Perform data science with KNIME. Learn how to do data cleaning, AI machine learning, ETL, and data preprocessing with KNIME
Key Features
- No coding required
- Solve data cleaning challenges together and enhance your basic KNIME skills
- Learn the fundamentals for NLP tasks in KNIME using only KNIME nodes
Book Description
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 will learn how to use additional helpful KNIME nodes not covered in the other two classes. Solve data cleaning challenges together for different datasets. Use pre-trained models in TensorFlow in KNIME (involves Python coding).
Also, learn the fundamentals for NLP tasks (Natural Language Processing) in KNIME using only KNIME nodes (without any additional coding).
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/KNIME-for-Data-Science-and-Data-Cleaning
What you will learn
- How to use TensorFlow in KNIME
- How to do data science in KNIME with and without coding
- How to solve data cleaning and data preparation challenges
- How to replace Excel and start KNIME for ETL and data cleaning issues
- Examples of data science machine learning workflows with KNIME
Who this book is 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.
