Python – practical use of Python for data analysis and automation

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Confident Python basics - You will learn Python syntax, data types, variables, loops, and conditionals, so you can write clear scripts on your own for everyday analytical and data handling tasks.

Efficient work setup - You will configure IDLE, Jupyter, and PyCharm and learn how to use them smoothly, helping you test code faster, explore data more easily, and keep projects organized.

Reliable data structures - You will work with lists, tuples, dictionaries, and sets in practical exercises, making it easier to organize input data, prepare it for analysis, and keep your scripts tidy.

Data analysis with Pandas and NumPy - You will filter, group, and aggregate data in Pandas and perform fast numerical operations with NumPy, so you can analyze real datasets more efficiently and with confidence.

Data import and export - You will learn how to read and save data from CSV, Excel, and SQL sources, making it easier to connect Python with the files and databases used in your daily work.

Clear data visualizations - You will create charts in Matplotlib, Seaborn, and Plotly, from basic plots to heatmaps and interactive visuals, so you can present insights and patterns more effectively.

Automated reporting - You will build reports in Jupyter Notebook and learn to generate PDF and HTML outputs automatically, helping you deliver updated results regularly without manual repetition.

Practical machine learning intro - You will explore regression, classification, and clustering in Scikit-Learn and follow the main model-building steps, so you can start applying machine learning to data analysis.

Training programme

1. Introduction to the Python language

introduction,
basics of programming,
data types: numeric, boolean, text,
variables.

2. Environment preparation

installation of the IDLE/Jupyter environment,
operation of the IDLE/Jupyter interface,
PyCharm environment.

3. Basic operations

assignment statement,
arithmetic calculations.

4. Data structures

sequences (lists, tuples, ranges),
dictionaries,
sets.

5. Basic instructions

loops,
conditional instructions,
function Range.

6. Lists

indices,
methods,
functions,
modification of lists.

7. Library

rules of use,
example libraries and their functions.

8. Data analysis

import/export,
reading and writing data from a file.

9. Numerical analysis - statistics and arithmetic

basic statistical measures: mean, median, variance, standard deviation,
arithmetic operations on numerical data.

10. Pandas Library

introduction to Pandas,
data structures: Series, DataFrame,
operations on DataFrame: filtering, grouping, aggregating,
data manipulation: adding, removing, modifying data,
loading data from various formats: CSV, Excel, SQL,
basic statistical and analytical operations.

11. NumPy Library

introduction to NumPy,
NumPy arrays (ndarray): creation, indexing, modification,
basic mathematical operations and NumPy functions,
advanced functions: matrix operations, linear algebra,
applications in scientific computing.

12. Visualization and reporting

code organization,
code documentation,
effective management of data and analysis results.

13. Matplotlib Library

introduction to Matplotlib,
creating basic charts: line, bar, pie,
customizing charts: titles, axis labels, legends,
saving charts to files.

14. Reporting automation

generation of reports in PDF, HTML format,
automatic updating of charts and data in reports.

15. Machine Learning – Scikit-Learn library

introduction to the Scikit-Learn library,
basic algorithms: regression, classification, clustering,
model building process: data preparation, model training, evaluation,
integration with the Pandas and NumPy libraries in the context of data analysis.

16. Advanced visualization techniques with Matplotlib and Seaborn libraries

overview of the capabilities of the Matplotlib and Seaborn libraries,
creating multidimensional charts,
box plots (boxplots) – analysis of data distribution,
Heatmaps – visualization of correlation matrices,
scatter plots divided into categories,
customizing chart styles and aesthetics,
creating multi-panel charts and subplots.

17. Interactive visualizations with the Plotly library

introduction to the Plotly library,
creating dynamic line, bar and scatter charts,
interactive heatmaps and bubble charts,
3D charts and their applications,
integration of Plotly charts with web applications and dashboards,
exporting charts to presentations and websites.

18. Creating dynamic reports with Jupyter Notebook

basics of working in Jupyter Notebook,
combining code, visualizations and text in one document,
creating interactive reports using Markdown,
generating dynamic charts in Jupyter Notebook,
using widgets for interactive data analysis,
automatically updating reports and saving results.

19. Automation of reporting with Pandas and Matplotlib libraries

creating PDF and HTML reports with analysis results,
automatic updating of reports based on new data,
generating charts and tables in an automated manner,
exporting results to Excel and other formats,
creating reporting scripts running in the background,
scheduling reports – automatic running of scripts.

What are the prerequisites for participating in the training?

Basic computer skills - You should be comfortable using a computer, launching applications, saving files, and moving through folders, because the course includes work in several development tools.

Working with files - You should understand what CSV files, Excel files, and working directories are and know how to find and save data, since the course covers importing and exporting datasets.

Basic math knowledge - You should know basic arithmetic and understand concepts such as mean and median, because the training includes numerical analysis as well as introductory statistics.

Readiness to code - You should be ready to type and correct short pieces of code on your own, because the training is hands-on and built around practical exercises in Python throughout.