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 Duration 35 hours (5 days)

Course Outline

Introduction to Data Science/AI

  • Gaining insights through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and modern analytics approaches
  • Essential technologies

Data Science workflow

  • Crisp-dm
  • Data preparation
  • Model planning
  • Model building
  • Communication
  • Deployment

Data Science technologies

  • Languages for prototyping
  • Big Data technologies
  • End-to-end solutions for common challenges
  • Introduction to the Python language
  • Integrating Python with Spark

AI in Business

  • The AI ecosystem
  • AI ethics
  • Implementing AI within business operations

Data sources

  • Data types
  • 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
  • Classfication problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Addressing ML problems with Python

Deep learning

  • Scenarios where traditional ML algorithms fall short
  • Tackling complex issues with Deep Learning
  • Introduction to Tensorflow

Natural Language processing

Data visualization

  • Visualizing modeling results
  • Common visualization errors
  • Data visualization using Python

From Data to Decision – communication

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

Requirements

No specific prerequisites are required to participate in this course.

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