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Data Analytics Assignment Help

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Some of the homework help topics include:

  • Data analytic thinking, data mining for knowledge discovery, data science solution for business problems, Predictive modellin, correlation learning, supervised learning
  • Regression, classification, support vector machines, overfitting , avoidance, Clustering data, unsupervised learning methods, visualizing model performance, evidence 
  • Probabilities, text mining , network data, troduction to data, relations and the fundamentals of data analytics, Data preparation, data pre-processing, and quality analysis
  • Data analysis – feature selection, classification and clustering,  predictive and forecasting modelling,association rule mining & text mining, Understanding big data analytics, Visual analytics

 Data Analytics:

  • Basic techniques in data analytics, manipulation of data for analysis, creation of data files from multiple and dissimilar sources, Overview of data mining algorithms , clustering, association analysis, probabilistic modeling
  • Matrix decompositionstree-based methods, Bayesian methods, logistic regression, ensemble, bagging, boosting methods, neural network methodsuse of support vectors, Bayesian networks, clustering methods
  • k-means, hierarchical, self-organizating map methods,Machines, Languages & Computation,Information & Information Systems,Programming Foundations,Calculus,Essential Statistics,Geometry & Algebra,Advanced Programming
  • Logic & Algorithms,User & Data Modelling,Probability & Statistical Inference,Statistical Computing,Linear Algebra,Multivariate Calculus,Building Software Systems,Inference & Regression Modelling
  • Stochastics & Financial Econometrics,Communicating Mathematics & Statistics,Univariate and multivariate statistics,Marginal probability,joint probability ,conditional probability,Granger causality,transfer entropy
  • Spurious correlations,regularization,Forecasting and regressions,Calibration,validation hypothesis testing,complex networks,data acquisition,representation and plotting,,analytics ,Statistics ,Databases ,Regression 
  • Clustering ,Data Structures,Data Sharing,Graphs,Empirical investigation of complex data,Essential practical familiarization with complex and big data Typical challenges with real business data ,data acquisition ,manipulation 
  • Cleaning ,filtering , representation and plotting,Univariate and multivariate statistics,Marginal probability ,joint probability and conditional probability ,Empirical estimation of probability distributions ,Measures of dependency 
  • Granger causality ,mutual information ,transfer entropy ,Spurious correlations and regularization ,Forecasting and regressions , Calibration ,validation hypothesis testing,Modelling and filtering through networks
  • Basics on complex networks:, Construction of networks of interactions form correlation  causality measures , Information filtering though networks,Management of data,data integrity & quality issues,data warehousing,data mining and business intelligence,Management issues of security,ethics for information ,knowledge resources

Few Topics are:

  • Empirical investigation
  • Univariate and multivariate statistics
  • Modelling and filtering
  • Probabilistic modelling
  • Constructing predictive probabilistic models
  • Test and validate model performances
  • Select between alternative models.
  • correlation and causality measures
  • Information filtering
  • Hypothesis testing and validation.
  • Forecasting and regressions
  • Spurious correlations and regularization

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