KNIME data cleaning chAllenges


KNIME data cleaning chAllenges
KNIME data cleaning challenges
Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Data cleansing with KNIME Data wrangling No coding ETL Tool Data preparation with KNIME

What you’ll learn
You improve your KNIME skills by solving data cleaning challenges
You learn and apply new KNIME nodes
You can transfer techniques you learn here to your own data problems
You continue your KNIME learning journey with me together

Requirements
This course was requested by students and assumes prior knowledge with KNIME
If you have no prior knowledge with KNIME I highly recommend my KNIME – a crash course for beginners course first
no coding required
KNIME can be downloaded for free
Description
KNIME data cleaning challenges

The world of today and tomorrow is mainly driven by data. Companies need people with skills in data preparation and extraction.
What about spending less time, using a tool which requires no coding (allows it but does not require it!), which is fun to work with and has incredible data cleaning capabilities? That means, more fun and working faster!

This course is hands on and gives you the chance to learn and increase your skills in KNIME by facing data cleaning challenges

No matter if you are a business user working with data, a business user, a data analyst, data scientist or data engineer, KNIME is the right tool for you.

The goal is to learn KNIME nodes and concepts which can be transfered to your own data cleaning challenges.

If you have no prior knowledge then I highly recommend to take my beginners course "KNIME – a crash course for beginners" first. There you learn all the basics in a complete case study to get you up to speed. Based on this knowledge you can improve your skills with this and my other KNIME courses you find here.

I am convinced the course content provides value and will help you in your daily work and / or future career in our data driven world.

Are you ready? Then let’s go!

Who this course is for
Data Analysts
Business Users
Data Scientists
Data Engineers

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