Data science and Data preparation with KNIME

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be 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.

29 on-demand videos & exercises

Level: Beginner

English

4hrs

Access on mobile, web and TV

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

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.

Course Curriculum

About the Author

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.. Daniel Weikert is a 33-year-old entrepreneur, data enthusiast, consultant, and trainer. He is a master's degree holder certified in Power BI, Tableau, Alteryx (Core and Advanced), and KNIME (L1–L3). He is currently working in the business intelligence field and helps companies and individuals obtain vital insights from their data to deliver long-term strategic growth and outpace their competitors. He possesses a fervent dedication to both learning and teaching. His unwavering commitment extends to providing educational services and assisting individuals in achieving their objectives, mastering their fields, and embarking on new career journeys.