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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 modern analytics approaches
  • Essential technologies

Data Science Workflow

  • CRISP-DM methodology
  • Preparing data
  • Planning models
  • Building models
  • Communication
  • Deployment

Data Science Technologies

  • Languages 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
  • AI ethics
  • 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 challenges
  • Anomaly detection
  • Recommendation systems
  • Mining association patterns
  • Addressing ML problems with Python

Deep Learning

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

Natural Language Processing

Data Visualization

  • Reporting visual outcomes from modeling
  • Common visualization pitfalls
  • Data visualization using Python

From Data to Decision – Communication

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

Requirements

No specific prerequisites are required to enroll in this course.

 35 Hours

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Price per participant

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Provisional Upcoming Courses (Require 5+ participants)

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