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. Find out how to integrate external tools like Python, R, Google Sheets, and others. And lastly, learn how to export your data and create a report for sending to your colleagues.

This is a full day instructor-led course. During the course there’ll be hands-on sessions based on real-world use cases.

Course Content

• Introduction to KNIME Analytics Platform
• Data Access
• Data Manipulation & Data Aggregation
• Data Visualization
• Data Mining
• Deployment

This course builds on the [L1-DS] KNIME Analytics Platform for Data Scientists: 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, cross validation, and in-database processing.

This is a full day instructor-led course. During the course there’ll be hands-on sessions based on real-world use cases.

Course Content:

• Flow Variables
• Workflow Control - Loops, Switch and Try Catch
• Date & Time
• Advanced Machine Learning - Ensemble Models, Parameter Optimization, and Cross Validation
• Database Extension

This course builds on the [L1-DS] KNIME Analytics Platform for Data Scientists: 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, cross validation, and in-database processing.

This is a full day instructor-led course. During the course there’ll be hands-on sessions based on real-world use cases.

Course Content:

• Flow Variables
• Workflow Control - Loops, Switch and Try Catch
• Date & Time
• Advanced Machine Learning - Ensemble Models, Parameter Optimization, and Cross Validation
• Database Extension

This course dives into the details of KNIME Server and KNIME WebPortal. Learn how to use KNIME Server to collaborate with colleagues, automate tasks, and deploy KNIME workflows as analytical applications and services. Specifically, learn how to share workflows, data, and components with colleagues and among different functions within the company. Learn how to set access rights on your workflows, data, and components, execute workflows remotely on KNIME Server and from the KNIME WebPortal, and schedule report and workflow executions.

This is a one day instructor-led course run by one of our KNIME data scientists. During the course there’ll be hands-on sessions to practice working with KNIME Server.

This course focuses on how to use KNIME Analytics Platform for in-database processing and writing/loading data into a database. Get an introduction to the Apache Hadoop ecosystem and learn how to write/load data into your big data cluster running on premise or in the cloud on Amazon EMR, Azure HDInsight, Databricks Runtime or Google Dataproc. Learn about the KNIME Spark Executor, preprocessing with Spark, machine learning with Spark, and how to export data back into KNIME/your big data cluster.

This course lets you put everything you’ve learnt into practice in a hands-on session based on the use case: Eliminating missing values by predicting their values based on other attributes.

This is a two day instructor-led course. During the course there’ll be hands-on sessions based on real-world use cases.

Course Content

• Introduction to KNIME and the Database Extension
• Data Processing in a Traditional Database
• Working with Hadoop and Spark
• Machine Learning with Spark

This webinar, run by our partner Sistek.

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 webinar will be hosted by our customer Avon.

Our focus in this webinar is the topic of “Guided Analytics” and the opportunities it provides to maximize human resources through automation. By identifying which areas of an analyst’s work can be automated, valuable time can be freed up enabling the business analyst to concentrate on giving their expert input where it is needed most, for example interpreting the data and advancing future developments.

All too often analysts spend their time focusing on tasks that don’t bring as much added value as they could. In this webinar we will discuss how we can define processes by identifying the steps that can be automated in order to focus on steps that need the expert input an analyst can provide, such as data interpretation and developments.

The KNIME principle of Guided Analytics will be demonstrated and implemented during the webinar via a study case from Avon.

During this webinar, we will guide you through the complete journey of a data scientist: from training and selecting the best machine learning model for your data to putting your model into production and creating a simple web application.

For this, we will demonstrate a use case of bioactivity prediction.

We will:

• Train and optimize four different machine learning methods (Naive Bayes, Logistic Regression, Random Forest, XGBoost)
• Identify the best model to predict the activity of a compound on a particular biological target
• Use KNIME’s new integrated deployment functionality to automatically deploy the best model
• Create a simple web application that uses the deployed model to predict the activity of new compounds

The webinar will round off with a Q&A session. We look forward to lots of questions!

