Data manipulation & visualisation in Python | Knime | Excel

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Learn to pre-process and visualize data frames by using popular analytical software: Python & Knime & Excel.

What you’ll learn

  • Work with data frames (data wrangling | manipulation | visualisation) to prepare and understand your data
  • Work with Knime analytics platform environmnet
  • Undestand the Python’s syntaxes and how to work in Jupyter Notebook environment
  • Use Pandas, Matplotlib, Seaborn API’s enabling to transform data frames and create charts and plots in Python
  • Model and transform data
  • Visualise the data in charts and plots
  • Read data and work with more and different file types at one place
  • Join and merge different data
  • Group and pivot data by selected parameters
  • Modify, filter, resort, split, filter data, handle with missing values
  • Use basic math formulas on the columns
  • Use feature scaling to normalize your data under one common range
  • Visualise data by using different plots and charts (box plot, pie chart, scatter plot, line plot, histogram/column chart)
  • Transpose tables
  • Understand the Knime, Python and Excel environment


  • access to computer or laptop with Windows (32bit or 64 bit), Linux (64bit) or Mac (64bit) and with permission to download softwares (if not, ask your administrator to download it for you – it is common at company´s computers)
  • no prior skills required (basic data analyzing experience in different programs is an advantage)


We will focus on the most time-consuming part of the machine learning process which is the data exploration consisting from data visualisation and data wrangling serving for preparing and understanding your data.

The whole course is full of different data manipulation and visualisation hands-on exercises in three popular data science platforms:

1. open-source and very progressive programming language Python

2.  open-source, highly intuitive and effective analytics platform KNIME

3. the most popular for people working with data MS Excel,

where we we will load data, transform

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