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Course Outline

Introduction to Data Science/AI

  • Gaining knowledge through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and new analytics approaches
  • Key technologies

Data Science Workflow

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

Data Science Technologies

  • Languages used 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 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 vs. unsupervised learning
  • Forecasting problems
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Solving ML problems with Python

Deep Learning

  • Problems where traditional ML algorithms fail
  • Solving complex problems with Deep Learning
  • Introduction to TensorFlow

Natural Language Processing

Data Visualization

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

From Data to Decision – Communication

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

Requirements

No specific prerequisites are required to attend this course.

 35 Hours

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