Get in Touch

Course Outline

Introduction to Data Science/AI

  • Acquiring knowledge through data
  • Representing knowledge
  • Creating value
  • Overview of Data Science
  • The AI ecosystem and new approaches to analytics
  • Key technologies

Data Science workflow

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

Data Science technologies

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

AI in Business

  • AI ecosystem
  • Ethics of AI
  • Strategies for driving AI in business

Data sources

  • Data types
  • 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
  • Forecasting problems
  • Classfication problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Addressing ML problems with Python

Deep learning

  • Challenges where traditional ML algorithms fall short
  • Tackling complex issues with Deep Learning
  • Introduction to Tensorflow

Natural Language processing

Data visualization

  • Reporting modeling outcomes visually
  • Common visualization pitfalls
  • Data visualization with Python

From Data to Decision – communication

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

Requirements

Participants do not need any prior specific requirements to enroll in this course.

 35 Hours

Number of participants


Price per participant

Testimonials (7)

Upcoming Courses

Related Categories