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KNIME Analytics Bootcamp - ETL Tools for Data Science

KNIME Analytics Bootcamp - ETL Tools for Data Science

English | 2h 36m | Video 720p | Subtitles

Learn data analysis and manipulation using the most popular, open-source, and no-coding automation platform


 

What you'll learn

JOIN Types

Changing names of the column headers

Merging columns to create a new column

Pattern Matching Criteria

Range Checking Criteria

Finding Data Patterns

 

Requirements

Everyone who deals with the data on day-to-day basis

If you want to learn basics of KNIME

Anyone into Analysis, Machine Learning and AI

Description

Data pre-processing and coding is a prerequisite to move ahead in Data Science. KNIME eliminates those hurdles for you.

This course is for anyone who is familiar with tools such as Excel or Power Query (ETL). This course will help you get a head start in Data Science without any coding. 

We’ll learn some practical applications of data blending and manipulation and apply our knowledge in the real world and solve business queries immediately. Following are the topics that we'll cover in this course:

JOIN Types - Left Outer, Right Outer, Full Outer, Inner, Left Anti, and Right Anti

Splitting one column into two

Change the name of the column headers

Merge columns to create a new column

Find out list of Male/Female candidates (Pattern Matching Criteria)

Filter out people whose credit score exceed a certain limit (Range Checking Criteria)

Pivoting with multiple columns and complex aggregation methods

Finding data patterns

By the end of the course, we will feel comfortable working with the KNIME Analytics Platform.

Who this course is for:

If you use a lot of Excel and want to take your skills to the next level

Anyone who struggle to write VBA Macros code for automation

Anyone who is completely new to KNIME

Users who are looking for quick shortcuts, tips & tricks of KNIME

 

Homepage:

https://www.udemy.com/course/knime-essential-tools-a-practical-approach/

 

KNIME Analytics Bootcamp - ETL Tools for Data Science


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