Introduction to data analysis

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  • Data and Big Data Analysis: Approaches, Functions and Software Tools
    • The 1-st module explores the concept of data analysis and introduces
      basic techniques of this analysis. It discusses the concept of big data
      and its possible applications. It also considers the relationship between
      different approaches to process data as well as basic software for data
      analysis. Some useful functions for data analysis are presented. The
      principles of big data processing are discussed, in particular the
      MapReduce model.
  • Basic Characteristics of Data. Distributions, Statistics and Regressions
    • In Module 2, descriptive statistics and exploratory data analysis are
      discussed. The main characteristics of data distributions are introduced
      and their calculations are presented in some examples. Frequency and
      Bayesian approaches to hypothesis testing are explained. The basic concepts
      of regression and correlation analysis are formulated, focusing on linear
      analysis methods.
  • Clustering and Dimensionality Reduction
    • Module 3 discusses the clustering problem and the algorithms for solving it.
      Hierarchical clustering, k-means algorithm and CURE-algorithm are explained.
      Peculiarities of the algorithms operation in non-Euclidean space are specified.
      The module also covers some questions of dimensionality reduction, the basic
      facts of singular value decomposition, and illustrates its applications.
      It also considers the principal component analysis and CUR-decomposition,
      applicable for big data processing.
  • Machine Learning and Artificial Neural Networks
    • Module 4 discusses models and methods of machine learning. The model of the
      perceptron, its functioning, advantages and disadvantages are discussed in
      detail. The basic support vector machine and its generalizations are
      considered. Further it discusses artificial neural networks, their
      organization and training. The main features of deep neural networks,
      problems that appear with such networks and modern methods to overcome
      these problems are discussed. The convolutional and recurrent neural networks
      are also considered.