Methods of data analytics

Wikipedia, the free to: navigation, of a series on atory data analysis • information ctive data ptive statistics • inferential tical graphics • analysis  • munzner  • ben shneiderman  • john w. Body · v · ulam · von neumann · galerkin · analysis, also 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 mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. 1] 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 the data and 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, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. Science process flowchart from "doing data science", cathy o'neil and rachel schutt, is 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. 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. 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. 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. Phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i. The need for data cleaning will arise from problems in the way that data is entered and stored. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data,[5] deduplication, and column segmentation. 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. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. 9][10] the process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics such as the average or median may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data. Formulas or models called algorithms may be applied to the data to identify relationships among the variables, such as correlation or causation.

In general terms, models may be developed to evaluate a particular variable in the data based on other variable(s) in the data, with some residual error depending on model accuracy (i. Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results. Data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy. Article: data the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. Determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays such as tables and charts to help communicate key messages contained in the data. Scatterplot illustrating correlation between two variables (inflation and unemployment) measured at points in stephen few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the -series: a single variable is captured over a period of time, such as the unemployment rate over a 10-year period. Also: problem jonathan koomey has recommended a series of best practices for understanding quantitative data. They may also analyze the distribution of the key variables to see how the individual values cluster around the illustration of the mece principle used for data consultants at mckinsey and company named a technique for breaking a quantitative problem down into its component parts called the mece principle. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false. Hypothesis testing involves considering the likelihood of type i and type ii errors, which relate to whether the data supports accepting or rejecting the sion analysis may be used when the analyst is trying to determine the extent to which independent variable x affects dependent variable y (e.

This is an attempt to model or fit an equation line or curve to the data, such that y is a function of ary condition analysis (nca) may be used when the analyst is trying to determine the extent to which independent variable x allows variable y (e. Each single necessary condition must be present and compensation is not ical activities of data users[edit]. May have particular data points of interest within a data set, as opposed to general messaging outlined above. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points. Some concrete conditions on attribute values, find data cases satisfying those data cases satisfy conditions {a, b, c... Derived a set of data cases, compute an aggregate numeric representation of those data is the value of aggregation function f over a given set s of data cases? Data cases possessing an extreme value of an attribute over its range within the data are the top/bottom n data cases with respect to attribute a? A set of data cases, rank them according to some ordinal is the sorted order of a set s of data cases according to their value of attribute a? Rank the cereals by a set of data cases and an attribute of interest, find the span of values within the is the range of values of attribute a in a set s of data cases? A set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute’s values over the is the distribution of values of attribute a in a set s of data cases? Any anomalies within a given set of data cases with respect to a given relationship or expectation, e. A set of data cases, find clusters of similar attribute data cases in a set s of data cases are similar in value for attributes {x, y, z, ... A set of data cases and two attributes, determine useful relationships between the values of those is the correlation between attributes x and y over a given set s of data cases?

A set of data cases, find contextual relevancy of the data to the data cases in a set s of data cases are relevant to the current users' context? To effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data ing fact and opinion[edit]. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques. Analysts apply a variety of techniques to address the various quantitative messages described in the section ts may also analyze data under different assumptions or scenarios. 21] the different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and ics and business intelligence[edit]. Article: ics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. It is a subset of business intelligence, which is a set of technologies and processes that use data to understand and analyze business performance. Activities of data visualization education, most educators have access to a data system for the purpose of analyzing student data. 23] these data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses. Section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a wikipedia l data analysis[edit]. Most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not for common-method choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

Quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. Assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase. Should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in possible data distortions that should be checked are:Dropout (this should be identified during the initial data analysis phase). Nonresponse (whether this is random or not should be assessed during the initial data analysis phase). It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis characteristics of the data sample can be assessed by looking at:Basic statistics of important ations and -tabulations[31]. The final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are , the original plan for the main data analyses can and should be specified in more detail or order to do this, several decisions about the main data analyses can and should be made:In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method? The case of missing data: should one neglect or impute the missing data; which imputation technique should be used? Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are atory data analysis should be interpreted carefully. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. There are two main ways of doing this:Cross-validation: by splitting the data in multiple parts we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as ivity analysis: a procedure to study the behavior of a system or model when global parameters are (systematically) varied.

