KNIME – A Guide for Absolute Beginners [Video]
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-
Free ChapterLearn KNIME in a Fun and Easy Way - Through Solving a Case Study
- Welcome and Introduction
- Let Us Get Started with KNIME - the Interface
- Let Us Take a Look at Our Case Study Data
- Reading Data into KNIME - the Basics.
- Using Loops for Data Import - How to Be More Efficient in KNIME
- Data Cleaning and Saving Files with KNIME
- Webscraping in KNIME - Extracting Currency Exchange Rates from an API
- Joining and Cleaning Our Data in KNIME
- Final Data Cleaning and Preparation Steps for Visualizing Our Results
- Bonus - Saving the Data as a Hyper Format for Tableau
- Visualization - an Introduction on How to Visualize in Tableau or Power BI
- Predictive Analytics in KNIME - Machine Learning AI to Predict Customer Churn
- KNIME - Log Out and Final Words
About this
video
Data cleaning is always a big hassle, especially if we are short on 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 start from the basics and learn KNIME in a (hopefully) fun and easy way by going through a case study together and prepare the data based on the requirements in the case study.
After completing our data prep, we will learn how to visualize data using tools such as Power BI and Tableau.
Finally, we also briefly cover the predictive analytics capabilities of KNIME and see how easy machine learning in KNIME can be.
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 supportive files for this course are available at https://github.com/PacktPublishing/KNIME-A-Guide-for-Absolute-Beginners
- Publication date:
- July 2021
- Publisher
- Packt
- Duration
- 2 hours 31 minutes
- ISBN
- 9781801078108