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, 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 15-30 minutes 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 builds on the [L1-LS] KNIME Analytics Platform for Data Scientists (Life Science): Basics by introducing advanced data science concepts using Life Science examples.

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, get a taste for ensemble models, parameter optimization, and cross validation and see how Date/Time integrations work.

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

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 consisting of four 90-minute online sessions run by Professor Dursun Delen and one of our KNIME data scientists. Each session has an exercise for you to complete at home. The course concludes with a 45-minute wrap up session.

Course content

• Session 1: Introduction to Text Processing and Importing Text
• Session 2: Tokenization and Enrichment
• Session 3: Preprocessing, Transformation, and Classification Models
• Session 4: Visualization, Clustering, and Topic Modeling
• Session 5: Movie Forecasting Use Case, Recap and final Q&A

Integration of H2O.ai and KNIME offerings enables enterprises to create more accurate predictions and reduce time to value when implementing data science projects

ZURICH and MOUNTAIN VIEW, Calif. — Oct. 14, 2020: KNIME and H2O.ai, the two data science pioneers known for their open-source platforms, today announced a strategic partnership. The joint offering combines Driverless AI for AutoML and KNIME Server for workflow management across the entire data science life cycle - from data access to optimization and deployment. With this partnership, KNIME and H2O.ai offer a complete no-code enterprise data science solution to add value in any industry for end to end data science automation.

Preparing data for AI, selecting the right model, pushing it into production, and continuously optimizing it is a process that typically requires many stakeholders and several tools. Parts of it can be automated but flexibility is paramount in order to select the techniques that answer a company’s questions in the best way. The lack of an end-to-end tooling prevalent in most data practices, also makes it very difficult to ensure data lineage. This H2O.ai and KNIME integration now provides a solution that covers all of these challenges as well as increases data scientists’ productivity, reduces overall IT spend, and creates and uses more accurate predictions.

The expanded integration between H2O.ai and KNIME brings together all-encompassing, intuitive, automated machine learning from H2O.ai with the guided analytics from KNIME. Customers of H2O.ai and KNIME can now:

• Develop an integrated data science workflow in KNIME Analytics Platform and KNIME Server, from data discovery, data preparation to production-ready predictive models
• Deliver the power of automatic machine learning to business analysts, enabling more citizen data scientists with H2O Driverless AI
• Reduce model deployment times, leveraging H2O Driverless AI and KNIME Server for reliably managing workflow and creation process in production

“We have been using KNIME and H2O Driverless AI for years and we are very excited about this new integration and the automation and simplification that it will bring to our data science workflow,”said Alejandro Lopez, Data Science Leader of Vision Banco

“H2O Driverless AI users can now get an integrated data access and preparation platform with KNIME. This allows seamless operationalization and continuous learning demanded by our customers adapting at the speed of change today,”commented Sri Ambati, CEO and Founder of H2O.ai.

“The integration of Driverless AI offers KNIME users a strong, additional option to automate machine learning out of the box with a huge range of powerful algorithms. We believe that flexibility of choice brings most value to our users and customers, and H2O is a great addition to the mix,”stated Michael Berthold, CEO and Co-founder of KNIME.

H2O is a leading open-source AI platform, and its Driverless AI is a leading automatic machine learning (AutoML) platform. H2O Driverless AI automates time-consuming machine learning workflows with automatic feature engineering, model tuning, and model selection to achieve the highest predictive accuracy within the shortest amount of time. H2O Driverless AI empowers data scientists, statisticians, and domain scientists to work on projects faster and more efficiently by using automation to complete tasks that can take months in minutes or hours. It can now be used within a KNIME workflow.

KNIME Analytics Platform and KNIME Server provide a visual workflow platform for ETL, further machine learning choices, deployment, collaboration, and cloud execution. Users can blend and transform data from hundreds of data sources using a visual, no-code, fully auditable approach. KNIME also offers a wide range of options for how the output can be deployed - from REST, to web applications, to BI dashboards, and other third party tools. With Integrated Deployment, teams are able to automatically and continuously deploy and update models including the process of data access and pre-processing. Driverless AI adds a powerful choice for automating machine learning.

About H2O.ai

H2O.ai is the open source leader in AI and automatic machine learning with a mission to democratize AI for everyone. H2O.ai is transforming the use of AI to empower every company to be an AI company in financial services, insurance, healthcare, telco, retail, pharmaceuticals and marketing. H2O.ai is driving an open AI movement with H2O, which is used by more than 20,000 companies and hundreds of thousands of data scientists. H2O Driverless AI, an award winning and industry leading automatic machine learning platform for the enterprise, is helping data scientists across the world in every industry be more productive and deploy models in a faster, easier and cheaper way. 

