IndexAbstractIntroductionObjectivesTypes of AnalysisData RequirementsData ProcessingData CleaningConclusionReferencesAbstractData analytics is known as "data analysis" or "data analysis", it is a process of inspection, cleaning, transforming and modeling data with the aim of discovering useful information, suggesting conclusions and supporting decision making. Data analytics has multiple facets and approaches, encompassing different techniques under a variety of names, in different business, scientific and social science fields. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive processes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on information corporate. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in CDA data on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for prediction or predictive classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a kind of data. They are all varieties of data analysis. Data integration is a precursor to data analytics, and data analytics is closely linked to data visualization and dissemination. The term data analytics is sometimes used synonymously with data modeling. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original EssayIntroductionIntroduction: The process of converting raw data into information begins with data processing and continues with data analysis. The analysis involves the use of statistical techniques to sort the data with the aim of obtaining answers to the research questions. Analysis can be seen as the sorting, breaking down into constituent parts and manipulation of data to obtain answers to the research question or questions underlying the investigation project. The analysis is followed by interpretation of the research findings using the analysis output to make inferences and draw conclusions about relationships. Data analysis is performed using a thorough plan, developed by a flexible and open-minded analyst. Good, Bar and Scats have listed four ways to start analyzing the data you collect. Think in terms of meaningful tables enabled by the data. Carefully examine the problem statement and previous analysis, and study the original data records. Step away from the data to think about the problem in lay terms or to actually discuss the problems with others. Attack the data by doing various statistical calculations. Each of these approaches can be used to begin data analysis. The data analysis strategy is influenced by factors such as the type of data, the researcher's qualifications, and the assumptions underlying a statistical technique. Objectives Topic Statement: In a practice involved in planning for the future, framing issues through problem identification and realistic goals and objectives is critical. Evidence and citations: How problems are framed determines the nature of solutions and the criteria by 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 systemwill facilitate the achievement of these objectives. This section outlines existing goals and objectives that guide transportation improvement and planning at the federal, state, and local levels. Types of Analysis Topic Sentence: Quantitative data is anything that can be expressed as a number or quantified. Evidence and citations: Examples of quantitative data are achievement test scores, number of hours studied, or weight of a subject. Comment: These data can be represented using ordinal, interval or ratio scales and are suitable for most statistical manipulations. Topic sentence: Qualitative data cannot be expressed as numbers. Evidence and Citations: Data representing nominal scales such as gender, social economic status, religious preference are generally considered qualitative data. The Process of Data Analysis Topic Sentence: Analysis refers to breaking the whole into its separate components for individual examination. Evidence and Citations: Data analytics is a process of obtaining raw data and converting it into useful information for user decision making. Comment: 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 collection of data to make its analysis simpler, more precise, or more accurate , and all statistical (mathematical) machinery and results that apply to data analysis. Different phases can be distinguished, described below. The phases are iterative, as feedback from subsequent phases can lead to additional work in the previous phases. Data Requirements Topic Sentence: Data is needed because the inputs to the analysis are specified based on the requirements of those directing the analysis or the customers who will use the finished product of the analysis. Evidence and citation: The general type of entity on which data will be collected is called an experimental unit (for example, a person or a population of people. It is possible to specify and obtain specific variables regarding a population (for example, age and income). The data can be numeric or categorical (i.e. a text label for the numbers). Data Collection Topic Sentence: Data is collected from a variety of sources. Comment: Requirements can be communicated from analysts to data custodians, such as IT personnel within an organization. Evidence and Citations: Data can also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. They can also be obtained through interviews, downloads from online sources, or reading documentation. Data ProcessingTopic Sentence: The stages of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the stages of data analysis. Evidence and citations: The data initially obtained must be processed or organized for analysis. Comment: For example, these may involve entering data into rows and columns in a table format (i.e. structured data) for further analysis, for example within a spreadsheet or statistical software. Data Cleaning Topic Sentence: Once processed and organized, data may be incomplete, contain duplicates, or contain errors. Comment: The need to clean data will arise from problems in the way data is entered and stored. Evidence and Citations: Data cleansing is the process of preventing and correcting these errors. Common tasks include matching records, identifying data inaccuracy, overall quality of existing data,.