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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 modern analytics approaches
  • Core technologies

Data Science workflow

  • CRISP-DM
  • Preparing data
  • Planning models
  • Building models
  • 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

  • Data types
  • SQL vs NoSQL
  • Data storage
  • Data preparation

Data Analysis – Statistical approach

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

Machine learning in business

  • Supervised vs unsupervised learning
  • Forecasting problems
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Resolving ML problems using Python

Deep learning

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

Natural Language processing

Data visualization

  • Visualising reporting outcomes from models
  • Common pitfalls in visualisation
  • Data visualisation with Python

From Data to Decision – communication

  • Driving impact: data-driven storytelling
  • Effectiveness of influence
  • Managing Data Science projects

Requirements

No specific prerequisites are required to participate in this course.

 35 Hours

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Price per participant

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