Course Outline

Introduction to Data Science/AI

  • Knowledge acquisition through data
  • Knowledge representation
  • Value creation
  • Data Science overview
  • AI ecosystem and new approach to analytics
  • Key technologies

Data Science workflow

  • Crisp-dm
  • Data preparation
  • Model planning
  • Model building
  • Communication
  • Deployment

Data Science technologies

  • Languages used for prototyping
  • Big Data technologies
  • End to end solutions to common problems
  • Introduction to Python language
  • Integrating Python with Spark

AI in Business

  • AI ecosystem
  • Ethics of AI
  • How to drive AI in business

Data sources

  • Types of data
  • SQL vs NoSQL
  • Data Storage
  • Data preparation

Data Analysis – Statistical approach

  • Probability
  • Statistics
  • Statistical modeling
  • Applications in business using Python

Machine learning in business

  • Supervised vs unsupervised
  • Forecasting problems
  • Classfication problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Solving ML problems with Python language

Deep learning

  • Problems where traditional ML algorithms fails
  • Solving complicated problems with Deep Learning
  • Introduction to Tensorflow

Natural Language processing

Data visualization

  • Visual reporting outcomes from modeling
  • Common pitfalls in visualization
  • Data visualization with Python

From Data to Decision – communication

  • Making impact: data driven story telling
  • Influence effectivnes
  • Managing Data Science projects
  35 Hours


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