Data science and Data preparation with KNIME

Focused View

Dan We

4:00:27

181 View

01.01-course introduction.mp4

01:04

01.02-reading multiple csv files in bulk into knime update.mp4

22:07

01.03-reading multiple excel files in bulk into knime update.mp4

14:49

01.04-a great helper node for time series analysis in knime.mp4

06:31

01.05-examples of how to use loops in knime.mp4

05:53

01.06-more on loops in knime-several ways to get the same result.mp4

05:42

01.07-loops-how to split data into multiple output files.mp4

12:42

01.08-loops recursion in knime.mp4

13:41

01.09-webscraping with knime.mp4

14:41

01.10-webscraping with knime-financial data.mp4

16:25

01.11-scripting-how to use python in knime.mp4

11:08

01.12-python in knime-further examples.mp4

09:36

01.13-hyperparameter optimization in knime-data preparation.mp4

10:12

01.14-hyperparameter optimization for machine learning models using loops in knime.mp4

16:23

01.15-feature selection in knime.mp4

15:34

01.16-machine learning prediction process.mp4

09:50

01.17-knime logout.mp4

00:24

02.01-reading multiple csv files in bulk into knime.mp4

18:22

02.02-read multiple excel sheets in bulk into knime.mp4

14:27

02.03-loops how to split data into multiple output files.mp4

12:19

02.04-python in knime-further examples.mp4

08:37

9781801073288 Code.zip

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

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Data Science

Dan We

Instructor's Courses

Dan We is a 32-year-old entrepreneur, data scientist, and data analytics/visual analytics consultant. He holds a master’s degree and is certified in Power BI as a qualified associate in Tableau. He is currently working in business intelligence and helps major companies get key insights from their data in order to deliver long-term growth and outpace their competitors. He is committed to supporting other people by offering them educational services to help them accomplish their goals and become the best in their profession or explore a new career path.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.