Python for data analytics (EN/NL/FR)

In this 3 day ABIS course we learn how we can analyse data using Python. We look at the possibilities of Python in the field of data analysis and visualisation, including popular libraries such as NumPy, Pandas and Matplotlib.

Basic Python knowledge is a prerequisite for this training, as well as some notions of statistics; aimed at whoever wants to start practising data analysis, and anyone who needs to manipulate, visualize, or summarize their corporate data.

Remark: Course description in English; Dutch and French versions are available on the ABIS website. Courses are planned in Dutch, English, and French. Consult the ABIS website for alternate course formats.

Description:

During this training, we learn how we can analyse data using Python. In the last few years, the fields of data analytics and data science became of great importance throughout the professional world. At the same time, we notice a spectacular growth in the popularity of Python in this field: it is on its way to become the leading language in scientific and business data analysis. Python's general strengths as a multi-purpose programming language, combined with a number of specialised libraries, make it a very valuable tool for exploring, analysing, and visualising all sorts of data.

Main topics:

• Reading and writing data
  • Text files, CSV
  • Databases
  • Microsoft Excel
  • XML, HTML, JSON
• Data Wrangling and exploratory analysis
  • Concatenating, merging and joining data
  • Transforming data
  • Pivoting
  • Handling missing data
• Data aggregation and Grouping
• Regular Expressions
• Visualisation
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebooks
• Other useful packages

Intended for/Audience: This training is aimed at whoever wants to start practising data analysis: developers, data scientists, data architects, marketeers, and anyone who needs to manipulate, visualize, or summarize their corporate data.

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

Training Method/Didactics: Classroom training with demos and practical exercises.

Duration: 3 days.