Analysis of data in PythonLC-PYTHON-ANALYSIS

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Remote training: online live with a trainer and a group. Available on demand, at time and place convenient to you, for groups of at least 7 participants.

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Price: 1190 EUR

ability to pay in 3 installments

refreshments included

computer station included

first minute (30+ days before) - 3%

  1. Analyst working environment
    • Anaconda
      • Conda package manager
      • Pip Manager
      • Creating a virtual environment
    • Jupyter notebook
      • Markdown
      • Elements of Latex notation
  2. Data processing
    • Introduction to NumPy
      • Creating vectors and matrices
      • Transformations, operations in NumPy
        • Selection
        • Vectorisation
        • Broadcasting
      • Elements of arithmetic and algebra using NumPy
        • Solving linear equations
    • Introduction to Pandas
      • Series and data frames
      • Retrieving data from different sources
        • Files
        • Online resources
        • Databases
    • Data preparation and cleaning – Operations and transformations DataFrame
      • Deleting columns and rows
      • Dimensional changes – reshaping
      • Pivoting
      • Ranking and sorting data
      • Combining frames (concatenate, merge, join)
  3. Data analysis
    • Visualizations
    • Introduction to matplotlib
    • Graph generation from within pandas
    • Seaborn and other tools for data visualization in Python
    • Basics of statistical analysis
    • Statistical inference
  4. Introduction to machine learning.
  5. Overview of machine learning methods and algorithms
    • Breakdown of machine learning methods
      • Supervised learning
      • Unsupervised learning
  6. Machine learning process
    • Data mining
    • How to choose the best model for the task
    • Data preparation
    • Learning dataset
    • Test dataset
    • Model training
    • Model validation
    • Model overfitting
    • Techniques for data-dimensionality reduction
  7. Overview of machine learning methods
    • Regression
      • Linear regression
      • Polynomial regression
      • Logistic regression
    • Classification
    • Data grouping
    • Dimension reduction
    • Artificial Neural Networks
  8. Combining classifiers
  9. Visualizing the results
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