From Excel to Python | Knime: preprocess and visualize data

Description

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 them and visualise them.

So, what will we cover during this course?

• Start with the KNIME analytics platform (installation and environment description)

• Start with Python  (installation and environment description)

• Gathering the data into all platforms (data from Excel and csv)

  
  

• Data manipulation (preparation and transformation) I - Table, Row transform, Row filter and split

• Data manipulation (preparation and transformation) II - Column Binning, Column Convert and replace, Column Filter, Column Split, Column Transform, 

• Data manipulation (preparation and transformation) III - Other data types – date and time.

• Data manipulation - Feature scaling

• Data visualisation -  Histogram, Line plot, Pie chart, Scatter plot, Box plot

What You Will 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

Who Should Attend!

• data analysts, data scientists and those of you willing to learn new things
• anyone searching open-source, user-friendly, easily understandable and highly effective SW for data analyzing and machine learning tasks
• people working with data (also with big data)

TAKE THIS COURSE