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Course Outline

Introduction to Data Science/AI

  • Acquiring knowledge through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and emerging analytics approaches
  • Essential technologies

Data Science workflow

  • CRISP-DM
  • Data preparation
  • Model planning
  • Model construction
  • 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
  • Driving AI adoption in business

Data sources

  • Types of data
  • SQL vs NoSQL
  • Data Storage
  • Data preparation

Data Analysis – Statistical approach

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

Machine learning in business

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

Deep learning

  • Challenges where traditional ML algorithms fall short
  • Tackling complex problems with Deep Learning
  • Introduction to TensorFlow

Natural Language processing

Data visualization

  • Visualizing modeling outcomes for reporting
  • Common visualization pitfalls
  • Data visualization with Python

From Data to Decision – communication

  • Creating impact: data-driven storytelling
  • Effectiveness in influence
  • Managing Data Science projects

Requirements

No specific prior requirements are necessary to participate in this course.

 35 Hours

Number of participants


Price per participant

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