Data science and Data preparation with KNIME
KNIME - A powerful tool for data science and machine learning
Development,Data Science,KNIME
Lectures -21
Duration -4 hours
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Course Description
Data preparation, data cleaning, data preprocessing (whatever you want to call it) is quite often the most tedious and time-consuming work in the data science/data analysis area. Especially if we are short of time and want to deliver crucial data analysis insights to our audience.
KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.
In this course, we will learn the efficient ways to import multiple files into KNIME, loops, web scraping, scripting (using Python code in KNIME), hyperparameter optimization, and feature selection. Also, learn basic machine learning workflows and helpful nodes for this in KNIME.
By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.
All the resources and support files for this course are available at https://github.com/PacktPublishing/Data-science-and-Data-preparation-with-KNIME
Audience :
This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course.
Goals
- Enhance your basic KNIME skills already acquired
- Increase your productivity and save time in your data preparation tasks
- Discover what kind of loops are available and how to use them
- Learn how to use Python in KNIME
- Learn how to do data science in KNIME with and without coding
- Learn basic machine learning workflows and helpful nodes
Prerequisites
- Basic knowledge of machine learning is certainly helpful for the later lectures in this course.Â
- Note: Tableau Desktop and Microsoft Power BI Desktop are optional.
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Curriculum
Check out the detailed breakdown of what’s inside the course
Data Science and Data Preparation with KNIME
17 Lectures
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Course Introduction 01:04 01:04
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Reading Multiple CSV Files in Bulk into KNIME Update 22:07 22:07
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Reading Multiple Excel Files in Bulk into KNIME Update 14:49 14:49
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A Great Helper Node for Time Series Analysis in KNIME 06:31 06:31
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Examples of How to Use Loops in KNIME 05:53 05:53
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More on Loops in KNIME - Several Ways to Get the Same Result 05:42 05:42
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Loops - How to Split Data into Multiple Output Files 12:42 12:42
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Loops Recursion in KNIME 13:41 13:41
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Webscraping with KNIME 14:41 14:41
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Webscraping with KNIME - Financial Data 16:25 16:25
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Scripting - How to Use Python in KNIME 11:08 11:08
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Python in KNIME - Further Examples 09:36 09:36
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Hyperparameter Optimization in KNIME - Data Preparation 10:12 10:12
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Hyperparameter Optimization for Machine Learning Models Using Loops in KNIME 16:23 16:23
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Feature Selection in KNIME 15:34 15:34
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Machine Learning Prediction Process 09:50 09:50
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KNIME Logout 00:24 00:24
Older Videos KNIME Version Before 4.3
4 Lectures
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