The Netflix platform generates a LOT of data! How many movies are actually on Netflix? Do you think that Game of Thrones really was the most viewed TV series on Netflix? Which is the longest movie? Which country has produced most shows? Sometimes, you don’t actually need to go to the lengths of performing complex machine learning analysis.

Sometimes, a simple dashboard is a really effective tool to understand your data. Join this webinar, by Emilio Silvestri, to learn how to build an interactive dashboard to explore and visualize the data provided by Netflix in three simple steps!

• Step 1: Create some beautiful charts
• Step 2: Connect the charts to interact within a dashboard
• Step 3: Deploy the dashboard as a web page

After that, you have time left to admire those good-looking charts and Emilio will show how you can share them with the community on the KNIME Hub.

This webinar will be hosted by our partner Elder Research.

The push for organizations to incorporate analytics can result in rushed analyses with unreliable insights, even when many established best practices have been followed. A common root cause is what we refer to as the Tyranny of Coincidence, where spurious relationships in data appear significant.

Do you have data with hundreds or even thousands of possible predictors? Is your organization relying on categorical attributes with dozens of levels to inform critical models? Have you been tempted to use a “grid search” to systematically test hundreds of model parameterizations to optimally tune a model?

We will talk about how each of these scenarios lead to dangerous pitfalls without rigorous analytics discipline. At Elder Research we make model (and insight) validation a primary focus of every project, ensuring our clients can be confident that our work will hold up to time and scrutiny. In this webinar we will discuss some of the techniques we count on to avoid the Tyranny of Coincidence—target shuffling, robust combinations of cross-validation and hold out data, feature engineering, and more - with practical examples in KNIME.

This course introduces the main concepts behind Time Series Analysis, with an emphasis on forecasting applications: data cleaning, missing value imputation, time-based aggregation techniques, creation of a vector/tensor of past values, descriptive analysis, model training (from simple basic models to more complex statistics and machine learning based models), hyperparameter optimization, and model evaluation.

Learn how to implement all these steps using real-world time series datasets. Put what you’ve learnt into practice with the hands-on exercises.

This is a two day instructor-led course run by Professor Daniele Tonini and one of our KNIME data scientists with exercises in between.

Course content

• Introduction to Time Series Analysis and KNIME Components
• Understanding Stationarity, Trend and Seasonality
• Naive Method, ARIMA models, Residual Analysis
• Machine Learning, Model Optimization, Deployment

Join our partner Forest Grove and us for our first regional KNIME Summit. It will be all about how to transform your data practice and create value for the enterprise in production. Discuss with our experts, take a KNIME online course, learn from best practices, and sneak preview in the latest trends in data science.

Check out the website of Forest Grove for further information
 

SummitX APAC - Overview

1. DAY 1 - Keynote Sessions
2. DAY 2 - Trainings

1. DAY 1 - Keynote Sessions

October 28, 2020 | All times in AWST / UTC +8

2. DAY 2 - Trainings

October 29, 2020 | All times in AWST / UTC +8

KNIME Analytics Platform for Data Wranglers - Beginners

This course is designed for those who are just getting started on their data wrangler journey with KNIME Analytics Platform. It starts with an introduction of KNIME Analytics Platform and focuses on accessing, merging, transforming, fixing, standardising, 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.

• Start Time: 10:00 AM (AWST / UTC +8 (Perth))
• Duration: 2.5 Hours
• Cost: Free
• Select ticket type: Summit Day 1 + KNIME for Beginners Training

Register now

KNIME Server Course: Productionising and Collaboration

This course dives into the details of the commercial KNIME Server and KNIME WebPortal - discussing them from three different points of view: the power user, the administrator, and the end user. Find out how to exchange workflows and data between the server and the client, how to take advantage of the many server dedicated nodes and features when implementing a workflow, how to set access rights on workflows, data, and metanodes, share metanodes, execute workflows remotely and from the KNIME WebPortal, how to schedule report and workflow executions, and more.

