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.
What You Will Learn
- Enhance your basic KNIME skills already acquired
- Increase your productivity and save time in your data preparation tasks
- Discover what kind of loops are available and how to use them
- Learn how to use Python in KNIME
- Learn how to do data science in KNIME with and without coding
- Learn basic machine learning workflows and helpful nodes
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 : Data Science and Data Preparation with KNIME
- Course Introduction
- Reading Multiple CSV Files in Bulk into KNIME Update
- Reading Multiple Excel Files in Bulk into KNIME Update
- A Great Helper Node for Time Series Analysis in KNIME
- Examples of How to Use Loops in KNIME
- More on Loops in KNIME - Several Ways to Get the Same Result
- Loops - How to Split Data into Multiple Output Files
- Loops Recursion in KNIME
- Webscraping with KNIME
- Webscraping with KNIME - Financial Data
- Scripting - How to Use Python in KNIME
- Python in KNIME - Further Examples
- Hyperparameter Optimization in KNIME - Data Preparation
- Hyperparameter Optimization for Machine Learning Models Using Loops in KNIME
- Feature Selection in KNIME
- Machine Learning Prediction Process
- KNIME Logout
- Chapter 2 : Older Videos KNIME Version Before 4.3
- Title: Data science and Data preparation with KNIME
- Release date: September 2021
- Publisher(s): Packt Publishing
- ISBN: 9781801073288
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