Analysis of data in PythonLC-PYTHON-ANALYSIS

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Online (English)
  • 12.06 - weekend classes
  • 16.06 - day classes

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.

4.7/5 (98)
exempt from VAT

Price: 1190 EUR

ability to pay in 3 installments

refreshments included

computer station included

first minute (30+ days before) - 3%

Category: Python

The ability to find and analyze large data sets is a necessity in the modern world. Efficient data processing allows you to make effective and competitive decisions – and that guarantees business success.

Not every company can use the potential that lies in the data. To analyze large amounts, often incomplete information, statistical methods and knowledge of relevant technologies are necessary. You will get these skills on our course!

Python is one of the most popular languages ​​for data analysis because it has a lot of tools.

Pandas , NumPy , matplotlib and other popular packages represent a mature ecosystem of ready-to-use modules, and Python’s versatility allows you to download them as well, process and export in the form of reports as well as input files for other applications.

In our course, participants can broaden their knowledge about issues related to data analysis as well as learn about the most popular tools used for this purpose.

This training covers both subjects related to data analysis using Python and the use of acquired skills used in machine learning.




  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
        • dialing
        • vectorization
        • broadcasting
      • Elements of arithmetic and algebra using NumPy
        • Solving linear equations
    • Introduction to Pandas
      • Series and data frames
      • Acquiring data from various sources
        • Files
        • Resources on the Internet
        • Database
    • Preparing and cleaning data – DataFrame operations and transformations
      • Deleting columns and rows
      • Dimensional changes – reshaping
      • Pivoting
      • Rank and sort data
      • Combining frames (concatenate, merge, join)
  3. Data analysis
    • Visualizations
    • Introduction to matplotlib
    • Generating charts from the pandas level
    • 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
    • Division of machine learning methods
      • Supervised learning
      • Unattended learning
  6. Workflow work with machine learning
    • Data preparation
    • Model training
    • Model verification
  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
  10. How to choose the best model for the task?

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Audience and prerequisites

A course for people who know the basics of Python, who want to broaden their knowledge about issues related to data analysis, as well as learn the tools used for this purpose.

No requirements. However, basic knowledge of Python will be an additional advantage.


Course participants receive completion certificates signed by ALX.

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