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Course Outline

Introduction to Data Science/AI

  • Acquiring knowledge through data
  • Methods of knowledge representation
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and modern approaches to analytics
  • Essential technologies

Data Science workflow

  • CRISP-DM framework
  • Data preparation
  • Model planning
  • Building models
  • Communication
  • Deployment

Data Science technologies

  • Prototyping languages
  • Big Data technologies
  • End-to-end solutions for common issues
  • Intro to the Python language
  • Integrating Python with Spark

AI in Business

  • Understanding the AI ecosystem
  • Ethical considerations in AI
  • Driving AI adoption in business

Data sources

  • Categories of data
  • SQL versus NoSQL
  • Data Storage
  • Data preparation

Data Analysis – Statistical approach

  • Probability
  • Statistics
  • Statistical modeling
  • Business applications using Python

Machine learning in business

  • Supervised versus unsupervised learning
  • Forecasting challenges
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Solving ML challenges with Python

Deep learning

  • Addressing limitations of traditional ML algorithms
  • Tackling complex problems with Deep Learning
  • Intro to Tensorflow

Natural Language processing

Data visualization

  • Presenting modeling outcomes visually
  • Avoiding common visualization errors
  • Creating visualizations with Python

From Data to Decision – communication

  • Creating impact: data-driven storytelling
  • Enhancing influence effectiveness
  • Managing Data Science projects

Requirements

This course does not have any specific prerequisites or prior requirements for enrollment.

 35 Hours

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Price per participant

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