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Course Outline

Introduction to Data Science/AI

  • Gaining knowledge through data
  • Structuring and representing knowledge
  • Generating 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 utilised 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
  • Ethics of AI
  • Driving AI initiatives in business

Data sources

  • Types of data
  • 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 challenges
  • Classification challenges
  • Clustering challenges
  • Anomaly detection
  • Recommendation systems
  • Association pattern mining
  • Solving ML challenges with Python

Deep learning

  • Situations where traditional ML algorithms fall short
  • Tackling complex challenges with Deep Learning
  • Introduction to TensorFlow

Natural Language processing

Data visualization

  • Visual reporting of modelling outcomes
  • Common pitfalls in visualization
  • Data visualization with Python

From Data to Decision – communication

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

Requirements

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

 35 Hours

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