A very brief list of four of the more popular methods is:General linear model: a widely used model on which various methods are based (e. A database system endorsed by the united nations development group for monitoring and analyzing human – data mining framework in java with data mining oriented visualization – the konstanz information miner, a user friendly and comprehensive data analytics – fortran/c data analysis framework developed at – a visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine learning. A programming language and software environment for statistical computing and – c++ data analysis framework developed at and pandas – python libraries for data ss ing (statistics). Presentation l signal case atory data inear subspace ay data t neighbor ear system pal component ured data analysis (statistics). Clean data in crm: the key to generate sales-ready leads and boost your revenue pool retrieved 29th july, 2016. How data systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Manual on presentation of data and control chart analysis, mnl 7a, isbn rs, john m. Data analysis: an introduction, sage publications inc, isbn /sematech (2008) handbook of statistical methods,Pyzdek, t, (2003). Data analysis: testing for association isbn ries: data analysisscientific methodparticle physicscomputational fields of studyhidden categories: wikipedia articles with gnd logged intalkcontributionscreate accountlog pagecontentsfeatured contentcurrent eventsrandom articledonate to wikipediawikipedia out wikipediacommunity portalrecent changescontact links hererelated changesupload filespecial pagespermanent linkpage informationwikidata itemcite this a bookdownload as pdfprintable version. Most important methods for statistical data the information age, data is no longer scarce – it’s overpowering. The key is to sift through the overwhelming volume of data available to organizations and businesses and correctly interpret its implications. But to sort through all this information, you need the right statistical data analysis the current obsession over “big data,” analysts have produced a lot of fancy tools and techniques available to large organizations. However, there are a handful of basic data analysis tools that most organizations aren’t using…to their suggest starting your data analysis efforts with the following five fundamentals – and learn to avoid their pitfalls – before advancing to more sophisticated arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list.

The mean is useful in determining the overall trend of a data set or providing a rapid snapshot of your data. In some data sets, the mean is also closely related to the mode and the median (two other measurements near the average). However, in a data set with a high number of outliers or a skewed distribution, the mean simply doesn’t provide the accuracy you need for a nuanced decision. Standard standard deviation, often represented with the greek letter sigma, is the measure of a spread of data around the mean. A high standard deviation signifies that data is spread more widely from the mean, where a low standard deviation signals that more data align with the mean. In a portfolio of data analysis methods, the standard deviation is useful for quickly determining dispersion of data like the mean, the standard deviation is deceptive if taken alone. For example, if the data have a very strange pattern such as a non-normal curve or a large amount of outliers, then the standard deviation won’t give you all the information you sion models the relationships between dependent and explanatory variables, which are usually charted on a scatterplot. For example, an outlying data point may represent the input from your most critical supplier or your highest selling product. As an illustration, examine a picture of anscombe’s quartet, in which the data sets have the exact same regression line but include widely different data points. Sample size measuring a large data set or population, like a workforce, you don’t always need to collect information from every member of that population – a sample does the job just as well. Using proportion and standard deviation methods, you are able to accurately determine the right sample size you need to make your data collection statistically studying a new, untested variable in a population, your proportion equations might need to rely on certain assumptions. This error is then passed along to your sample size determination and then onto the rest of your statistical data analysis. Hypothesis commonly called t testing, hypothesis testing assesses if a certain premise is actually true for your data set or population.

