This webinar will be hosted by our partner MYDRAL.

The versatility of Excel has made it one of the world’s most popular tool for storing and analyzing datasets. However, as data preparation requirements get more complex, the use of Excel can quickly become challenging and time consuming. Join this webinar to find out how moving to the workflow approach in KNIME Analytics Platform will save you time and help you become more productive. Leveraging and capitalizing on your Excel experience, you'll learn about everything you need to effectively add KNIME to your skill set and unlock your analytical potential.

Bring your text analytics questions and join our expert KNIME panel discussion on Text Mining with KNIME Analytics Platform! Our panelists have been teaching courses and presenting webinars on this topic in recent months, and now it is your turn to chime in. If you have a question you’ve always wanted to ask, your brain is bursting with new ideas about NLP, or you just want to provide some feedback on the KNIME Text Processing extension - take advantage of this time to do so! Here’s a few examples of the types of questions we’ll cover:

• What are the most common text mining use cases?
• Do you have examples for grabbing data from pdf and docx files?
• What are some of the common preprocessing nodes used in KNIME Text Processing?
• How do I interpret topics I obtain from a topic extraction algorithm?
• Stemming vs Lemmatization: which should I use?

During this 90 minute panel, we’ll be accepting questions and topic suggestions from the audience. You can also send us your questions in advance - there’s an opportunity to do that when you register through zoom. Even if you don’t have questions of your own, please feel free to come listen to the discussion!

Meet the panel:

Scott Fincher is a Data Scientist with KNIME based in Austin, TX. He routinely teaches, presents, and leads group workshops covering topics such as the KNIME Analytics Platform, Machine Learning, and the broad Data Science umbrella. He enjoys assisting other data scientists with general best practices and model optimization. For Scott, this is not just an academic exercise. Prior to his work at KNIME, he worked for almost 20 years as an environmental consultant, with a focus on numerical modeling of atmospheric pollutants. Scott holds an MS in Statistics and a BS in Meteorology, both from Texas A&M University.

Dr. Dursun Delen is the holder of William S. Spears Endowed Chair in Business Administration, Patterson Family Endowed Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. Dr. Delen has authored more than 100 peer reviewed journal articles and 10 books/textbooks in the broad area of Business Analytics/Data Science. He has been the recipient of several research and teaching awards including the prestigious Fulbright scholar, regents’ distinguished teacher and researcher, president’s outstanding research and teaching, and Big Data mentor awards.

Panel Host:

Dr. Rosaria Silipo is not only an expert in data mining, machine learning, reporting, and data warehousing, she has become a recognized expert on the KNIME data mining engine, about which she has published three books: KNIME Beginner’s Luck, The KNIME Cookbook, and The KNIME Booklet for SAS Users. Previously Dr. Silipo worked as a freelance data analyst for many companies throughout Europe. She has also led the SAS development group at Viseca (Zürich), implemented the speech-to-text and text-to-speech interfaces in C# at Spoken Translation (Berkeley, California), and developed a number of speech recognition engines in different languages at Nuance Communications (Menlo Park, California). Dr. Silipo gained her doctorate in biomedical engineering in 1996 from the University of Florence, Italy.

We want to help you grow professionally to face future challenges. To make this easier, we’ve reworked our range of self-paced courses and would like to introduce our new learning path. Have a look, we’re sure you’ll find the right course to progress along your professional path!

New Learning Path

Let’s first look at the different steps along the learning path. Our concept is to first teach the tool and then the science.

 Levels 1 and 2 - Data Science (DS) or Data Wrangling (DW)

In the first two levels, you learn how to use the tool (KNIME Analytics Platform) for either data science (DS) or data wrangling (DW) tasks in two steps: basic (level L1) and advanced (level L2).

 Level 3 - Productionizing and Collaboration

At level L3, you learn about the enterprise related features of KNIME Server: options for easy and safe deployment, integration with company policies around security and governance, model monitoring, and more.

Level 4 - Specialization

At the final level L4, you can choose from a range of specialized topics. We offer courses on big data, time series analysis, text processing, machine learning, and chemistry data - with more to come.

Certification

Upon completion of any level, you can take the KNIME Certification, during which we officially test your expertise and certify your knowledge.

