Exploratory Data Analysis: A Bird’s Eye View
Exploratory Data AnalysisModule I: A Bird’s Eye ViewDr. Mark WilliamsonDaCCoTAUniversity of North Dakota
IntroductionExploratory data analysis: The approach that explores datasets to summarize their main characteristicsusing summary statistics and graphical methods First step for successful statistical analysesExploratory sicTests
LandscapeExploratory DataAnalysisViewdataSummarystatisticsObservation numberVariable numberVariable typeVariable rd deviationOutliersMissing dataBasicGraphsHistogramBar plotBoxplotScatter-plotQQ-plotBasicTestsCheck assumptionsT-testsCorrelationsANOVALinear model
Structures and UsesViewdataObservation numberVariable numberVariable typeVariable categoryStructureVariable category:Nuisance/bookkeepingDependent variableIndependent variableStructure:Long or widePaired dataRepeated measuresObservation number:number of rowsnumber of samplesVariable type:Numerical discreteNumerical continuousCategorical nominalCategorical ordinalVariable number:Number of columnsNumberBody enior1098.817210.1BiologyFreshman
Structures and andard deviationOutliersMissing dataMeanMedianAlso know as the average; the sum of all values divided by number of valuesAlso known as the middle value; less influenced by outliers than meanModeThe most common value among observationsRangeThe distance between the highest and lowest value among observationsVarianceHow much the values are spread out; average all the numbers differ from the meanStandard deviation Another measures of how much the values are spread out; square root of varianceOutliersMissing dataVery high or very low values; may be true or some sort of measurement/writing errorData that should be there but is not; may be error or simple not measured
Structures and UsesBasicGraphsHistogram:Distribution of valuesBoxplot:Median, quartiles,outliers across groupsQQ-plot:Test for normalityBar-plot:Mean across catter-plotRelationship between tonumerical variables
Structures and nearOutliersmodelMissing dataCheckassumptionsT-testsIs the data normally distributed? (histogram, qq-plot)How large is the variance? (summary statistics)Are there equal observations across groups? (summary statistics)Is there is a difference between one group and a set value or between two groups?(boxplot, bar-plot)CorrelationsIs there a relationship between two numerical variables? (scatter plot)ANOVAIs there a difference between three or more groups? (boxplot, bar-plot)Linear modelCan the dependent variable be predicted from one or more independentvariables?
Examples
Quick Assessment1. What are the four basic parts of exploratorydata analysis?View Data, Summary Statistics, Simple Graphs, Simple Tests2. What graph would you use to see if theremight be a relationship between twonumerical variables?Scatter Plot3. What exploration part helps determine thevariable number, type, and category?Viewing the data4. What summary statistic is also called theaverage?Mean5. If you ran an ANOVA, what sort of graph(s)would you use to display the results?Bar-plot and/or boxplot6. What type of graphis this?Histogram
Quick Assessment1. What are the four basic parts of exploratorydata analysis?View Data, Summary Statistics, Simple Graphs, Simple Tests2. What graph would you use to see if theremight be a relationship between twonumerical variables?Scatter Plot3. What exploration part helps determine thevariable number, type, and category?Viewing the data4. What summary statistic is also called theaverage?Mean5. If you ran an ANOVA, what sort of graph(s)would you use to display the results?Bar-plot and/or boxplot6. What type of graphis this?Histogram
Summary and Conclusion Exploratory data analysis is the first step in analyzing data Viewing data helps determine the type and structure of the data Summary statistics helps summarize the data numerically Simple graphs helps visualize structure and relationships of the data Simple tests provide a guide for further data analysis Tune in next time for a stroll through the core components ofExploratory Data Analysis in Module II: Leaves and Trees
Mean Median Mode Range Variance Standard deviation Outliers Missing data Mean Also know as the average; the sum of all values divided by number of values Median Also known as the middle value; less influenced by outliers than mean Mode The most common value among observations Range The distance between the highest and lowest value among .
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Exploratory Data Analysis - Detailed Table of Contents [1.] This chapter presents the assumptions, principles, and techniques necessary to gain insight into data via EDA--exploratory data analysis. 1. EDA Introduction [1.1.] 1. What is EDA? [1.1.1.] 2. How Does Exploratory Data Analysis differ from Classical Data Analysis?
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for automated suggestions for portions of the exploratory data analysis process. Many intuitive user interface features that would be ideal to have for an exploratory data analy-sis tool are available in Tableau [1] which is descended from earlier research in exploratory data analysis such as Polaris [14] and Mackinlay's earlier work [9].
of methods for Exploratory Data Analysis & Sentiment Analysis by utilizing various packages concerned. Keywords—Exploratory Data Analysis; Sentiment Analysis; Data Analytics; Python; Seaborn; Numpy; Tensorflow - Keras I. INTRODUCTION The term "Data Analysis" is known to be rooted in the statistics space, which itself is known to have a .
The tasks of Exploratory Data Analysis Exploratory Data Analysis is listed as an important step in most methodologies for data analysis (Biecek,2019;Grolemund and Wickham,2019). One of the most popular methodologies, the CRISP-DM (Wirth,2000), lists the following phases of a data mining project: 1.Business understanding. 2.Data understanding.
built upon existing models. Tukey contrasted exploratory analysis with calculations of values, or con rmatory data analysis . These two sets of methods are both forms of model checking: exploratory data analysis is the search for unanticipated areas of model mis t, and con rmatory data analysis quanti es the extent to which these discrepancies .
