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Course Outline

Introduction to Data Science and AI

  • Acquiring knowledge from data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and modern analytics approaches
  • Essential technologies

Data Science Workflow

  • Crisp-dm methodology
  • Data preparation
  • Model planning
  • Model construction
  • Communication strategies
  • Deployment

Data Science Technologies

  • Languages for prototyping
  • 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
  • Strategies for implementing AI 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 tasks
  • Classification tasks
  • Clustering tasks
  • Anomaly detection
  • Recommendation systems
  • Mining association patterns
  • Addressing ML challenges with Python

Deep Learning

  • Limits of traditional ML algorithms
  • Tackling complex problems with Deep Learning
  • Introduction to Tensorflow

Natural Language Processing

Data Visualization

  • Presenting modeling results visually
  • Common visualization errors
  • Visualizing data with Python

From Data to Decision – Communication

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

Requirements

No prior specific requirements are necessary to participate in this course.

 35 Hours

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Price per participant

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