The courses are offered instructor-led (online and onsite) and self-paced (online only), meeting the needs of different types of learners: those who prefer an instructor, scheduled lessons, and solution walk-throughs, as well as those who prefer self-paced learning with sets of videos and associated exercises for self-completion and self-checking.

Figure 1. KNIME learning path including basic and advanced data wrangling and data science courses (L1 & L2), productionizing and collaboration course (L3), and the courses around specialized topics (L4). All courses are provided online and onsite; the courses at levels L1, L2, and L3 are also provided as self-paced courses.

We've been offering many online courses over the last few months, and we’d like to see you at the onsite courses all over the world as soon as it’s possible to run them again!

How to find the right course

The door to access all types of courses - online, self-paced, and onsite - is our KNIME Courses Overview page. Here you can find the descriptions for each course type and links to either the schedules (online and onsite) or links to the self-paced courses to get started right away.

Figure 2. Online, self-paced, and onsite courses are accessible via the KNIME Courses Overview page. In the section “Self-paced courses”, click the “Start a course” button to see the range of self-paced courses.

For example, to access one of our new self-paced courses, go to the “Self-paced courses” section and click the “Start a course” button. You’ll land on the KNIME Self-Paced Courses page. Select the level you need (L1 or L2) and the professional profile (Data Science DS or Data Wrangling DW) and then click on the corresponding course, to get started right away.

What’s new about the self-paced courses?

All self-paced courses have been reshaped to follow the same structure as their instructor-led counterparts - the online courses - with the same contents. New video content has been introduced to cover:

• New database nodes: The videos about the database nodes show you how to connect to a database either with the dedicated connector node, or with the generic DB Connector node. These videos also show you how to perform basic and advanced data manipulation directly on the database, with the DB data manipulation nodes with a graphical user interface, as well as with the DB nodes with an editor for your own SQL script.
• Components: A component encapsulates a workflow snippet into a functionality, a KNIME node, that can be reused and shared. The new videos guide you from basic introduction of the concept, to sharing, linking, and configuring components.
• Loops and switches are described through several new videos, introducing the different types of loops and switches and how to automate the result selection.
• More machine learning (only in L2-DS): Advanced machine learning algorithms, such as the random forest, are introduced in the Advanced Machine Learning lesson of the L2-DS course. These videos introduce the theory behind the algorithm and how to train the model in KNIME Analytics Platform.
• Updates from KNIME Analytics Platform 4.0, 4.1, and 4.2: These are mainly visible in the new introductory videos. The visual appearance of the KNIME workbench has changed over time, the KNIME Hub search panel was introduced, the description panel now also shows the description of components and workflows, the welcome page has a new design, and more. Since we really like this new KNIME workbench, we recorded the videos again in the Overview of KNIME Analytics Platform lesson of the courses on the L1 level.

More to come

Maybe you’ve already seen or even attended some of the courses on the L4 level that are currently only available as instructor-led, online courses:

• L4-ML Introduction to Machine Learning Algorithms
• L4-TP Introduction to Text Processing

Inspired by the huge response to the online courses, we’re working on providing self-paced versions of these courses too. Stay tuned! We’ll keep you up to date about the upcoming course schedule and new contents of the self-paced courses. A number of instructor-led online courses, webinars, and events are already announced for September. In the meantime - try out one of the self-paced courses.

This free webinar, run by our partner Redfield, will highlight how text mining can significantly benefit your organization and how you can use KNIME Software for your text mining tasks.

To start with, Jan Lindquist from Redfield will take you through the range of insights and knowledge that can be mined from text, using business use cases to highlight this. For example in marketing (online customer interactions), politics (political speeches party alignment), technology (covid-19 app acceptance), research (publication biases), and electronic records (e.g., email, messaging, document repositories), spam filtering, fraud detection, alternative facts detection, and Q&A.

The challenge for organizations is to extract the valuable knowledge/information hidden in the mass of unstructured data (text) that comprises the vast majority of all data. Overcoming this challenge is critical in order for organizations to stay competitive. In the second part Redfield experts, Nadjet Bouayard-Agha and Artem Ryasik, will dive into various text mining techniques such as NLP, topic modeling, sentiment analysis, and network analysis using KNIME. And Nadjet will show you how to couple network analysis to text mining to produce results.

Redfield will provide examples from the presentation, which will be available for you to explore further after the webinar.

