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Course Outline

Introduction to Data Science and AI

  • Gaining insights through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI landscape and modern analytics approaches
  • Core technologies

Data Science Workflow

  • CRISP-DM methodology
  • Preparing data
  • Planning the model
  • Building the model
  • Communicating findings
  • Deployment

Data Science Technologies

  • Prototyping languages
  • Big Data technologies
  • End-to-end solutions for common challenges
  • Getting started with Python
  • Integrating Python with Spark

AI in Business

  • The AI ecosystem
  • Ethical considerations in AI
  • 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 challenges
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Addressing ML problems with Python

Deep Learning

  • Limitations of traditional ML algorithms
  • Tackling complex problems with Deep Learning
  • Introduction to TensorFlow

Natural Language Processing

Data Visualisation

  • Reporting modelling outcomes visually
  • Common visualisation pitfalls
  • Data visualisation with Python

From Data to Decision – Communication

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

Requirements

There are no specific prerequisites required to enrol in this course.

 35 Hours

Number of participants


Price per participant

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