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Exploratory Data Analysis (EDA)_ The First Step in Any Data Science Project

<br>Business name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Mumbai<br>Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd, opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602<br>Phone: 09108238354 <br>Email: enquiry@excelr.com<br>

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Exploratory Data Analysis (EDA)_ The First Step in Any Data Science Project

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  1. ExploratoryDataAnalysis(EDA):TheFirstStepinAnyDataScienceProjectExploratoryDataAnalysis(EDA):TheFirstStepinAnyDataScienceProject EDAisacriticalstepindatasciencethathelpsunderstandthestructure,patterns,and anomalieswithinadataset.DataScienceCourse.Itlaysthefoundationforinformed decision-makingandmodelling.Herearefivekeypoints: • Understanding the Dataset • Purpose:Explorethedataset’sstructure,variables,anddatatypestounderstandits contentandlimitations. • Methods:Usedescriptivestatisticslikemean,median,mode,andstandarddeviation. • Tools:Python’sPandas(info(),describe()),R’ssummary()function,orSQLfor databaseexploration. • IdentifyingMissingandErroneousData • Purpose:Detectandhandlemissingvalues,duplicates,andinconsistenciestoprepare dataforanalysis. • Methods:Checkfornullvalues,outliers,andinvalidentries. • Tools:Pandas(isnull()),Excelfilters,orvisualizationtoolslikeTableau. • Visualizing Data Distributionsand Relationships • Purpose:Usevisualizationstounderstandthedistributionofvariablesandrelationships betweenthem. • Methods: • Univariateanalysis(e.g.,histograms,boxplots). • Multivariateanalysis (e.g.,scatterplots,correlationheatmaps). • Tools:Matplotlib,Seaborn,andPowerBI.

  2. DetectingPatternsandTrends • Purpose:Identifypatterns,trends,oranomaliesthatcouldinfluencetheanalysisor modelperformance. • Methods:Time-seriesanalysis,grouping,andaggregationfortrenddiscovery. • Tools:Python’sgroupby()inPandas,orTableau’sdrag-and-dropinterfaceforvisual trendanalysis. • InformingHypothesesandModelingDecisions • Purpose:Generatehypothesesandinsightstoguidesubsequentmodelingandfeature engineering. • Methods:Combinestatisticalandvisualfindingstodeterminewhichvariablesaremost relevant. • Tools:PythonandRforhypothesistesting,JupyterNotebookfordocumentingEDA workflows. EDAisnotjustaboutinspectingdata;itisaboutuncoveringinsightsandsettingthestagefor effectivedata-drivenproblem-solving.DataScienceCourseinMumbai.ProperEDAensures thatthedatasetisunderstoodandoptimizedforthenextstepsinthedatascienceprocess. Businessname:ExcelR-DataScience,DataAnalytics,BusinessAnalyticsCourseTraining Mumbai Address:304,3rdFloor,PratibhaBuilding.ThreePetrolpump,LalBahadurShastriRd, oppositeManasTower, Pakhdi,ThaneWest,Thane,Maharashtra400602 Phone:09108238354 Email:enquiry@excelr.com

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