This course, hosted by our partner Target Reply, will be run in Italian. It builds on the [L1-DS] KNIME Analytics Platform for Data Scientist: Basics by introducing advanced data science concepts.

Learn all about flow variables, different workflow controls such as loops, switches, and error handling. Find out how to automatically find the best parameter settings for your machine learning model, see how Date&Time integrations work, and get a taste for ensemble models, parameter optimization, and cross validation.

This course consists of four, one-hour online sessions run by one of our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 30-minute wrap up session.

The course is capped at 40 participants - so register early!

Course Content:

• Session 1: Flow Variables
• Session 2: Workflow Control - Loops, Switch and Try Catch
• Session 3: Date&Time and Integrations
• Session 4: Advanced Machine Learning - Ensemble Models, Parameter Optimization, and Cross Validation

This webinar, run by our partner Sistek, will be held in Turkish.

Fraud detection in the insurance industry is usually built on two structures. One of them is the ML based algorithms that detect the fraud attempts, the other one is the rule sets that are created based on the experiences of the Claim Department. Although these methods handle detecting the problem and can generate a solution for individual frauds, organized fraud attempts are still a big and complicated problem for the insurance industry.

Detecting and taking precautions for all fraud attempts is difficult. Traditional ML algorithms or database queries are often not sufficient. These traditional methods need to be enhanced; the application of network-based solutions creates a great opportunity to detect relations between any participants of a claim file and blacklisted fraud individuals and organizers.
 
In this webinar, we will summarize investigating a claim file participant for organized fraud connections using KNIME network nodes with a shortest path algorithm combined with a rule set and ML fraud detection algorithms.

Speakers:

Mehmet Abaci – CIO - Anadolu Sigorta
After earning his degree in engineering from METU in 1991, he started to work as a software developer and took various executive roles at Türkiye İş Bankası, Softech Software. With 30 years of Information Technology experience and knowledge of the banking and insurance industries, he has taken on important roles in a wide range of projects including Software and API Development Platforms and Methodologies, Mobility, Big Data, Business Intelligence, Compliance, Cloud Computing, Robotic Process Automation (RPA) and Machine Learning and Artificial Intelligence (AI). He has been as Assistant General Manager of Anadolu Sigorta since 2012.

Vedat Güneş– BI and Analytics Manager - Anadolu Sigorta
After earning his degree in engineering from Marmara University in 2005, he began his career  as a software developer. He has worked in different roles in various industries such as the beverage, telecom and insurance industries. With 15 years of data and analytics experience, he has held his position as Business Intelligence and Analytics manager of Anadolu Sigorta since 2019.

Onur Özcan - Application Architect - Anadolu Sigorta ICT
After graduating in engineering from Koç University in 2010, Onur worked as a business intelligence engineer. He has been employed in various industries like health, pharmaceutical, finance and insurance. With 10 years of experience in BI, DW, ETL, ML and AI projects; he has been served as Chief Data Analytics architect since 2018 at Anadolu Sigorta.

This free online learnathon is a mix between a hackathon and a workshop. It's like a workshop because we'll learn more about the data science cycle: data access, data blending, data preparation, model training, optimization, testing, and deployment. It's like a hackathon because we'll work in groups to hack a workflow-based solution to guided exercises.

The tool of choice is the open-source, GUI-driven KNIME Analytics Platform. Because KNIME is open, it offers great integrations with an IDE environment for R, Python; SQL, and Spark.

This session is presented by Adeyemi Odeneye (Meetup group: Lagos KNIME Users), from several years a strong member of our KNIME Community in Nigeria.

We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle. Then we'll solve three sets of exercises, where each set will focus on one of three aspects of the data science cycle:

• Set 1 - Working on the raw data. Data access and data preparation.
• Set 2 - Machine Learning. Which model shall I use? Which parameters?
• Set 3 - I have a great model. Now what? The model deployment phase.

Due to the current situation with COVID-19, we can't, unfortunately, meet in person to work together in three groups. Instead, everyone can download the workshop material here and follow the presenter solving a subset of the exercises. Our data scientists will be available and happy to support you (remotely) while you work on the exercises.

Please note: This course will be held in German.

