1 / 4

_Introduction to Data Science

ExcelR Data Science Course In Mumbai<br>

elsy1
Download Presentation

_Introduction to Data Science

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. AdatasciencecourseinMumbaioffersanopportunitytogainin-depthknowledgeand practicalskillsintherapidlygrowingfieldofdatascience.Mumbai,beingamajorfinancialand technologicalhubinIndia,providesanidealenvironmentforaspiringdatascientiststolearnandapplytheirskills.Here’sadetailedoverviewofwhatacomprehensivedatasciencecourse inMumbaimightentail: • Introduction toData Science • 1.OverviewofDataScience: • Definitionand importanceofdatascience. • Rolesandresponsibilitiesofadatascientist. • Keyskillsandcompetenciesrequiredindatascience. • ApplicationsofDataScience: • Usecasesinvariousindustriessuchasfinance,healthcare,marketing,ande-commerce. • Real-worldexamplesofdata-drivendecision-making. • ProgrammingforData Science • 1.PythonforDataScience: • IntroductiontoPythonprogramming. • Essential libraries:NumPy,pandas,matplotlib,seaborn. • WritinganddebuggingPythoncode. • RProgramming: • BasicsofRprogramming. • Keypackages:dplyr,ggplot2,tidyr. • DatamanipulationandvisualizationinR. • DataCollectionandCleaning • 1.DataCollectionMethods: • Techniquesfordatacollection:webscraping,APIs,surveys. • Datasources:publicdatasets,databases,andproprietarydata. • DataCleaningandPreprocessing: • Handlingmissingvalues,duplicates,andoutliers. • Datanormalizationandtransformation. • Featureengineeringandselection. • StatisticalAnalysisandDataExploration • 1. DescriptiveStatistics: • -Measuresofcentraltendencyandvariability.

  2. -Datadistributionandvisualization. • InferentialStatistics: • Hypothesistesting,confidenceintervals,andp-values. • Statisticaltests:t-tests,chi-squaretests,ANOVA. • DataVisualization • PrinciplesofDataVisualization: • Importanceofdatavisualizationindataanalysis. • Designprinciplesforeffectivevisualizations. • ToolsforDataVisualization: • CreatingvisualizationswithPython(matplotlib,seaborn)andR(ggplot2). • IntroductiontoTableauandPowerBIfor interactivevisualizations. • MachineLearningandPredictiveModeling • 1.IntroductiontoMachineLearning: • Supervised vs.unsupervisedlearning. • Key algorithms: linear regression, decision trees, clustering, and more. • ModelBuildingandEvaluation: • Datasplitting:trainingandtestingsets. • Modelevaluationmetrics:accuracy,precision,recall,F1score. • Cross-validationandhyperparametertuning. • BigDataTechnologies • Introduction toBigData: • Overviewofbigdataconceptsandtechnologies. • UnderstandingHadoopandSpark. • BigDataTools: • UsingHadoopfordistributeddatastorageandprocessing. • WorkingwithSparkforlarge-scaledataanalysis. • DataScienceintheCloud • CloudComputingFundamentals: • Basicsofcloudcomputinganditsbenefits. • Keycloudserviceproviders:AWS,GoogleCloud,MicrosoftAzure. • 2.DataScienceontheCloud:

  3. Usingcloudservicesfordatastorage,processing,andmachinelearning.Usingcloudservicesfordatastorage,processing,andmachinelearning. • IntroductiontotoolslikeAWSS3,GoogleBigQuery,AzureMachineLearning. • CapstoneProjectandPracticalApplications • 1.Real-WorldDataScienceProjects: • Hands-onprojectsusingrealdatasets. • End-to-enddatascienceworkflow:fromdatacollectiontomodeldeployment. • CapstoneProject: • Comprehensiveprojectshowcasingtheentiredatascienceprocess. • Presentation offindingsandrecommendations. • SoftSkillsandCareerDevelopment • 1.CommunicationSkills: • Presentingdatainsightstonon-technicalstakeholders. • Writingclearandconcisereports. • CareerPreparation: • Buildingaprofessionalportfoliowithprojectsandvisualizations. • Resumewriting,interviewpreparation,andnetworkingtips. • NetworkingandIndustryConnections • 1.IndustryInteraction: • Guestlecturesandworkshopsbyindustryexperts. • Opportunitiestointeractwithprofessionalsfromtopcompanies. • InternshipsandJob Placements: • Assistancewithinternshipsandjobplacements. • LeveragingMumbai’svibrantjobmarketforcareeropportunities. • CertificationandContinuousLearning • 1.Certification: • Earningacertificateuponcoursecompletion. • Preparationforindustry-recognizedcertifications(e.g.,GoogleData Analytics,Microsoft Certified: Data Scientist). • ContinuousLearning: • Keepingup-to-datewiththelatesttrendsandtechnologiesindatascience. • Accesstoresourcesforongoinglearninganddevelopment.

  4. BytheendofthedatasciencecourseinMumbai,studentswillhaveastrongfoundationin datascienceprinciples,practicalexperiencewithindustrytools,andtheabilitytoapplytheir skillstosolvereal-worldproblems.Thecoursepreparesparticipantsforasuccessfulcareerin datascience,leveragingMumbai’sdynamicecosystemtoconnectwithindustryleadersand opportunities. BusinessName:ExcelR-DataScience,DataAnalytics,BusinessAnalystCourseTraining Mumbai Address:Unitno.302,03rdFloor,AshokPremises,OldNagardasRd,NicolasWadiRd, MograVillage,GundavaliGaothan,AndheriE,Mumbai,Maharashtra400069,Phone: 09108238354,Email:enquiry@excelr.com.

More Related