In data analysis and statistics, you consider the result of a hypothesis test statistically significant if the results couldn’t have happened by random chance. Another common error is the hawthorne effect (or observer effect), which happens when participants skew results because they know they are being l, these methods of data analysis add a lot of insight to your decision-making portfolio, particularly if you’ve never analyzed a process or data set with statistics before. Once you master these fundamental techniques for statistical data analysis, then you’re ready to advance to more powerful data analysis learn more about improving your statistical data analysis through powerful data visualization, click the button below to download our free guide, “5 tips for security data analysis” and start turning your abstract numbers into measurable y policysite mapdesign by hinge© big sky associates. Once you master these fundamental techniques for statistical data analysis, then you’re ready to advance to more powerful data analysis learn more about improving your statistical data analysis through powerful data visualization, click the button below to download our free guide, “5 tips for security data analysis” and start turning your abstract numbers into measurable y policysite mapdesign by hinge© big sky courses by r sional college icates of transferable credit & get your degree degrees by ical and ications and ry arts and l arts and ic and repair l and health ortation and and performing a degree that fits your schools by degree degree raduate schools by sity video counseling & job interviewing tip networking ching careers info by outlook by & career research : data analysis: techniques & this lesson, we'll learn about data analysis. We'll define the two methods of data analysis, quantitative and qualitative, and look at each of their various techniques. The lesson will then conclude with a summary and a & worksheet - performing data error occurred trying to load this refreshing the page, or contact customer must create an account to continue er for a free you a student or a teacher? Definition & of data ary data analysis: methods & ch methodology: approaches & ing, applying, and drawing conclusions from research to make sources: definition & s & populations in research: atory research: definition, methods & is hypothesis testing? Components & ience sampling in statistics: definition & m solving in organizations: skills, steps & importance of good communication with iate data: definition, analysis & is business analysis? Process, methods & scale: definition, examples & ng frame: definition & e preparatory mathematics: help and mcdougal economics - concepts and choices: online textbook us: tutoring ry 101: intro to ce hall algebra 1: online textbook ss calculus: help & mathematics: prep and us: homework help algebra: tutoring ss math business mathematics: study guide & test business law: study guide & test culus algebra: help and us: help and ental assessment test in math: practice & study assessments for educators - mathematics: practice & study ss 103: introductory business ing games for ms & antonyms lesson learning style: strategies & vs. Beginning look at data analysislet's imagine that you have just enrolled in your first college course. Research is about gathering data that you can analyze and use to come to some sort of conclusion. So, before you begin your data collection, you realize that you have a lot to learn about the various methods and techniques of gathering data. Before we look at the methods and techniques of data analysis, lets first define what data analysis is.

Data analysis is the collecting and organizing of data so that a researcher can come to a conclusion. Methods of data analysisokay, you have decided to prove that public school is better than private school, but now you need to figure out how you will collect the information and data needed to support that idea. This technique can take a long period of time because the researcher needs to be accepted into the group so that they observe data that is natural. Anyone can -by-exam regardless of age or education learn more, visit our earning credit erring credit to the school of your able degree, area career path that can help you find the school that's right for ch schools, degrees & the unbiased info you need to find the right articles by an area of study or degree ical and biomedical ications and ry arts and personal l arts and ic and repair l and health ortation and and performing care data analysis education and training program to become a clinical data analyst: education and career to become a health data analyst: step-by-step career tistics degree and certificate program analyst degree and certificate program s degree in informatics: program in addictions counseling: info on doctorate program options for aspiring urban and regional or of science (bs): health information management degree ate degree in computer analysis: program force rotc programs in s that involve creativity and helping care privacy officer: job description & al technology degree programs in ts to be fined for skipping class in san francisco to survive law ion graduate programs california. The standard ed value in probability: definition & g percentiles in a data set: formula & to calculate mean, median, mode & fying & calculating distribution & shifts in the ility of independent and dependent ility of simple, compound and complementary tical significance: definition & is data analysis? Categorical analysis: techniques & methods related study guide & help business subtest ii: practice and study business subtest iii: practice and study math exam: tutoring mcdougal algebra 2: online textbook principles of public speaking: study guide & test uction to public speaking: certificate prep: tutoring school geometry: homework help school geometry: tutoring school trigonometry: help and school trigonometry: homework help school trigonometry: tutoring prep: tutoring g active in teacher organizations for business perkins' effect on technical education business educator's relationship with schools & ives of business education -based learning in business & worksheet - funding opportunities for & worksheet - society & culture's impact on global & worksheet - goal setting as part of & worksheet - change agents in & worksheet - calculating current business: business business: foundations of business: written & oral business: employment communication & career business: economic allocation of school library media specialist: practice & study ing 308: applied english speakers of other languages: practice & study school counselor: practice & study logy 302: cognitive tation skills for ing implementation & ng strategically for ing tactics & strategies for segmentation uing education opportunities for molecular biology college & career readiness standards for social core state standards in ces for assessing export w personal rnia school emergency planning & safety le stick bridge lesson is an introductory prepositional phrase? Definition & souls by nikolai gogol: characters & adjectives starting with & worksheet - distribution methods for global & worksheet - questions for freak the mighty chapter & worksheet - why do people travel? Department of rs engage their p a research g the proposal - data your research proposal, you will also discuss how you will conduct an analysis of your data. By the time you get to the analysis of your data, most of the really difficult work has been done. If you have done this work well, the analysis of the data is usually a fairly straightforward you look at the various ways of analyzing and discussing data, you need to review the differences between qualitative research/quantitative research and qualitative data/quantitative do i have to analyze data? The analysis, regardless of whether the data is qualitative or quantitative, may:Describe and summarize the fy relationships between fy the difference between r, you distinguished between qualitative and quantitative research. Source of confusion for many people is the belief that qualitative research generates just qualitative data (text, words, opinions, etc) and that quantitative research generates just quantitative data (numbers). Sometimes this is the case, but both types of data can be generated by each approach.

