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.
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
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.
About The Author
Dan We: 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 has a passion for learning and teaching. He is committed to supporting other people by offering them educational services and helping them accomplish their goals, gain expertise in their profession, or explore new careers.
Table of contents
Chapter 1 : Introduction
- Welcome to KNIME
- Copying or Moving Files with KNIME
- Reading multiple Excel files - Potential Errors and Solutions
- Reading Multiple Excel Files - Benefits of Loops
- Excel Files with Different Table Structures in KNIME
- Useful Nodes - Column Aggregations
- Countries - Data Cleaning Challenge
- Merge Table Challenge in KNIME
- A JSON File Challenge in KNIME
- Create the Neural Network h5 Model File to be Used in KNIME
- Mismatching Addresses - Introduction to Similarity Search in KNIME
- TensorFlow Neural Network Regression Implementation in KNIME
- Transfer Learning in KNIME Using Python Scripts
- Introduction to NLP in KNIME Part 1
- NLP in KNIME Part 2 - Data Preprocessing and Cleaning
- NLP in KNIME Part 3 - Bag of Words and Document Vector
- NLP in KNIME - Choose ML Algorithm and Score Our Model
- Chapter 2 : Older Videos KNIME Version Before 4.3
- Title: KNIME for Data Science and Data Cleaning
- Release date: August 2021
- Publisher(s): Packt Publishing
- ISBN: 9781801071413
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