In recent years, the importance of data analytics and data science in the professional world has increased significantly. At the same time, we have observed that Python has taken a leading role as a programming language in this field. Today, it is perhaps the most essential tool for data analysis in science and the business world. Python’s strengths as a general-purpose programming language, combined with specialised libraries like Numpy, Pandas, and Matplotlib, make it an excellent tool for exploring, analysing, and visualising various types of data.

In this course we will learn:

• How to import data from various sources into a Python environment;

• How to process and prepare data for analysis;

• How to analyse data using Numpy and Pandas;

• How to visualise the results of our analysis.

• Reading and writing data
  • Text files, CSV
  • Databases
  • Microsoft Excel
  • JSON, XML, HTML
• Data Wrangling and exploratory analysis
  • Combining data: appending, concatenating, merging and joining data
  • Transforming data
  • Pivoting
  • Handling missing data
• Data aggregation and Grouping
• Working with text
  • Universal string operations
  • Regular Expressions (optional)
• Visualisation
• The Scientific Python ecosystem (SciPy)
  • NumPy
  • Pandas
  • Matplotlib
  • Jupyter Notebooks and IPython
• Other useful packages

Classroom training with demos and extensive practical exercises.

Delivered as a live, interactive training: available in-person or online, or in a hybrid format. Training can be implemented in English, Dutch, or French.

Basic Python knowledge is a prerequisite for this training, as well as some notions of statistics (see courses Python fundamentals and Statistics fundamentals).

This training is aimed at whoever wants to start performing data analysis: developers, data scientists, data architects, marketeers... and anyone who needs to manipulate, visualize, or summarize data.