Basics of Statistics in Python with pandas, sklearn, matplotlib, and seaborn Libraries (PYTHAI1)

Programming, Python

This training focuses on the basics of statistics in Python and the pandas, sklearn, matplotlib, and seaborn libraries. Participants will learn about data analysis and interpretation, fundamental principles of machine learning, and data analysis visualization. The training includes descriptive statistics, probability and distribution, relationships between variables, inferential statistics, and working with categorical variables.

Participants will gain the skills needed to analyze and correctly interpret data, understand the basic principles of machine learning, and communicate data analysis conclusions to colleagues, superiors, subordinates, and business partners using data visualization libraries.

COURSE LOCATION AND AVAILABLE DATES



Public courses are usually delivered in Czech, but this course is also available in English. We can arrange private training for your team online, at your premises or in our classrooms, and tailor the content to your needs.

For groups of around 4 or more participants, private training can already be comparable in price to booking individual places on a public course. Send us your requirements and we’ll recommend the best format and provide an exact quote.

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Course content:

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  • Descriptive statistics
    1. Mean, median, variance, standard deviation, quartiles
    2. Calculation of descriptive statistics with pandas
    3. Visualization of descriptive statistics using matplotlib and seaborn
  • Probability and distribution
    1. Basics of probability
    2. Discrete and continuous distribution: binomial, normal, exponential, t-student
    3. Calculation of probability and percentiles with Python
  • Relationships between variables
    1. Correlation: Pearson's, Spearman's
    2. Calculation of correlation with pandas and visualization using seaborn
    3. Regression analysis: linear regression, least squares method
    4. Implementation of linear regression with sklearn
    5. Inferential statistics
  • Hypothesis, null hypothesis, alternative hypothesis
    1. Type I and II errors
    2. Basic tests: t-test, ANOVA, chi-square
    3. Implementation of tests with Python using the scipy library
  • Inferential statistics
    1. Hypothesis, null hypothesis, alternative hypothesis
    2. Type I and II errors
    3. Basic tests: t-test, ANOVA, chi-square
    4. Implementation of tests with Python using the scipy library
  • Working with categorical variables
    1. Identification of categorical variables
    2. Conversion of categorical variables to numerical
    3. Implementation of conversion of categorical variables with pandas and sklearn
Prerequisites:
Basic knowledge of Python and the Pandas library, ideally at the level of PYTH1 training
Recommended previous course:
Python – Programming Basics (PYTH1)
Schedule:
2 days (9:00-17:00)
Price per person:
752.00 € ( 909.92 € incl. 21% VAT)
Language:
Česky

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