is required to be subjected to Data Processing and Data Cleaning. The data that is collected must be processed or organized for analysis.
This includes structuring the data as required for the relevant Analysis Tools. For example, the data might have to be placed into rows and columns in a table within a Spreadsheet or Statistical Application. A Data Model might have to be created. The processed and organized data may be incomplete, contain duplicates, or contain errors. Data Cleaning is the process of preventing and correcting these errors. There are several types of Data Cleaning that depend on the type of data.
For example, while cleaning the financial data, certain totals might be compared against reliable published numbers or defined thresholds. Likewise, quantitative data methods can be used for outlier detection that would be subsequently excluded in analysis. Data that is processed, organized and cleaned would be ready for the analysis.
Various data analysis techniques are available to understand, interpret, and derive conclusions based on the requirements. Data Visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data. Statistical Data Models such as Correlation, Regression Analysis can be used to identify the relations among the data variables. These models that are descriptive of the data are helpful in simplifying analysis and communicate results.
The process might require additional Data Cleaning or additional Data Collection, and hence these activities are iterative in nature. The results of the data analysis are to be reported in a format as required by the users to support their decisions and further action. The feedback from the users might result in additional analysis. The data analysts can choose data visualization techniques, such as tables and charts, which help in communicating the message clearly and efficiently to the users.
The analysis tools provide facility to highlight the required information with color codes and formatting in tables and charts. Data Analysis - Process Advertisements. Previous Page. Next Page. Previous Page Print Page.
Data Collection, Processing and Analysis
Acquiring data : Acquisition involves collecting or adding to the data holdings. There are several methods of acquiring data:. Data processing: A series of actions or steps performed on data to verify, organize, transform, integrate, and extract data in an appropriate output form for subsequent use. Methods of processing must be rigorously documented to ensure the utility and integrity of the data. Data Analysis involves actions and methods performed on data that help describe facts, detect patterns, develop explanations and test hypotheses.
The processing of data and further analysis may be break up into three stages: (1) data stage, the process of exploratory data analysis and data cleaning are typically iterative. Data%20 · Processing%alpost103.org 6.
Data processing is, generally, "the collection and manipulation of items of data to produce meaningful information. The term Data Processing DP has also been used to refer to a department within an organization responsible for the operation of data processing applications. The United States Census Bureau history illustrates the evolution of data processing from manual through electronic procedures.
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future.
Data Processing: Made Simple, Second Edition presents discussions of a number of trends and developments in the world of commercial data processing. The book covers the rapid growth of micro- and mini-computers for both home and office use; word processing and the 'automated office'; the advent of distributed data processing; and the continued growth of database-oriented systems. The text also discusses modern digital computers; fundamental computer concepts; information and data processing requirements of commercial organizations; and the historical perspective of the computer industry.
Data processing is the conversion of data into usable and desired form. Most of the processing is done by using computers and other data processing devices, and thus done automatically.
Quantitative Data: Definition, Types, Analysis and Examples
Without data processing, companies limit their access to the very data that can hone their competitive edge and deliver critical business insights. Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output. Data processing starts with data in its raw form and converts it into a more readable format graphs, documents, etc. Read Now.
Data processing is the collecting and manipulation of data into the usable and desired form. The manipulation is nothing but processing, which is carried either manually or automatically in a predefined sequence of operations. The next point is converting to the desired form, the collected data is processed and converted to the desired form according to the application requirements, that means converting the data into useful information which could use in the application to perform some task. The Input of the processing is the collection of data from different sources like text file data, excel file data, database, even unstructured data like images, audio clips, video clips, GPRS data, and so on.
analysis as data analysis is primarily non-statistical. The standard guidelines used for coding are given below: Use numbers to represent.
Home Consumer Insights Market Research. Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. This data is any quantifiable information that can be used for mathematical calculations and statistical analysis, such that real-life decisions can be made based on these mathematical derivations. This data can be verified and can also be conveniently evaluated using mathematical techniques.
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