For instance, a questionnaire (quantitative research) will often gather factual information like age, salary, length of service (quantitative data) – but may also collect opinions and attitudes (qualitative data). It comes to data analysis, some believe that statistical techniques are only applicable for quantitative data. There are many statistical techniques that can be applied to qualitative data, such as ratings scales, that has been generated by a quantitative research approach. Even if a qualitative study uses no quantitative data, there are many ways of analyzing qualitative data. For example, having conducted an interview, transcription and organization of data are the first stages of analysis. Manchester metropolitan university (department of information and communications) and learn higher offer a clear introductory tutorial to qualitative and quantitative data analysis through their analyze this!!! In additional to teaching about strategies for both approaches to data analysis, the tutorial is peppered with short quizzes to test your understanding. The site also links out to further te this tutorial and use your new knowledge to complete your planning guide for your data are many computer- and technology-related resources available to assist you in your data general ing research (lots of examples of studies, and lots of good background, especially for qualitative studies). Data tative data analysis rice virtual lab in statistics also houses an online textbook, hyperstat. The site also includes a really useful section of case studies, which use real life examples to illustrate various statistical sure which statistical test to use with your data? The diagram is housed within another good introduction to data statistical analysis and data management computer-aided qualitative data analysis are many computer packages that can support your qualitative data analysis. The following site offers a comprehensive overview of many of them: online r package that allows you analyze textual, graphical, audio and video data. Questions are addressed by researchers by assessing the data collection method (the research instrument) for its reliability and its ility is the extent to which the same finding will be obtained if the research was repeated at another time by another researcher.

The following questions are typical of those asked to assess validity issues:Has the researcher gained full access to the knowledge and meanings of data? Procedure is perfectly reliable, but if a data collection procedure is unreliable then it is also invalid. The other problem is that even if it is reliable, then that does not mean it is necessarily ulation is crosschecking of data using multiple data sources or using two or more methods of data collection. There are different types of triangulation, including:Time triangulation – longitudinal ological triangulation – same method at different times or different methods on same object of igator triangulation – uses more than one ng error is a measure of the difference between the sample results and the population parameters being measured. The many sources of non-sampling errors include the following:Researcher error – unclear definitions; reliability and validity issues; data analysis problems, for example, missing iewer error – general approach; personal interview techniques; recording dent error – inability to answer; unwilling; cheating; not available; low response section was discussed in elements of the proposal, where there are many online resources, and you have reflective journal entries that will support you as you develop your ideas for reliability and validity in your planning guide.