Python for Data analytics

Get familiar with the Python stack for Data Science, and automate tasks such as aquisition, cleaning, processing and analysis of data.

Description of the course

The Python Data Analytics syllabus is designed to help analysts, researchers, BI experts, data scientists and developers becoming fluent in the use of the Python programming language, with the purpose of automating tasks such as acquisition, cleaning and analysis of digital data.

Using interactive examples and hands-on exercises, the course starts with a 1-day introduction to the core concepts of Python programming, which provides participants with the foundations to achieve a high degree of independence and flexibility for their analysis process. The course then continues with two days focused on how Python tools can help the audience performing data wrangling and data analysis effectively.

Learning objectives

By attending this course, you will learn about:

  • Running Python code with Jupyter and Anaconda
  • Core concepts of the Python language
  • Loading, filtering, sorting, transforming, aggregating, analysing and plotting data with pandas
  • Mathematical functions and array operations with NumPy
  • Visualising data with matplotlib and plotly


Environment Set-up
  • The Anaconda distribution as Python Data Science platform
  • Overview on Python virtual environment set-up
  • Running code in Jupyter notebook
Python core concepts
  • Built-in data types in Python
  • Control flow statements
  • Defining and using custom functions
  • Working with dates and times
  • Accessing data on file (CSV, JSON, ...)
Python Data Science libraries
  • pandas:
    • Working with table-like data in pandas
    • Loading data from file into DataFrame objects
    • Data transformation and indexing
    • Summary statistics over DataFrame objects
    • Data aggregation queries (groupby() method)
    • Exploratory analysis of new datasets
    • Data visualisation over DataFrames
    • Join/merge operations with DataFrames
    • Time series operations in pandas
    • Working with text data in DataFrames
  • NumPy:
    • Working with NumPy arrays
    • Essential operations with NumPy arrays
    • Stats and linear algebra with NumPy
  • matplotlib vs plotly for Data Visualisation

Get in touch

For any enquiry about our services, please contact: