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Course Outline

Introduction to Data Science/AI

  • Gaining knowledge from data
  • Knowledge representation
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and emerging analytics approaches
  • Core technologies

Data Science workflow

  • CRISP-DM
  • Data preparation
  • Model planning
  • Model building
  • Communication
  • Deployment

Data Science technologies

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

AI in Business

  • AI ecosystem
  • Ethical considerations in AI
  • 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 versus unsupervised learning
  • Forecasting challenges
  • Classification challenges
  • Clustering challenges
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Addressing ML challenges 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

  • Visual reporting from modeling results
  • Common pitfalls in visualization
  • Data visualization with Python

From Data to Decision – communication

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

Requirements

There are no specific prerequisites for attending this course.

 35 Hours

Number of participants


Price per participant

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