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Course Outline

Introduction to Data Science/AI

  • Gaining insights through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and modern analytics perspectives
  • Essential technologies

Data Science workflow

  • Crisp-dm
  • Preparing data
  • Planning models
  • Building models
  • Communication
  • Deployment

Data Science technologies

  • Languages for prototyping
  • Big Data technologies
  • Comprehensive solutions for common issues
  • Getting started with Python
  • Integrating Python with Spark

AI in Business

  • The AI ecosystem
  • AI ethics
  • Implementing AI in business operations

Data sources

  • Various data types
  • SQL vs NoSQL
  • Data Storage
  • Data preparation

Data Analysis – Statistical approach

  • Probability
  • Statistics
  • Statistical modeling
  • Applying business insights using Python

Machine learning in business

  • Supervised vs unsupervised learning
  • Prediction tasks
  • Classification tasks
  • Clustering tasks
  • Identifying anomalies
  • Recommendation systems
  • Mining association patterns
  • Addressing ML challenges with Python

Deep learning

  • Limitations of traditional ML algorithms
  • Tackling complex issues with Deep Learning
  • Intro to Tensorflow

Natural Language processing

Data visualization

  • Presenting visual results from models
  • Common errors in visualization
  • Visualizing data with Python

From Data to Decision – communication

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

Requirements

No prior specific prerequisites are required to enroll in this course.

 35 Hours

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