Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points. # Task general Description Pro forma Abstract Examples 1 Retrieve value given a set of specific cases, find attributes of those cases. What are the values of attributes x, y,. In the data cases a, b, c,? what is the mileage per gallon of the ford Mondeo? how long is the movie gone with the wind? 2 Filter given some concrete conditions on attribute values, find data cases satisfying those conditions.
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Analysts may use robust statistical measurements to solve certain analytical problems. 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 man false. For example, the hypothesis might be that "Unemployment has no effect on inflation which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type i and type ii errors, which relate to whether the data supports accepting or rejecting the hypothesis. Regression analysis may be used when the analyst is trying to determine the extent requirements to which independent variable x affects dependent variable y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)? This is an attempt to model or fit an equation line or curve to the data, such that y is a function. Necessary condition analysis (NCA) may be used when the analyst is trying to determine the extent to which independent variable x allows variable y (e.g., "To what extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)? Whereas (multiple) regression analysis uses additive logic where each x-variable can produce the outcome and the x's can compensate for each other (they are sufficient but not necessary necessary condition analysis (NCA) uses necessity logic, where one or more x-variables allow the outcome to exist. Each single necessary condition must be present and compensation is not possible. Analytical activities of data users edit Users may have particular data points of interest within a data set, as opposed to general messaging outlined above.
These include: Check raw data for anomalies prior to performing your analysis; re-perform important calculations, such as verifying columns of data that are formula driven; Confirm main totals are the essay sum of subtotals; Check relationships between numbers that should be related in a predictable way. 7 For the variables under examination, analysts typically obtain descriptive statistics for them, such as the mean (average median, and standard deviation. They may also analyze the distribution of the key variables to see how the individual values cluster around the mean. An illustration of the mece principle used for data analysis. The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the mece principle. Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. The relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or mece. For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as revenue of divisions a, b, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).
For example, plotting unemployment (X) and inflation (Y) for slogan a sample of months. A scatter plot is typically used for this message. Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison. Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used. 12 13 Techniques for analyzing quantitative data edit see also: Problem solving Author Jonathan koomey has recommended a series of best practices for understanding quantitative data.
A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market. Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual. Budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference amount. Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 010, 1120, etc. A histogram, a type of bar chart, may be used for this analysis. Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions.
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4 When 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 analysis tables and charts) to help communicate key messages contained in the data. Tables are helpful to a user who might lookup specific numbers, while charts (e.g., bar charts or line charts) may help explain the quantitative messages contained in the data. Quantitative messages edit main article: Data visualization A time series illustrated with a line chart demonstrating trends. Federal spending and revenue over time. A scatterplot illustrating correlation between two variables (inflation and unemployment) measured at points in time.
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 process. Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend. Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure ) by sales persons (the category, with each sales person a categorical subdivision ) during a single period. A bar chart may be used to show the comparison across the sales persons. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100).
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. 4 Modeling and algorithms edit mathematical 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.e., data model Error). 2 Inferential statistics includes techniques to measure relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X) explains the variation in sales (dependent variable Y).
In mathematical terms, y (sales) is a function of X (advertising). It may be described as y ax b error, where the model is designed such that a and b minimize the error when the model predicts Y for a given range of values. Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results. 2 Data product edit a data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy. 4 Communication edit main article: Data visualization Once the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.
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For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable. 7 Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that summary 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. 8 Exploratory data analysis edit Once the data is cleaned, it can be analyzed. Analysts may apply a outsiders 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.
It may also be obtained through interviews, downloads from online sources, or reading documentation. 4 Data processing edit 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 organised 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. 4 Data cleaning edit Once processed and organised, 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 plan data, 5 deduplication, and column segmentation. 6 Such data problems can also be identified through a variety of analytical techniques.
of data to make its analysis easier, more precise or more accurate, and all the machinery and results. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases. 4 Data requirements edit The data is necessary as inputs to the analysis, which is 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). 4 Data collection edit 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.
Data integration is a precursor to data analysis, according to whom? and data analysis is closely linked how? to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling. Contents, the the process of data analysis edit, data science process flowchart from "Doing Data Science cathy o'neil and Rachel Schutt, 2013. 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.
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Data analysis is a process of inspecting, cleansing, transforming, and modeling data biography with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains. Data 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 mainly 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 while cda focuses 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 of the above are varieties of data analysis.