knime-academy-101

🤟 Cevi Herdian (Knime Academic Alliance Partner) -> itsmecevi.github.io

📊 About Knime Analytics

📉 KNIME Technical Spesifications

📒 KNIME Changelog

🔮 Duration: 10 hours

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Overview

• Slide: Data-Driven Impact

• Slide: Open Source 101

• Slide: KNIME 101

• Big Question: Connecting the dots?

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Slide: Analytics 101

• Overview
• Statistics
• Data Mining
• Data Science
• Artificial Intelligence
• Machine Learning
• Neural Network
• Deep Learning

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Knime-Academy-101

• Slide: KNIME GUI Graphical User Interface
• Read Data:
  • CSV file-> airtravel.csv
  • MS Excel-> financial_sample.xlsx
  • Table-> adult_women.table
  • Database-> WebActivity.sqlite
• Transform:
  • Basic Row Filter
  • Advanced Row Filter
  • Column Filter
  • Transformation
  • Lag Column
  • Data Time Manipulation
  • Classic Aggregation with Group By
  • Pivoting
  • Join
  • Concatenate
  • Database Data Manipulation

• Analyze:

  • Data Explorer
  • Logistic Regression
  • Decision Tree
  • Confusion Matrix and Class Statistics
  • ROC Curve
  • Regression Model Evalution
  • Random Forest
• Deploy / Visualize:
  • Write Data to File
  • Export to BIRT Report
  • Visualization

Hands-On Workflow

• Simple Statistics
• Clustering
• Classification
• Forecasting
• Scripting (R & Python)

Industry Use Case (Practicing Data Science with KNIME course)

• Web & Social Media:
  • Document Classification: Spam vs Ham
  • Topic Detection: What is it all about?
  • Sentiment Analysis: What is with the tone?
  • Find the influencers
  • Sentiment & Influencers
• Customer Intelligence:
  • Churn Prediction
  • Customer Segmentation
• Retail and Supply Chain
  • Market Basket Analysis with the Apriori Algorithm
  • Movie Recommendations with Spark Collaborative Filtering

• Banking and Insurance: Credit Risk Assessment

• Web Analytics: Click Stream Analysis

• IoT:
  • Bike Restocking Alert with Minimum Set of Input Features
  • Taxi Demand Prediction using Random Forest on Spark
  • Anomaly Detection
• Cybersecurity:
  • Fraud detection
  • Fraud detection using a neural auto-encoder
• Text Generation:
  • Neural Machine Translation with RNN
  • Product Naming with Deep Learning for Marketers and Retailers-Keras Inspired

Preferences :

• knime.com
• https://trello.com/b/fCiyw9I4/references