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AI360Xpert

Data Scientist

A comprehensive guide for Data Scientist

""1MATHEMATICS & STATISTICS2PROGRAMMING & SOFTWARE ENGINEERING3DATA WRANGLING & EXPLORATION4DATA VISUALIZATION5MACHINE LEARNING6DEEP LEARNING7MLOPS & DEPLOYMENT8DOMAIN KNOWLEDGE & SOFT SKILLSData ScientistLinear AlgebraVectors, Matrices, andTensorsMatrix Multiplicationand TransformationsDeterminants andInversesEigenvalues andEigenvectorsSingular ValueDecompositionSVDPrincipal ComponentAnalysis (PCA) MathCalculusLimits and ContinuityDerivatives andGradientsPartial Derivativesand the JacobianChain RuleIntegralsOptimizationMaxima/MinimaGradient DescentHessianProbability and StatisticsDescriptive StatisticsMeanMedianVarianceProbabilityDistributionsNormalBinomialPoissonExponentialBayes' Theorem andBayesian InferenceInferential StatisticsHypothesis Testingp-valuesANOVAMaximum LikelihoodEstimation (MLE) &Maximum A PosterioriMAPA/B TestingConfidence IntervalsCausal InferencePropensity ScoreMatchingDifference-in-DifferencesMarkov Chains andMonte CarloSimulationsPython FundamentalsVariables, Data Types,Control FlowFunctions, Generators,and IteratorsObject-OrientedProgrammingOOPData StructuresListsDictionariesSetsTuplesError Handling andExceptionsType Hinting andPydanticData Manipulation LibrariesNumPyArraysBroadcastingVectorizationPandasDataFramesSeriesMergingGroupingPolarsFast DataFramesSoftware Engineering PracticesVersion ControlGit & GitHubClean Code, PEP 8, andLintersRuffFlake8Unit TestingpytestunittestVirtual EnvironmentsvenvcondaPoetryuvCommand Line Interface(CLI) BasicsDatabase & SQL SkillsSQL for Data ScienceJoinsAggregationsWindow FunctionsCTEsCloud Data WarehousesSnowflakeBigQueryRedshiftNoSQL DatabasesMongoDBCassandraVector DatabasesChromaDBMilvusQdrantPineconeData TransformationdbtData CollectionAPIsRESTGraphQLJSONWeb ScrapingBeautifulSoupScrapySeleniumDatabase ConnectivitySQLAlchemypsycopg2Data CleaningHandling MissingValuesImputationDroppingHandling OutliersZ-scoreIQRIsolation ForestData Type ConversionsString ManipulationRegexExploratory Data Analysis (EDA)Univariate AnalysisBivariate andMultivariate AnalysisCorrelation andCovarianceFeature Engineering,Extraction, andSelectionBig Data TechnologiesApache Spark & PySparkDask and RayStatic Visualization LibrariesMatplotlibFiguresAxesSubplotsSeabornStatistical DataVisualizationInteractive VisualizationPlotlyInteractive GraphsBokehNetworkXNetwork & GraphVisualizationGeospatial VisualizationGeoPandasFoliumDashboards and ReportingStreamlitTableau / PowerBIDashSupervised Learning (Regression)Linear RegressionSimpleMultipleRidge, Lasso, andElastic Net RegressionRegularizationPolynomial RegressionSupervised Learning (Classification)Logistic RegressionK-Nearest NeighborsKNNSupport VectorMachinesSVMNaive BayesDecision TreesEnsemble MethodsRandom ForestGradient BoostingXGBoostLightGBMCatBoostUnsupervised LearningClusteringK-MeansHierarchicalDBSCANGaussian MixtureModelsDimensionalityReductionPCAt-SNEUMAPAssociation RulesAprioriFP-GrowthAnomaly DetectionTime Series AnalysisStationarity andAutocorrelationACF/PACFARIMA, SARIMA, andProphetExponential SmoothingRecommendation SystemsContent-BasedFilteringCollaborativeFilteringMatrix FactorizationModel Evaluation & TuningCross-ValidationK-FoldStratifiedTime-Series SplitEvaluation MetricsAccuracyPrecisionRecallF1-ScoreROC-AUCPR-AUCRegression MetricsMSERMSEMAEMAPER-squaredHyperparameter TuningGrid SearchRandom SearchOptunaHyperoptDealing withImbalanced DataSMOTEClass WeightsNatural Language Processing (NLP) FundamentalsText PreprocessingTokenizationStemmingLemmatizationBag of Words andTF-IDFWord EmbeddingsWord2VecGloVeSentiment Analysis andTopic ModelingReinforcement LearningMarkov DecisionProcessesMDPQ-Learning and ValueIterationPolicy GradientsDeep ReinforcementLearningDQNPPONeural Network FoundationsPerceptrons andActivation FunctionsForward and BackwardPropagationLoss Functions andOptimizersAdamSGDRMSpropRegularizationDropoutBatch NormalizationWeight DecayDeep Learning FrameworksTensorFlow / KerasBasicsPyTorch FundamentalsJAXOptional/AdvancedSpecialized ArchitecturesConvolutional NeuralNetworks (CNNs) forImage DataObject Detection(YOLO, Faster R-CNN)and Image SegmentationU-NetRecurrent NeuralNetworks (RNNs),LSTMs, and GRUs forSequential DataGraph Neural NetworksGNNsTransfer LearningResNetVGGEfficientNetGenerative AI & Large Language Models (LLMs)TransformersAttention MechanismSelf-AttentionHugging Face EcosystemLarge Language ModelsGPTLlamaMistralPrompt EngineeringRetrieval-AugmentedGenerationRAGParameter-EfficientFine-TuningPEFTLoRAQLoRAAI Agents and Tool UseLangChainAutoGenCrewAIDiffusion ModelsModel Serialization & RegistryPickle, Joblib, andSafetensorsONNX Format andTensorRTModel OptimizationQuantizationPruningDistillationModel RegistriesMLflow Model RegistryAPI Development & ServingFastAPIFlaskModel ServingTriton InferenceServerRay ServevLLMContainerization & OrchestrationDocker BasicsDockerfileImagesContainersDocker ComposeKubernetes BasicsCloud & CI/CDCloud BasicsAWSGCPor AzureServerlessArchitecturesGitHub Actions &GitLab CI for CI/CDML Lifecycle ManagementExperiment TrackingMLflowWeights & BiasesNeptuneData Pipelines andOrchestrationAirflowMagePrefectFeature StoresFeastHopsworksModel Monitoring andObservabilityEvidentlyArizeLLMOpsLangChainLlamaIndexLangSmithBusiness AcumenFraming BusinessProblems as DataProblemsDefining KPIs, OKRs,and Success MetricsROI Analysis for MLModelsCommunicationStorytelling with DataPresenting TechnicalFindings toNon-TechnicalAudiencesWriting TechnicalDocumentation andReportsEthics and PrivacyData PrivacyGDPRCCPAHIPAAAlgorithmic Bias andFairnessResponsible AIExplainable AISHAPLIMEGrad-CAMProject ManagementAgile MethodologiesScrumKanbanCRISP-DM Framework