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Course Outline

Introduction to Data Science/AI

  • Gaining insights through data
  • Representing knowledge structures
  • Generating business value
  • Overview of Data Science
  • AI ecosystem and modern analytics approaches
  • Essential technologies

Data Science workflow

  • CRISP-DM framework
  • Preparing data
  • Planning models
  • Constructing models
  • Communicating findings
  • Deployment strategies

Data Science technologies

  • Prototyping languages
  • Big Data technologies
  • End-to-end solutions for common challenges
  • Getting started with Python
  • Integrating Python with Spark

AI in Business

  • Understanding the AI landscape
  • Ethical considerations in AI
  • Strategies for driving AI adoption in business

Data sources

  • Categorizing data types
  • Comparing SQL and NoSQL
  • Data storage solutions
  • Preprocessing data

Data Analysis – Statistical approach

  • Probability concepts
  • Statistical principles
  • Building statistical models
  • Applying statistics in business using Python

Machine learning in business

  • Supervised versus unsupervised learning
  • Forecasting challenges
  • Classification tasks
  • Clustering tasks
  • Identifying anomalies
  • Building recommendation systems
  • Mining association patterns
  • Implementing ML solutions with Python

Deep learning

  • Scenarios where traditional ML falls short
  • Addressing complex issues with Deep Learning
  • Introduction to TensorFlow

Natural Language processing

Data visualization

  • Presenting modeling results visually
  • Avoiding common visualization errors
  • Creating visualizations using Python

From Data to Decision – communication

  • Creating impact: data-driven storytelling
  • Enhancing influence effectiveness
  • Oversight of 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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