Here are the slides from our Data Science Learnathons. A learnathon is where we learn more about the data science cycle - data access, data blending, data preparation, model training, optimization, testing, and deployment. We also work in groups to hack a workflow-based solution to guided exercises. The tool of choice for this learnathon is KNIME Analytics Platform.

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From Raw Data to Deployment
KNIMEr: rosaria.silipo@knime.com
KNIMEr: maarit.widmann@knime.com
KNIMEr: jerome.treboux@hevs.ch
@KNIME

2. © 2018 KNIME AG. All Rights Reserved.
Do you recognize this?
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https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

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Let’s unroll it!
It always starts
with some data …
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Data
Preparation
Model
Training
Model
Optimization
Deployment
Data Manipulation
Data Blending
Missing Values Handling
Feature Generation
Dimensionality Reduction
Feature Selection
Outlier Removal
Normalization
Partitioning
…
Model Training
Bag of Models
Model Selection
Ensemble Models
Own Ensemble Model
External Models
Import Existing Models
Model Factory
…
Parameter Tuning
Parameter Optimization
Regularization
Model Size
No. Iterations
…
Performance Measures
Accuracy
ROC Curve
Cross-Validation
…
Files & DBs
Dashboards
REST API
SQL Code Export
Reporting
…
Model
Evaluation

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The many Lives of a Dataset
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Data
Preparation
Model
Training
Model
Optimization
Model
Evaluation
Deployment
Partitioning:
• Training Set
• Validation Set
• Test Set
Training Set Validation Set Test Set New Data from Real
World Applications
Original Data
Set with Past
Observations

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Data Exploration
• Sometimes in between Data Access and Data
Preparation there is a Data Exploration phase
• The Data Exploration phase is useful to get to
know the data
• KNIME offers a few visualization nodes to build
dashboards to explore the data
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What about Big Data?
• Big Data serves Scalability
• The whole Analytics Process is no different on
Big Data
• You need:
– a Big Data Platform
– The KNIME Big Data (Spark & Hive) Extension
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One Example for Every Need
The KNIME EXAMPLES Server
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50_Applications

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Classification Problem & Data Set
• Airline Dataset: http://stat-computing.org/dataexpo/2009/the-data.html
• Smaller dataset (Jan 2007) (AirlineDataset.table)
• Challenge:
Predict Departure Delays
If on original airline dataset, only flights from airport ORD
Output Class = “delay” if depdelay > 15min
otherwise “no delay”
Available features: date, dep time, arr time, carrier, destination, cancelled, …
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Challenges
• Group 1. Data Access and Data Preparation
• Group 2. ML Model Training
• Group 3. Model Deployment
• Import file Learnathon_2018.knar into your workspace
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Group 1. Data Access and Data Preparation
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Group 2. Model Training & Optimization
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Group 3. Deployment
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• Deployment Options – Multiple challenges:
– Workflow deployment to KNIME Server
– Remote/Scheduled execution from KNIME
Server
– KNIME RESTful Web Services
– Build a Composite Interactive Dashboard and
make it available on KNIME Web Portal
– Generate a report with BIRT
– Write Prediction Results to a Database

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KNIME Spring Summit 2019
March 18 – 22 at bcc Berlin Congress Center, Berlin
• Monday & Tuesday: One-day courses
• Wednesday & Thursday: Summit sessions
• Friday: Workshops
Use the code
LEARNATHON
for 10% off tickets!
Register at
knime.com/spring-summit2019

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KNIME Beginner’s Luck
Free Copy of KNIME Beginner’s Luck Book from KNIME Press
https://www.knime.com/knimepress
with this code: PARIS-1118
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15. © 2018 KNIME AG. All Rights Reserved.
You can find KNIMers here!
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• KNIME (www.knime.com)
• BLOG for news, tips and tricks(www.knime.com/blog)
• FORUM for questions and answers (tech.knime.com/forum)
• EXAMPLE SERVER for example workflows
• LEARNING HUB (www.knime.com/learning-hub)
• KNIME TV channel on
• KNIME on @KNIME
• KNIME on https://www.facebook.com/KNIMEanalytics
• On

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The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by KNIME.com AG under license from KNIME GmbH,
and are registered in the United States. KNIME® is also registered in Germany.
Thank You!