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Course Outline

Introduction to Data Science/AI

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

Data Science Process

  • Crisp-dm methodology
  • Data preparation
  • Modeling strategy
  • Building models
  • Communication of results
  • Deployment

Data Science Tools

  • Prototyping languages
  • Big Data frameworks
  • Comprehensive solutions for common challenges
  • Basics of the Python language
  • Connecting Python with Spark

AI in Corporate Environments

  • The AI ecosystem
  • AI ethics
  • Strategies for implementing AI in business

Data Origins

  • Data categories
  • SQL versus NoSQL
  • Data storage mechanisms
  • Data preprocessing

Data Analysis – Statistical Methods

  • Probability theory
  • Statistical concepts
  • Statistical modeling techniques
  • Business applications using Python

Machine Learning in Business

  • Supervised versus unsupervised learning
  • Forecasting tasks
  • Classification challenges
  • Clustering techniques
  • Anomaly detection
  • Recommendation systems
  • Mining association patterns
  • Addressing ML issues with Python

Deep Learning

  • Limits of traditional ML algorithms
  • Tackling complex issues with Deep Learning
  • Getting started with Tensorflow

Natural Language Processing

Data Visualization

  • Visualizing modeling outcomes
  • Avoiding common visualization errors
  • Creating visualizations with Python

From Data to Decisions – Communication

  • Generating impact: Data-driven storytelling
  • Enhancing influence effectiveness
  • Overseeing Data Science projects

Requirements

No prior experience or specific prerequisites are required to participate in this course.

 35 Hours

Number of participants


Price per participant

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