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Course Outline

Introduction to Data Science and AI

  • Acquiring knowledge through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and a new approach to analytics
  • Key technologies

Data Science workflow

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

Data Science technologies

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

AI in Business

  • The AI ecosystem
  • Ethics of AI
  • How to drive AI initiatives in business

Data sources

  • Types of data
  • SQL versus NoSQL
  • Data storage
  • Data preparation

Data Analysis – Statistical approach

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

Machine learning in business

  • Supervised versus unsupervised learning
  • Forecasting problems
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Solving machine learning problems with Python

Deep learning

  • Problems where traditional machine learning algorithms fail
  • Solving complex problems with deep learning
  • Introduction to TensorFlow

Natural Language Processing

Data visualisation

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

From Data to Decision – communication

  • Making an impact: data-driven storytelling
  • Influence effectiveness
  • Managing Data Science projects

Requirements

None

 35 Hours

Number of participants


Price per participant

Testimonials (7)

Provisional Upcoming Courses (Require 5+ participants)

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