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    Admission> LLL programmes> LLL programme details

    LLL programme details

    LLL programmesLLL programme details

    Data Analysis Methodology: From Basics to Machine Learning Methods Application (C-K446-0001)

    Data Analysis Methodology: From Basics to Machine Learning Methods Application
    full-time
    Czech
    "The course provides participants with a comprehensive insight into a wide range of data analysis methods. It begins with basic data analysis methodology, which includes describing one-dimensional and two-dimensional data, signal sampling, and basic statistical signal processing methods.

    Participants will be introduced to nonlinear regression analysis, principal component analysis, and gradually progress to more sophisticated methods such as support vector machine (SVM). These methods are crucial for understanding complex patterns and relationships in data and enable more precise modeling and prediction.

    Throughout the course, participants will also learn to use decision trees, which are useful for both classification and prediction, and gradient methods, which are important for model optimization. These techniques enhance participants' ability to analyze and interpret data and enable them to work more efficiently with larger and more complex datasets.

    The course also emphasizes the importance of validation and evaluation of classification accuracy, which are key steps in working with data and models. Participants will learn to properly evaluate the results of their models.

    Overall, the course provides participants with a broad overview of basic and advanced data analysis methods and enables them to apply these techniques to real-world data in various domains, from academic research to industrial applications. Thus, the course equips participants with the necessary skills and knowledge for successful and efficient work in the field of data analysis."
    Data Analysis Methodology: From Basics to Machine Learning Methods Application
    Introduction to Programming and Python Language 1
    Getting acquainted with the Python language, introduction to possible IDEs, basic work with variables, loops, conditions

    Introduction to Programming and Python Language 2
    Functions, libraries, modules, installation and importing libraries and modules

    Working with data in tabular form, simple data visualization
    Pandas library - DataFrame, statistical summaries, data indexing in DataFrame, filtering, aggregation, data removal, correlation matrix, loading various data types (read_csv, read_sql)
    Seaborn library - Relational plots, bar plots, histogram, heatmap

    Statistical data evaluation
    Normality tests of data and use of the SciPy library
    Testing hypotheses of data independence - Fisher's exact test, Boschloo's test, chi-square test

    Linear regression, introduction to nonlinear regression, validation of regression models, regularization
    Working with SciPy and NumPy libraries

    Data transformation - normalization, standardization, encoding, PCA, LDA
    Introduction to the Scikit-learn library

    Classification models #1: Logistic regression; Evaluation of classification model quality
    Issues in evaluating and comparing classification models, creating models based on logistic regression

    Classification models #2: Support Vector Machine, Decision Trees, Random Forests; differences between binary classification and multi-class classification
    Advanced classification models and the multi-class classification problem
    10
    none
    podání přihlášky a zaplacení účastnického poplatku

    • Course requirements and qualification acquired

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      aktivní účast
      Osvědčení o absolvování programu
      nejsou uvedeny

    • Guarantor and venue

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      Vrba Jan Ing. Ph.D.
      Department of Mathematics, Informatics and Cybernetics
      Department of Mathematics, Informatics and Cybernetics
      Data Analysis Methodology: From Basics to Machine Learning Methods Application
      Steinbach Jakub Ing.
      Vrba Jan Ing. Ph.D.

    • Dates and duration

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      2024/2025
      summer semester
      21.02.25
      1
      32 hours
      8 lekcí po 4 hodinách = 32 hodin

    • Fees and financing

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      14900 Kč / kurz

    • Application

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      On-line
      Steinbach Jakub Ing.
      Jakub.Steinbach@vscht.cz
      220443773
      01.05.2024 - 01.02.2025