Data cleaning and transformation
Case Study: Financial Analysis in KNIME
Andrew Logan
Data Scientist, 4&8 Ltd
Sticking to the plan
Clean, useful, and consistent data
Cleaning: missing values, deduplication, removing unnecessary rows and columns, etc
Transformation: string manipulation, numerical calculations, renaming columns etc
Sparkling clean
Most common nodes: Missing Value, Duplicate Row Filter, Column Filter, String Cleaner
Annotate!
Sparkling clean
Most common nodes: Missing Value, Duplicate Row Filter, Column Filter, String Cleaner
Annotate!
Many others: use the KNIME forum -
https://forum.knime.com/
or search engine for special cases
Making useful changes
Manipulating data to make it useful
String manipulation
Making useful changes
Manipulating data to make it useful
String manipulation
Numerical calculation
Making useful changes
Manipulating data to make it useful
String manipulation
Numerical calculation
Some other transformation nodes
Annotations!
Easier to understand and re-use
Metanodes - the towns on the map.
Easier to understand and re-use
Metanodes - the towns on the map.
Many nodes into one
Easier to understand and re-use
Metanodes - the towns on the map.
Many nodes into one
Time saver
Easy to understand
Easy to annotate
Let's practice!
Case Study: Financial Analysis in KNIME
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