Course content

  • Statistical Methods, Data Science, Machine Learning

    Statistics I
    Statistics II
    kNN
    Metrics and Evaluation
    Metrics and Evaluation II
    Regularization
    Bayes theorem
    Decision Trees
    Regression
    Preprocessing
    Dimensionality Reduction
    LDA
    PCA
    k-Means

  • Advanced methods, Neural Networks and Deep Learning

    Pattern recognition
    Non-linear Regression
    SVD
    Ensembles
    Convolutional Neural Networks
    Deep Learning
    Other Neural Networks

  • Hands-on experience

    Students will gain hands-on experience by working in Jupyter Notebooks, using industry-standard Python libraries such as Pandas, NumPy, Matplotlib, and more. They will apply their skills by solving Kaggle challenges, reinforcing both data analysis and machine learning techniques. By the end of the program, participants will be prepared for both the scientific applications in research and academia, and theoretical and practical assessments commonly used in job interviews at tech companies.

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