AWS - Certified Solution Architect

This guide provides necessity materials and practice test for free to learn and appear for AWS Architect Examination. ComputeCompute PracticeStorageStorageDatabaseDatabaseMigrationMigrationNetworking and Content DeliveryNetworking & Content DeliveryManagement ToolsManagement ToolsMedia ServicesMedia ServicesAnalyticsAnalyticsSecurity, Identity and ComplianceSecurity & ComplianceApplication IntegrationApplication IntegrationDesktop & App StreamingDesktop & App StreamingAdditional Services & ToolsAdditional Services & Tools Read the full article

Mathematics for Data Science

Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in data science. Beyond the basics of calculus, linear algebra, and probability, there is a certain kind of mathematical thinking that comes up pretty often when you’re trying to understand data. It involves quantifying something you want to measure, then understanding how the quantification works in mathematical terms. The interesting part is not usually doing the math, but figuring out what math to do. Most of the mathematics required for Data Science lie within the realms of statistics and algebra, Statistics, in particular, is at the very foundation of Data Science, and is the collection of tools which helps us separate significance from randomness. Algebra is quite often at the heart of the analysis itself. The further quantitative skills facilitate intuition, which is essential in analytics. Data-scientist should have a knowldge about one or more of this topics : Linear algebraDiscrete mathDifferential equationsTheory of statisticsNumerical analysis : numerical linear algebra and quadratureAbstract algebraNumber theoryReal analysisComplex analysisIntermediate analysisProbability and StatisticsLinear AlgebraMatrix Theory Calculus Set theory 👉 Here are some of the Useful resources to improve your Math skills & Data Science Expertise-

MUST READ Books

1) The Elements of Statistical Learning(Springer Series) 2) Introduction to Linear Algebra by Gilbert Strang. 3) Naked Statistics by Charles Wheelan. 4) An Introduction to Statistical Learning: with Applications in R. 5) Pattern Recognition and Machine Learning by Christopher M. Bishop. 6) Pattern Classification ((A Wiley-Interscience publication). 7) Introduction to Statistical Learning 8) Introduction to Bayesian Statistics Must Know Algorithms for Data Scientist AlgorithmsLibraryTutorialPrincipal Component Analysis(PCA)/SVDhttps://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.svd.html http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html https://arxiv.org/pdf/1404.1100.pdfLeast Squares and Polynomial Fittinghttps://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.htmlhttps://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.polyfit.htmlhttps://lagunita.stanford.edu/c4x/HumanitiesScience/StatLearning/asset/linear_regression.pdfConstrained Linear Regressionhttp://scikit-learn.org/stable/modules/linear_model.htmlhttps://www.youtube.com/watch?v=5asL5Eq2x0A https://www.youtube.com/watch?v=jbwSCwoT51M K means Clusteringhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.htmlhttps://www.youtube.com/watch?v=hDmNF9JG3lo https://www.datascience.com/blog/k-means-clusteringLogistic Regressionhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.htmlhttps://www.youtube.com/watch?v=-la3q9d7AKQSVM (Support Vector Machines)http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.htmlhttps://www.youtube.com/watch?v=eHsErlPJWUUFeedforward Neural Networkshttp://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html https://github.com/keras-team/keras/blob/master/examples/reuters_mlp_relu_vs_selu.pyhttp://www.deeplearningbook.org/contents/mlp.html http://www.deeplearningbook.org/contents/autoencoders.html http://www.deeplearningbook.org/contents/representation.htmlConvolutional Neural Networks (Convnets)https://developer.nvidia.com/digits https://github.com/kuangliu/torchcv https://github.com/chainer/chainercv https://keras.io/applications/http://cs231n.github.io/ https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/Recurrent Neural Networks (RNNs)https://github.com/tensorflow/models https://github.com/wabyking/TextClassificationBenchmark http://opennmt.net/http://cs224d.stanford.edu/ http://www.wildml.com/category/neural-networks/recurrent-neural-networks/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/Conditional Random Fields (CRFs)https://sklearn-crfsuite.readthedocs.io/en/latest/http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/ https://www.youtube.com/watch?v=GF3iSJkgPbADecision Treeshttp://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html http://xgboost.readthedocs.io/en/latest/ https://catboost.yandex/http://xgboost.readthedocs.io/en/latest/model.html https://arxiv.org/abs/1511.05741 https://arxiv.org/abs/1407.7502 http://education.parrotprediction.teachable.com/p/practical-xgboost-in-pythonTD Algorithms https://github.com/keras-rl/keras-rl https://github.com/tensorflow/minigohttps://web2.qatar.cmu.edu/~gdicaro/15381/additional/SuttonBarto-RL-5Nov17.pdf https://www.youtube.com/watch?v=2pWv7GOvuf0 Read the full article