Course Data Analysis in PythonLC-PYTHON-ANALYSIS

The course is available on demand.

Online (English)
  • 22.02 - weekend classes (Sat-Sun, on average every 2 weeks)

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

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exempt from VAT

Price: 1290 EUR

ability to pay in 3 installments


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  1. Introduction to programming in Python
    • Genesis and history of Python
    • Applications and possibilities
  2. Installation and configuration of the environment
    • Python interpreter
    • Virtual environment (venv)
    • Integrated development environment (IDE)
  3. Basics of Python syntax
    • Interaction with the user
    • Variables and basic data types
    • Data structures
    • Conditional statements
    • Loops
    • Comprehension expressions
  4. Procedural programming
    • Basics of defining functions
    • Passing arguments
    • Date and time handling (`datetime` module)
  5. Analyst’s work environment
    • Anaconda
      • Conda package manager
      • pip manager
      • Creating a virtual environment
    • Jupyter notebook
      • Markdown
      • Elements of Latex notation
  6. Data processing
    • Introduction to NumPy
      • Creating vectors and matrices
      • Transformations, operations in NumPy
        • Selection
        • Vectorization
        • Broadcasting
      • Arithmetic and algebra using NumPy
        • Solving linear equations
    • Introduction to Pandas
      • Data series and frames
      • Obtaining data from various sources
        • Files
        • Resources on the Internet
        • Databases
    • Data Preparation and Cleansing – DataFrame Operations and Transformations
      • Deleting columns and rows
      • Changing dimensions – reshaping
      • Pivoting
      • Ranking and sorting data
      • Combining frames (concatenate, merge, join)
  7. Data analysis
    • Visualizations
      • Introduction to matplotlib
    • Generating charts from pandas
    • Seaborn and other data visualization tools in Python
    • Basics of statistical analysis
    • Statistical inference
  8. Introduction to machine learning
  9. Review of machine learning methods and algorithms
    • Machine learning methods
      • Supervised learning
      • Unsupervised learning
  10. Machine learning process
    • Data mining
    • How to choose the best model for the task
    • Data preparation
      • Training set
      • Test set
    • Model training
    • Model validation
    • Model overfitting
    • Data dimensionality reduction techniques
  11. Overview of machine learning methods
    • Regression
      • Linear Regression
      • Polynomial regression
      • Logistic regression
    • Classification
    • Data grouping
    • Dimension reduction
    • Artificial Neural Networks
  12. Combining classifiers
  13. Visualizing results
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