Advanced Data Analysis in Python (PYDATA2)

Programming, Python

Our Advanced Data Analysis in Python course is designed for users with a basic understanding of Python (Pandas library) who want to learn how to analyze data more effectively. You will learn advanced dataframe transformations, how to combine data from multiple dataframes, data visualization, and working with time series.

Furthermore, you will get acquainted with linear regression and text data analysis. This course will provide you with practical experience in advanced data analysis in Python.

THIS TRAINING COURSE WILL HELP YOU:

    Analyze larger data sets or information from multiple data sets at once, prepare a data set for use in machine learning models, database export, or visualization, create time series analysis, even over your own time series (typically shifts in the company, working days in the company, etc.), create full-fledged and even interactive dashboards, work and analyze text values more easily, grasp the concept of machine learning

WHO SHOULD ATTEND?

    Users with basic knowledge of Python (Pandas library) who want to significantly simplify working with large data, users with basic knowledge of Python who are looking for a way to analyze and visualize data in one environment

COURSE LOCATION AND AVAILABLE DATES



This course is not scheduled as a public course.
It is delivered exclusively as customised training. The content, duration, date, and delivery format are tailored to the customer's requirements and can also be provided in English.

Request customised training

Course content:

Hide details
  • Advanced dataframe transformations
    1. Conversion of columns to rows and vice versa
    2. Working with list data type columns
    3. Creation of contingency tables
    4. Combining data from multiple columns into one list data type column
  • Combining data from multiple dataframes
    1. Stacking data
    2. Joining data side by side
  • Data visualization
    1. Setting up the visualization window
    2. Setting properties of individual graphs
    3. Options for drawing multiple graphs in one window
    4. Basics of creating interactive reports
  • Working with time series
    1. Moving by rows
    2. Moving by a defined time period
    3. Creating your own calendar (e.g., work time calendar, working days calendar, etc.)
  • Linear regression
    1. Introduction to the issue of linear regression
    2. Data preparation for the sklearn library's linear regression model
    3. Verification of model accuracy, interpretation of results
  • Text data analysis
    1. Data normalization
    2. Clustering of similar texts (e.g., typos, texts without diacritics, etc.)
Prerequisites:
Knowledge of Python language and Pandas library
Recommended previous course:
Data Analysis in Python (PYDATA1)
Schedule:
3 days (9:00-17:00)
Price per person:
672.00 € ( 813.12 € incl. 21% VAT)
Language:
Česky

Training and learning environment