Course objectives

To get familiarized with Python as a tool for automated data processing, which involves data analysis and visualization, model building, assessment and validation, prediction, as well as reporting results of the analysis.

Training scope
  • Introduction to statistical data analysis
    • Supervised and unsupervised learning
    • Classes and examples of models
    • Methods of model estimation/calibration
    • Principles of statistical hypothesis testing
    • Model validation and selection
    • Overfitting (bias-variance trade-off)
  • Data Science techniques that will be implemented:
    • Linear and nonlinear regression models
    • Artificial neural networks
    • Decision trees and random forests
    • Bayesian classifiers
    • Ensemble modelling
    • Confusion matrix, ROC curve
    • Model quality statistics
    • Cross-validation
  • Model development with Python:
    • Python as a programming language
    • PyCharm – an IDE for Python
    • Jupyter Notebooks – documents with live code
  • Pandas for:
    • Import and export of data
    • Data structures  and data manipulation
  • NumPy for linear algebra and numerical computation
  • Scikit-learn for:
    • Regression
    • Classification
    • Clustering
    • Model validation and selection

Data Scientists or anyone who wants to use Python as a tool for automated data analysis. This is an introductory level training. Utilized ML methods and programming techniques will be explained,  but it is assumed that participants have a basic experience with Python.

The number of participants: 8-15 people

Duration: 2 days

Available language: PL / EN

Available course material: PL / EN

Course form
Workshops (lectures + practical case studies in Python).

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Agnieszka Rzeczkowska

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Agnieszka Rzeczkowska

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