Data science and Data preparation with KNIME

Course Curriculum

Lecture 1: Course introduction

Lecture 2: Important Please read before you get started Courseupdate Info

Chapter 1: Data science and Data preparation with KNIME New Interface

Lecture 1: Switch between old and new KNIME interface

Lecture 2: Download the course files here

Lecture 3: 4 Reading multiple CSV files in bulk into KNIME

Lecture 4: 5 Reading multiple CSV files in bulk into KNIME with Loops

Lecture 5: 6 Reading multiple Excel files efficiently into KNIME

Lecture 6: 7 A great helper node for timeseries analysis in KNIME

Lecture 7: 8 Examples of Loops in KNIME

Lecture 8: 9 Exploring more Loops in KNIME Analytics Platform

Lecture 9: 10 Loops to split data into multiple outputs

Lecture 10: 11 Advanced Loops in KNIME Recursion example

Lecture 11: 12 How to do webscraping in KNIME

Lecture 12: Url for the next lecture

Lecture 13: 13 Webscraping financial data from an API with KNIME

Lecture 14: 14 Scripting How to use Python in KNIME

Lecture 15: 15 Scripting in KNIME python as part of our workflow

Lecture 16: 16 Hyperparameter Tuning in KNIME Data preparation

Lecture 17: 17 Hyperparameteroptimization in KNIME Training and Testing

Lecture 18: 18 Feature Selection in KNIME How to get started

Chapter 2: KNIME "classic Interface" Data science and Data preparation with KNIME

Lecture 1: 1 One message at the beginning

Lecture 2: Download all resources here

Lecture 3: Reading multiple csv files in bulk into KNIME update

Lecture 4: Reading multiple Excel files in bulk into KNIME update

Lecture 5: 3 A great helper Node for time series analysis in KNIME

Lecture 6: Important for the next chapter with loops

Lecture 7: 4 Examples on how to use loops in KNIME

Lecture 8: 5 More on loops in KNIME – several ways to get the same result

Lecture 9: 6 Loops How to split data into multiple output files update

Lecture 10: Important KNIME Update (depending on which version you use for the next video)

Lecture 11: 7 Loops Recursion in KNIME

Lecture 12: 8 Webscraping with KNIME

Lecture 13: Webscraping with KNIME update – financial data

Lecture 14: Scripting – How to use Python in KNIME (R and Java as well)

Lecture 15: Python in KNIME – further examples – update

Lecture 16: 12 Hyperparameter optimization in KNIME – data preparation

Lecture 17: 13 Hyperparameter optimization for Machine Learning Models using loops in KNIME

Lecture 18: 14 Feature Selection in KNIME

Lecture 19: 15 Let's rehearse the machine learning prediction process

Chapter 3: More KNIME data prepararation and cleaning hands on

Lecture 1: Important note

Lecture 2: Attached you can find the course files to follow along.

Lecture 3: Missing Project days data

Lecture 4: Splitting long text challenge

Lecture 5: Approximate Match mapping challenge

Lecture 6: Customer Weekday Weekend challenge

Lecture 7: Bonus How to check for missing flow variables

Lecture 8: Check available Info challenge

Lecture 9: Approximate Date Lookup challenge

Lecture 10: Textprocessing string removal challenge

Lecture 11: keyword tagging challenge

Lecture 12: misspelling_challenge

Lecture 13: How to deal with date conversion issues

Lecture 14: Bonus Molecule challenge

Chapter 4: Natural Language Processing (NLP) in KNIME and additional Bonus resources

Lecture 1: Important Note

Lecture 2: Attached you can find and download the resources to follow along

Lecture 3: Copy or Moving files with KNIME update

Lecture 4: Countries – data cleaning challenge

Lecture 5: Merge Table Challenge in KNIME

Lecture 6: A Json file challenge in KNIME

Lecture 7: Mismatching addresses – Introduction to similarity search in KNIME

Lecture 8: Introduction to NLP in KNIME

Lecture 9: NLP in KNIME 2 Data preprocessing and cleaning

Lecture 10: NLP in Knime 3 bag of words and document vector

Lecture 11: NLP in KNIME Choose ML Algorithm and score our model

Chapter 5: Bonus

Lecture 1: How to use the new ChatGPT Data App in KNIME

Lecture 2: More learning