Data science and Data preparation with KNIME: Perform data science and data preparation with KNIME, with examples of data science machine learning workflows

Dan We

S$53.99

Video Sep 2021 4hrs 1st Edition

Dan We

S$53.99

Video Sep 2021 4hrs 1st Edition

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Key benefits

No coding required
Solve a data cleaning example together and enhance your basic KNIME skills
Increase your productivity and save time in your data preparation tasks

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

Who is this book for?

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. Basic knowledge of machine learning is certainly helpful for the later lectures in this course. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.