• Start Time: 2:00 PM (AWST / UTC +8 (Perth))
• Duration: 2.5 Hours
• Cost: $99+GST
• Select ticket type: Summit Day 1 + KNIME Advanced Server Training

Register now

Please note: This webinar will beheld in French.

Join our trusted partner, MYDRAL, in this webinar to discover how KNIME and Tableau work seamlessly together to support your data journey. Taking the role of a sales analyst in a retail company, you’ll learn how to prepare your data and directly push the output to Tableau to create powerful visualizations and dashboards that update as the data changes.

Find out how KNIME and Tableau enable you to:

• Clean, blend, and join data across multiple data sources
• Deploy the created workflow to Tableau Server
• Build and automate your dashboards

This course will be hosted by our partner IQuartil and held in Spanish.

The course 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 15-minute wrap up session.

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
• Session 5: Wrap-up session

This course will be hosted by our partner IQuartil and held in Spanish.

The 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 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 15-minute wrap up session.

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
• Session 5: Wrap-up session

Our partner, H2O.ai, is hosting a webinar to share the details and benefits of the recent integration of H2O.ai and KNIME, which enables enterprises to create more accurate predictions and reduce time to value when implementing data science projects.

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 essential for selecting the techniques that answer a company’s questions in the best way.

The lack of end-to-end tooling prevalent in most data practices also makes it difficult to ensure data lineage. This H2O.ai and KNIME integration provides a solution that covers these challenges as well as increases data scientists’ productivity, reduces overall IT spend, and creates and uses more accurate predictions.

See this integration in action in a live demo and find out more about the benefits available to customers, specifically:

• Enable fast, reliable, and flexible access to data, insights, and predictions in enterprise data
• Build a single integrated visual data science workflow from data preparation to AutoML and to model production
• Simplify and speed up machine learning with the power of H2O Driverless AI automatic machine learning to business analysts in KNIME Analytics Platform and KNIME Server
• Productionize H2O Driverless AI models(MOJO) using KNIME drag and drop canvas for model management and monitoring

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 15-30 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

This course is designed for Life Scientists 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 with a focus on Life Science data.

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 and Data Import
• Session 2: Data Manipulation & Data Aggregation
• Session 3: Data Visualization & Data Mining
• Session 4: Database Extension & External Tools

This course dives into the details of KNIME Server and KNIME WebPortal. Learn how to use KNIME Server to collaborate with colleagues, automate repetitive tasks, and deploy KNIME workflows as analytical applications and services.

Specifically, learn how to share workflows, data, and components with colleagues and among different functions within the company. Learn how to set access rights on your workflows, data, and components, execute workflows remotely on KNIME Server and from the KNIME WebPortal, and schedule report and workflow executions.

You’ll also learn how to build and deploy an analytical application using KNIME Software and how to automate the deployment task using the KNIME Integrated Deployment Extension.

This is an instructor-led course consisting of three 60-minute 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.

Course Content

• Session 1: Introduction to KNIME Software, Collaboration, & Workflow Execution
• Session 2: Components & WebPortal Workflows
• Session 3: Integrated Deployment, REST API, & Administration

This course introduces you to the most commonly used Machine Learning algorithms used in Data Science applications.

At the course we will explore different supervised algorithms for classification and numerical problems such as decision trees, logistic regression, and ensemble models. We will also look at recommendation engines and neural networks and investigate the latest advances in deep learning. In addition, we will examine unsupervised learning techniques, such as clustering with k-means, hierarchical clustering, and DBSCAN.

We will also discuss various evaluation metrics for trained models and a number of classic data preparation techniques, such as normalization or dimensionality reduction.

This course is designed for current and aspiring data scientists who would like to learn more about machine learning algorithms used commonly in data science projects.

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 to 30-minute wrap up session.

Course Content:

• Session 1: Introduction and Decision Tree Algorithm
• Session 2: Regression Models, Ensemble Models, and Logistic Regression
• Session 3: Neural Networks and Recommendation Engines
• Session 4: Clustering and Data Preparation