This course is followed by the [L2-DW] KNIME Analytics Platform for Data Wranglers: Advanced by introducing advanced concepts for building and automating workflows.

This course is designed for those who are just getting started on their data wrangler journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

The course focuses on accessing, merging, transforming, fixing, standardizing, and inspecting data from different sources. It dives into data cleaning and aggregation, using methods such as advanced filtering, concatenating, joining, pivoting, and grouping. With all of this, you’ll learn how to get your data into the right shape to generate insights quickly.

We’ll take you through everything you need to get started with KNIME Analytics Platform, so you can start creating well-documented, standardized, reusable workflows for your (often) repeated tasks.

This is an instructor-led course consisting of four, 75 minutes online sessions run by one of our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 15-minute wrap up session.

Course Content

• Session 1: Introduction to KNIME Analytics Platform & Importing Data  
• Session 2: Data Merging and Data Cleaning I
• Session 3: Data Cleaning II and Data Transformation
• Session 4: Date & Time and Data Aggregation and Q&A

Please note: This course will be held in German.

This course builds on the [L1-DW] KNIME Analytics Platform Course for Data Wranglers: Basics by introducing advanced concepts for building and automating workflows.

Learn all about flow variables, different workflow controls such as loops, switches, and how to catch errors. And lastly learn how to visualize your data, export your results, format your Excel tables, and look beyond data wrangling towards data science, training your first classification model.

This is an instructor-led course consisting of four, one hour online sessions run by one of our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 15-minute wrap up session.

Course Content

• Session 1: Data Visualization & Data Export     
• Session 2: Flow Variables
• Session 3: Workflow Control
• Session 4: Styling Excel Tables & Introduction to Machine Learning

This course is designed for those who are just getting started on their data science journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

The course then introduces you to KNIME Analytics Platform covering the whole data science cycle from data import, manipulation, aggregation, visualization, model training, and deployment.

This is an instructor-led course consisting of four, 1 hour online sessions run by one of our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 15-minute wrap up session.

Course Content

• Session 1: Introduction to KNIME Analytics Platform and Data Import
• Session 2: Data Manipulation & Data Aggregation
• Session 3: Data Visualization & Data Mining I
• Session 4: Data Mining II & Deployment

This course is about text mining, its theory, concepts, and applications. Specifically, the course focuses on the acquisition, processing and mining of textual data with KNIME Analytics Platform. You will learn how to use the Text Processing Extension to read textual data into KNIME, enrich it semantically, preprocess it, transform it into numerical data, and extract information and knowledge from it through descriptive analytics (data visualization, clustering) and predictive analytics (regression, classification) methods. Course also covers popular text mining applications including social media analytics, topic detection and sentiment analysis.

Put what you’ve learnt into practice with the hands-on exercises.

This is an external instructor-led course run by Professor Dursun Delen and one of our KNIME data scientists.

Course content

Day 1

• Introduction to Text Processing and Importing
• TextTokenization and Enrichment

Day 2

• Preprocessing, Transformation, and Classification Models
• Visualization, Clustering, and Topic Modeling
• Movie Forecasting Use Case, Recap and final Q&A

During this online course you’ll learn to build interactive cheminformatics workflows using KNIME Analytics Platform and its Cheminformatics Extensions. The hands-on training will contain several units where we'll cover a diverse set of topics such as data manipulation and interactive filtering, fingerprints and R-group decomposition, similarity searches and clustering, and data visualization and exploration. After completing this course you'll have a set of fully functional workflows and will have learned how to build your own.

This is an instructor-led course consisting of four 75-minute online sessions, each session has an exercise for you to complete at home, we will go through the solution at the start of the following session. The course concludes with a 15-minute wrap up session.

Course Content

• Session 1: Exploring Chemical Data
• Session 2: R Group Decomposition
• Session 3: Clustering
• Session 4: Similarity Search

** The first 50 registrations who register from a European state until October 14 will get a KNIME swag package! Please provide your address in order to get one. **

This free online learnathon, co-hosted by our partner ABAX, will be held in German.

A learnathon is a mix between a hackathon and a workshop. It's like a workshop because we'll learn more about the data science cycle: data access, data blending, data preparation, model training, optimization, testing, and deployment. It's like a hackathon because we'll work in groups to hack a workflow-based solution to guided exercises.

