Data Science and Predictive Analytics

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Education includes a practical part of a variety of predictive analytics practice examples within KNIME Analytics. KNIME Analytics is a tool for the entire data science process (preparation, analysis and presentation of data) and has been the leader in the Gartner Magic Quadrant in the field of machine learning and data science for the past two years.

SUMMARY

Data Science is the most significant trend in the field of data analytics, which requires a wider picture of data analytics starting from preparation, analysis, and presentation of data. Predictive analytics as a part of the field of data science gets more important because traditional reports can answer the past and the present but not the future. This training covers the theme of data mining including data preparation, modeling predictive models, model interpretation and evaluation, and presentation of results.

Education includes a practical part of a variety of predictive analytics practice examples within KNIME Analytics. KNIME Analytics is a tool for the entire data science process (preparation, analysis and presentation of data) and has been the leader in the Gartner Magic Quadrant in the field of machine learning and data science for the past two years.

AUDIENCE

Education is intended for all those who use information in their everyday work, regardless of their previous knowledge of predictive analytics. The basics of computer work and basic knowledge of statistics are a prerequisite for this education.

CONTENT

Day 1

  • Introduction to predictive analytics
  • CRISP methodology
  • Data sources and data organization
  • Understanding Data
  • Preparation of data
  • Imputing missing data for modeling purposes
  • Influence of outliers and extremes on models and solving such values
  • Practical examples in the tool (KNIME Analytics Platform)

Day 2

  • Correlation of data
  • Sampling data, data partitioning, data balancing
  • Model design
  • Predictive model types (supervised/unsupervised) and their application
  • Types of algorithms and their application (regression, trees, neural networks, etc.)
  • Understanding the Model, Model Accuracy, Model Evaluation
  • Practical examples in the tool (KNIME Analytics Platform)


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