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Course Outline

Foundations of Data Science and AI

  • Extracting insights from data
  • Methods of knowledge representation
  • Generating business value
  • Overview of Data Science
  • The AI landscape and modern analytics approaches
  • Core technologies

Data Science Methodologies

  • CRISP-DM framework
  • Data preparation techniques
  • Modeling strategy
  • Constructing models
  • Effective communication
  • Implementation and deployment

Technologies in Data Science

  • Languages for prototyping
  • Big Data infrastructure
  • Comprehensive solutions for common challenges
  • Basics of the Python language
  • Integrating Python with Spark

AI in the Business Environment

  • The AI ecosystem
  • Ethical considerations in AI
  • Driving business growth with AI

Data Sources

  • Various data types
  • Comparing SQL and NoSQL
  • Data storage solutions
  • Data preparation processes

Statistical Approaches to Data Analysis

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

Machine Learning in Business

  • Supervised vs. unsupervised learning
  • Prediction tasks
  • Classification challenges
  • Clustering techniques
  • Detecting anomalies
  • Building recommendation systems
  • Mining association patterns
  • Resolving ML challenges with Python

Deep Learning

  • Limitations of traditional ML algorithms
  • Tackling complex issues with Deep Learning
  • Getting started with Tensorflow

Natural Language Processing

Data Visualization

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

Turning Data into Decisions – Communication

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

Requirements

No prior specific requirements are necessary to enroll in this course.

 35 Hours

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Price per participant

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