Learn more at www.H2O.ai

About KNIME

KNIME provides software for fast and intuitive access to advanced data science. At the core is the open source KNIME Analytics Platform, a visual workbench providing a wide range of state-of-the-art analytics tools and techniques to handle any use case — from basics to highly advanced. It is complemented by the commercial KNIME Server which makes data science productive in the enterprise, while staying in the same software environment for deployment, collaboration, management and optimization. Headquartered in Zurich, KNIME has offices in Austin TX, Konstanz and Berlin. 

Learn more at www.knime.com

This course, hosted by our partner Target Reply Rome, will be run in Italian. It 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 course consists of four, one-hour online sessions. 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 at the end of the last session.

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

This course will be followed by the [L2-DS] KNIME Analytics Platform for Data Scientists: Advanced - Online held in Italian by our Partner as well.

Course Content

• Session 1: Introduction to KNIME Analytics Platform
• Session 2: Data Import and Blending
• Session 3: Data Manipulation, Aggregation, and Visualization
• Session 4: Introduction to Data Mining, and Wrap Up

This course, hosted by our partner Target Reply Rome, 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 at the end of the last 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, and Wrap Up

This webinar is hosted by Paras Gupta (Day5 Analytics) and Scott Fincher (KNIME).

Finance professionals address complex business demands by using large volumes of transactional data to gain insight. Today, many professionals spend way too long on data management and managing poor performing spreadsheets - and not enough time on business partnering.

Is it time to stop managing spreadsheets, and start delivering value?

KNIME is a tool which has enabled Finance professionals across the globe to automate their own manual, error-prone, tedious and repetitive data processes. We will demonstrate how Finance professionals can use KNIME to escape spreadsheet hell - without bugging IT for help!

   - Learn how to automate financial reporting in KNIME's visual, no-code workspace

   - Explore how Finance departments across the globe are empowered to drive digital transformation and innovation with KNIME

KNIME in Finance

   - Free to download and use - unlimited users, rows and functionality

   - No code, visual workflows

   - Input data from, and output data to, virtually any data platform

   - Option to upgrade for Enterprise collaboration

Join Greg Landrum for this advanced chemistry webinar and find out how a blend of tools - RDKit, KNIME, and Python - are used together for R-group analysis.

Greg is a big fan of both KNIME and Python, “I regularly use them together with the RDKit to work with and analyze chemical data.”

In this webinar, he will share and walk through a workflow that uses a combination of KNIME nodes and Python scripting to do some advanced R-group analysis and visualization. In addition to the workflow itself, he will talk about his rules of thumb for figuring out which tool or combination tools to use for particular tasks.

There will also be plenty of time for Q&A at the end of the webinar.

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.

Agenda & groups/breakout rooms

We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle.

After this presentation we split into three groups. Each group focuses on one of the three aspects of the data science cycle.

Three zoom breakout rooms will be activated for this purpose. You go into the room for the group you sign up for (below) to attend the specific tutorial and exercises.

There will be a KNIME data scientist in each breakout room to help you while you work on the exercises.

Choose which group (Group 1, 2, or 3) you want to join on the zoom registration page.

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

Resources

Download the learnathon materials here.

“I like KNIME, you like Juypter, and that does not stop us from working together! I can call your code scripts and you can call my workflow.”

At this webinar, Mahantash Pattadkal (Monty) and Paolo Tamagnini will show you how to display and run a KNIME workflow in Jupyter Notebook and how to display and run Jupyter Notebook in your KNIIME workflow.

They will walk you through the installation steps and also show live demos. Bring your questions for the Q&A session at the end!

This webinar will be hosted by our partner S-IN Soluzioni Informatiche.

Find out how to automate the extraction of hazard information from thousands of Safety Data Sheets (SDS). We’ll show you how you can remove the need for manual work by automatically gathering and harmonizing text-based information. Using text mining and string manipulation in KNIME Analytics Platform, the recovery of risk phrases, which is requested by Regulators, is ensured.  

What are SDS?

Safety Data Sheets (SDS) are standardized documents chemical manufacturers use to communicate chemical hazard information to chemical handlers. They typically contain chemical properties, health and environmental hazards, protective measures, as well as safety precautions for storing, handling, and transporting chemicals.
Chemical handlers extract information from SDS by reading the section of interest. Such a manual process is not effective when the Health, Safety & Environment (HSE) manager needs to gather hazard information about all chemicals used in the company to develop an adequate risk management plan.

Regulators are pushing companies to harmonize and gather this information and make it available to anyone who works with chemical substances, such as synthetic or analytical chemists.

Data visualization is one of the most important parts of data analysis and an integral piece of the whole data science process. It not only enables the communication of results, it also serves to explore and understand data better. For this reason, data visualization is a necessary part of the toolkit for anyone working in data science.

This course focuses on data visualisation goals, primary assumptions, and common techniques. (Please note that this is an introductory data visualization course.) We will explain a variety of approaches to compare data, find relationships, investigate development, and visualize multidimensional data. We will conclude with the creation of interactive dashboards and how to make them accessible via a web browser.

