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    • R
    •     Machine Learning with Python & R
    • Machine Learning
    •     Visualisation
    • Power BI
    •     Deep Learning with Python & R
    • Deep Learning
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    • Pure Maths
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    • The MNIST Database
    • Chars74K
    • ImageNet
    • Very large Datasets
    • Amazon Web Services (AWS) datasets
    • Google datasets
    • Youtube labeled Video Dataset
    • Text Datasets
    • Spam – Non Spam
    • Twitter Sentiment Analysis
    • Movie Review Data
    • General Datasets
    • data.gov
    • data.gov.in
    • World Bank
    • RBI
    • Five Thirty Eight Datasets
    • Sites with links to various sources
    • KDNuggets
    • Awesome Public Datasets
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    • Module 6:Data Munging
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    • Module 8:Pure Maths for computer science
    • Module 9:Model Evaluation
    • Module 10:Neural network
    • Module 11:Python| R |SQL|Hadoop|Spark
    •     Data Engineering
    • Module 1:CAP theorem
    • Module 2:Lamda Architecture
    • Module 3:Cassandra, Neo4j and other nosql databases
    • Module 4:The Storm distributed real time computation system
    • Module 5:Hadoop, HDFS, MapReduce, and other Hadoop/SQL technologies
    • Module 6:Spark framework
    • Module 7:Continuous deployment and delivery to the cloud.
    • Module 8:Microservices using containers to deliver reliable infrastructure.
    • Module 9:Vision systems
    • Module 10:RDBMS systems
    • Module 11:OLAP cubes and Business Intelligence systems
    • Module 12:Clustering: K-means, mixture models, hierarchical
    • Module 13:Dimensionality reduction and visualisation
    • Module 14:Inference: Bayes, MCMC
    • Module 15:Max-margin methods (SVMs)
    • Module 16:Bayesian networks
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    • Module 1:Risk Management & Valuation
    • Module 2:Financial Engineering
    • Module 3:Equity|Venture capital
    • Module 4:Stochasic Calculus
    • Module 5:Fintech innovation
    • Module 6:Investment|Portfolio Mng
    • Module 7:Financial Statistics
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