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 Duration 35 hours

Course Outline

Intro to Data Science & AI

  • Acquiring knowledge from data
  • Representing knowledge
  • Generating value
  • Overview of Data Science
  • The AI ecosystem and modern analytics approaches
  • Essential technologies

Data Science Process

  • CRISP-DM framework
  • Preparing data
  • Planning models
  • Building models
  • Communication strategies
  • Deployment

Technologies in Data Science

  • Languages for prototyping
  • Big Data technologies
  • End-to-end solutions for common issues
  • Introduction to the Python language
  • Integrating Python with Spark

Business AI

  • The AI ecosystem
  • Ethical considerations in AI
  • Implementing AI in business settings

Data Sources

  • Data types
  • SQL vs. NoSQL
  • Storage solutions
  • Data preparation techniques

Data Analysis – Statistical Methods

  • Probability theory
  • Statistical principles
  • Statistical modeling
  • Business applications using Python

Business Machine Learning

  • Supervised vs. unsupervised learning
  • Forecasting tasks
  • Classification challenges
  • Clustering tasks
  • Anomaly detection
  • Recommendation systems
  • Association rule mining
  • Addressing ML problems with Python

Deep Learning

  • Limitations of traditional ML algorithms
  • Complex problem solving with Deep Learning
  • Introduction to TensorFlow

Natural Language Processing

Data Visualization

  • Presenting modeling results visually
  • Avoiding common visualization errors
  • Visualization techniques in Python

From Data to Decision – Communication

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

Requirements

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

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