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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 analytical approaches
  • Core technologies

Data Science Methodologies

  • CRISP-DM framework
  • Data preparation
  • Model planning
  • Model construction
  • Communication
  • Deployment

Data Science Technologies

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

AI in Business

  • AI ecosystem
  • AI ethics
  • Implementing AI in business

Data Sources

  • Data types
  • SQL versus NoSQL
  • Data storage
  • Data preparation

Data Analysis – Statistical Methods

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

Machine Learning in Business

  • Supervised versus unsupervised learning
  • Forecasting challenges
  • Classification problems
  • Clustering problems
  • Anomaly detection
  • Recommendation systems
  • Association pattern mining
  • Addressing ML challenges with Python

Deep Learning

  • Challenges where traditional ML algorithms fall short
  • Resolving complex issues with Deep Learning
  • Introduction to TensorFlow

Natural Language Processing

Data Visualization

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

From Data to Decision – Communication

  • Making an impact: data-driven storytelling
  • Effectiveness of influence
  • Managing Data Science projects

Requirements

No prior specific prerequisites are necessary to participate in this program.

 35 Hours

Number of participants


Price per participant

Testimonials (7)

Provisional Upcoming Courses (Require 5+ participants)

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