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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 a new perspective on analytics
  • Essential technologies

Data Science Workflow

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

Data Science Technologies

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

AI in Business

  • The AI ecosystem
  • Ethical considerations in 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 challenges
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Mining association patterns
  • Solving ML problems using Python

Deep Learning

  • Scenarios where traditional ML algorithms fall short
  • Addressing complex problems with Deep Learning
  • Introduction to Tensorflow

Natural Language Processing

Data Visualization

  • Visualizing reporting outcomes from models
  • Common pitfalls in visualization
  • Data visualization using Python

From Data to Decision – Communication

  • Making an impact through data-driven storytelling
  • Improving influence effectiveness
  • Managing Data Science projects

Requirements

No specific prerequisites are required to participate in this course.

 35 Hours

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