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Research of Data Analysis and Different Types of Analysis

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Research of Data Analysis and Different Types of Analysis essay

Table of contents

  1. Abstract
  2. Introduction
  3. Objectives
  4. Types Of Anaysis
  5. Data requirements
    Data processing
    Data cleaning
  6. Conclusion
  7. References


Data analysis is known as ‘analysis of data ‘or ‘data analytics’, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focus on modeling and knowledge discovery for predictive rather than purely descriptive process, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovery new features in the data CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistics, and structural techniques to extract and classify information from textual sources, a species of data. All are varieties of data analysis. Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling.


The process of converting raw data into information starts with data processing and continues to data analysis. The analysis involves using statistical techniques to order data with objective of obtaining answers to research questions. Analysis can be viewed as the ordering, the breaking down into constituent parts, and the manipulation of data to obtain answers to the research question or questions underlying the survey project. Analysis is followed by interpretation of research results by using the output of analysis to make inference and draw conclusion about the relationships. Analysis of data is done using a careful plan, developed by an open-minded and flexible analyst.

Good, Bar and Scats have listed four modes to get started on analysis the gathered data

  1. To think in terms of significant tables that the data permit.
  2. To examine carefully the statement of problem and earlier analysis and to study the original records of data.
  3. To get away from the data to think about the problem in layman’s terms or to actually discuss the problems with others.
  4. To attack the data by making various statistical calculations. Any of these approaches can be used to start analysis of data. The data analysis strategy is influenced by factors like the type of data, the research design researcher’s qualifications and assumptions underlying a statistical technique.


In a study involved with planning for the future, framing the issues through problem identification and realistic goals and objectives is critical. How problems are framed shapes the nature of the solutions and the criteria upon which those solutions will be judged. The purposes of this section are to identify goals and objectives for East Anchorage’s future transportation system, to help ensure that the future transportation system will facilitate our achievement of those goals. This section outlines the existing goals and objectives guiding transportation improvements and planning at the federal, state, and local levels.

Types Of Anaysis

Quantitative data are anything that can be expressed as a number, or quantified. Examples of quantitative data are scores on achievement tests, numbers of hours of study, or weight of a subject. These data may represented by ordinal, interval, or ratio scales and lend themselves to most statistical manipulation.

Qualitative data cannot be expressed as a number. Data that represent nominal scales such as gender, socieo economic status, religious preference are usually considered to be qualitative data. The process of data analysis Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories Statistician John Tukey defined data analysis in 1961 as:”Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data. There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases.

Data requirements

The data is necessary as inputs to the analysis are specified based upon the requirements of those directing the analysis or customers who will use the finished product of the analysis. The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). Data collection Data is collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.

Data processing

The phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data analysis.Data initially obtained must be processed or organized for analysis. For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software.

Data cleaning

Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, duplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable. Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct.


Now a days we will not able to live data analysis. Because in every field we must need variety types of analysis . Which will helps as very much. This data analysis helps in economical field, business field, statistical field ..etcThe statistical techniques in the data analysis is help to order the objective of obtaining answers. Through this analysis we will got good and accurate result.


1. Research methodology (Shashi K. Gupta , Praneet Rangi)

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