This external instructor-led course consists of four, 75 minutes online sessions run by Barbora Stetinova and one of our KNIME data scientists. Along with the teaching time, each session has an exercise for you to complete at home. The course concludes with a 15-minute wrap up session.

Course Content:

• Session 1: Introduction, visualizing individual data points and their evolution
• Session 2: Distributions, outliers, and finding relations in data
• Session 3: Dimensionality reduction, visualizing multiple dimensions, and a simple dashboard
• Session 4: Creating and deploying interactive dashboards

Finance professionals address complex business demands utilizing and processing large volumes of transactional data to gain insight. Today, many professionals spend way too long on data management and limiting, poor performing spreadsheets - and not enough time on business partnering.

Isn't it time to stop managing spreadsheets, and start delivering value?

KNIME enables Finance professionals across the globe to automate manual, error-prone, tedious and repetitive data processes across multiple data sources to improve efficiency and accuracy without IT support or coding.
 

At this webinar you will learn how:

• Finance professionals use KNIME to break away from spreadsheet limitations

• Automate financial reporting in KNIME's visual, no-code workspace

• Finance team collaborate virtually and securely in the office or around the world

This webinar is hosted by Paras Gupta (Day5 Analytics) and Scott Fincher (KNIME).

Most higher education data analytics courses nowadays cover at least a bit of data science: data acquisition, data transformation, data cleaning, and standardization, data blending, data visualization; some machine learning, some statistics, or all of the above.

KNIME Analytics Platform is an excellent tool for such courses: It is free (and open-source) and its visual workflow approach is very easy to grasp and intuitive to apply. With its extensive collection of operations and machine learning algorithms, however, you will rarely lack functionality which is further made unlikely by integrations with popular data science tools like Python and R.

With the spread of KNIME Software in the industry, more and more companies are looking for graduates with KNIME experience for positions in data engineering, data analysis, business analytics, and data science. Therefore, proven expertise in KNIME Software (through certification) helps graduates on the job market.

In this webinar, we want to show you how we can support you in the adoption of KNIME Analytics Platform in the classroom.

NOTE:

This webinar is being held on December 2, 2020 at 10 AM - 11 AM UTC +1 (Berlin) which is 6 PM - 7 PM UTC +9 (Tokyo).

If another time works better for you, then join our team on December 2, 2020 at 2 PM - 3 PM UTC -6 (Chicago).

Most higher education data analytics courses nowadays cover at least a bit of data science: data acquisition, data transformation, data cleaning, and standardization, data blending, data visualization; some machine learning, some statistics, or all of the above.

KNIME Analytics Platform is an excellent tool for such courses: It is free (and open-source) and its visual workflow approach is very easy to grasp and intuitive to apply. With its extensive collection of operations and machine learning algorithms, however, you will rarely lack functionality which is further made unlikely by integrations with popular data science tools like Python and R.

With the spread of KNIME Software in the industry, more and more companies are looking for graduates with KNIME experience for positions in data engineering, data analysis, business analytics, and data science. Therefore, proven expertise in KNIME Software (through certification) helps graduates on the job market.

In this webinar, we want to show you how we can support you in the adoption of KNIME Analytics Platform in the classroom.

NOTE:

This webinar is being held on December 2, 2020 at 2 PM - 3 PM UTC -6 (Chicago).

If another time works better for you, then join our team on December 2, 2020 at 10 AM - 11 AM UTC +1 (Berlin) which is 6 PM - 7 PM UTC +9 (Tokyo).

This webinar will be hosted by our partner ITZONE.
The first part of the webinar will be held by Vincenzo Tursi (KNIME) in English, the second part will be covered by our partner ITZONE and held in Mongolian.

Developing Machine Learning models is always considered very challenging. Not only is a deep understanding of ML algorithms and the consequences for the corresponding system required, the developer also needs expertise in command-driven development. The introduction of KNIME Software has brought the development of Machine Learning models into the purview of the common man.

During the webinar we will introduce you to KNIME Analytics Platform, a modern, visual data analysis environment that allows for code-free creation of advanced analytics, empowering more casual users. It offers an extremely wide range of data sources, tools, and methods - many based on leading open source projects - all within one platform.
Based on the example of a churn prediction, we will investigate the prediction techniques, the underlying logic and prerequisites, as well as performances. We’ll have time to answer your questions, too!

This webinar is hosted by our trusted partner, Target Research will be held in Italian.

They'll discuss how data science is not only machine learning, but also using data in an efficient way, without wasting time and resources. To improve operational efficiency and provide useful business insights from core company data, it’s essential to use tools that facilitate the acquisition, transformation and presentation of data - such as KNIME Analytics Platform.

Target Research will show three industry use cases of efficient data usage: automated data processing, guided pricing tool, and reporting visualization.

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, 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 15-30 minutes 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