Large-scale data analysis in Python

Why choose this training?

Standard Python data analysis tools quickly hit limitations when working with large datasets — pandas slows down, RAM runs out, and processing takes hours instead of minutes. This two-day training prepares analysts and data engineers for efficient work with large data volumes, from pandas optimization through modern libraries Polars and Vaex, to distributed processing with Dask and PySpark. The program also covers columnar formats Parquet and Arrow, memory profiling and strategies for choosing the right tools depending on data scale.

After completing the training, participants will be able to: optimize pandas for performance and memory usage, process distributed datasets using Dask, use the Polars library as an efficient alternative to pandas, work with columnar formats Parquet and Arrow. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: data analysts working with large datasets in Python, Data Scientists looking for more efficient data processing tools, data engineers building analytical pipelines.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 2 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.