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Course Outline

Introduction to Data Science and AI

  • Gaining knowledge through data
  • Knowledge representation
  • Value creation
  • Overview of Data Science
  • The AI ecosystem and emerging analytical approaches
  • Core technologies

Data Science Workflow

  • CRISP-DM
  • Data preparation
  • Model planning
  • Model building
  • Communication
  • Deployment

Data Science Technologies

  • Programming languages for prototyping
  • Big Data technologies
  • End-to-end solutions for common challenges
  • Introduction to Python
  • Integrating Python with Spark

AI in Business

  • The AI ecosystem
  • Ethics of AI
  • Driving AI adoption 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 challenges
  • Classification challenges
  • Clustering challenges
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Solving machine learning problems with Python

Deep Learning

  • Scenarios where traditional machine learning algorithms fall short
  • Solving complex problems using 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

  • Creating impact: data-driven storytelling
  • Enhancing influence effectiveness
  • Managing Data Science projects

Requirements

There are no specific prerequisites required to enroll in this course.

 35 Hours

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