The tool of choice is the open-source, GUI-driven KNIME Analytics Platform. Because KNIME is open, it offers great integrations with an IDE environment for R, Python; SQL, and Spark.

We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle. Then we'll solve three sets of exercises, where each set will focus on one of three aspects of the data science cycle:

• Set 1 - Working on the raw data. Data access and data preparation.
• Set 2 - Machine Learning. Which model shall I use? Which parameters?
• Set 3 - I have a great model. Now what? The model deployment phase.

Due to the current situation with COVID-19, we can't, unfortunately, meet in person to work together in three groups. Instead, everyone can download the workshop material here and follow the presenter solving a subset of the exercises. Our data scientists will be available and happy to support you (remotely) while you work on the exercises.

Save the date for KNIME Fall Summit Online: Monday 16 - Friday 20 November, 2020!

We will keep this page updated with information on the online summit program. You can also sign up below to receive updates via email.

KNIME Summits are all about Data Science in Action. Learn more about how to use KNIME Software by taking a KNIME course, see how others are using KNIME to solve data science challenges, hear about the latest features and get a sneak peek at what's currently in development. Network with the KNIME community, take part in meetings of interest, and enjoy the online social program!

If you don’t know KNIME or if you have colleagues who should know about KNIME, then this webinar is the place to start! Join us as we highlight:

• A modern, open source data science platform with a visual workflow editor that lets you focus on learning methods rather than learning the tool itself.
• An extremely wide range of data sources, tools, and methods - many based on leading open source projects - all within one platform. 
• Software that is open source and free. No limitations on methods, data, or operating systems.
• A strong KNIME community to support you, including thousands of freely available working examples.

NOTE:

This webinar is being held on Tuesday, October 27 at 1:30 PM - 2:30 PM UTC +1 (Berlin).

If the US time zone is better for you, then join our US team on Tuesday, October 27 at 1:30 PM - 2:30 PM UTC -5 (Chicago).

If you don’t know KNIME or if you have colleagues who should know about KNIME, then this webinar is the place to start! Join us as we highlight:

• A modern, open source data science platform with a visual workflow editor that lets you focus on learning methods rather than learning the tool itself.
• An extremely wide range of data sources, tools, and methods - many based on leading open source projects - all within one platform. 
• Software that is open source and free. No limitations on methods, data, or operating systems.
• A strong KNIME community to support you, including thousands of freely available working examples.

NOTE:

This webinar is being held on Tuesday, October 27 at 1:30 PM - 2:30 PM UTC -5 (Chicago).

If the European time zone is better for you, then join our European team on Tuesday, October 27 at 1:30 PM - 2:30 PM UTC +1 (Berlin).

Join the developers behind the KNIME Hub on a tour that takes you behind the scenes!

At this free webinar you’ll get to know the people who developed the KNIME Hub. You’ll learn about the range of KNIME Hub functionality, get explanations, and also links to resources. In the second part of the webinar, the Hub team will look at how KNIME Hub is now integrated into KNIME Analytics Platform, discuss more recently added features, and look ahead to what’s on the horizon in terms of future development.

Anything else? We want to know what kinds of functionality you’d like to see in the future. Tell us what’s on your KNIME Hub wish list!

This webinar is for you if:

• You’re already using KNIME Hub, want to learn more, and have a wish list of items you think would be great to add to the KNIME Hub
• You’re interested to find out if this might be the platform you’ve been looking for to collaborate with others

Agenda:

• Meet the KNIME Hub Team (who they are and how they work)
• Back to basics - KNIME Hub fundamentals
• Out now: latest features of KNIME Hub
• What’s on your wishlist?

Join Franziska Rau and Adrian Nembach in this webinar to find out what the deep learning network BERT is and how it can be used for different text processing related tasks.

We will kick off the webinar with a short introduction to BERT and how it achieves state-of-the-art performance for a variety of text processing tasks.

Then we will dive into text classification for our first Movie Reviews use case. We’ll show you how to perform sentiment analysis on a dataset of movie reviews using BERT and TensorFlow 2 in KNIME Analytics Platform.

Our second use case involves a semantic search application on the CORD-19 dataset, which contains a plethora of papers on COVID-19. Here, we will use BERT to get a semantic representation of the content of the papers that allows us to find the most relevant for a user-supplied question.