This three day course is aimed at those who are familiar with the essentials when working with data and are interested in learning about how Data Science, Analytics, Machine Learning, and Artificial Intelligence (AI) can be used to yield value [...]
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This three day course is aimed at those who are familiar with the essentials when working with data and are interested in learning about how Data Science, Analytics, Machine Learning, and Artificial Intelligence (AI) can be used to yield value from data assets.This course will be of interest if you are interested in developing your own skills to move from analytics to Data Science, or if you are supporting organisational digital change, or if you are working with Data Scientists and want to learn more about what’s possible.You will be introduced to key concepts and tools for use in Data Science, including typical Data Science Project lifecycles, potential applications & project pitfalls, relevant aspects of data governance and ethics, roles and responsibilities, Machine Learning and AI model development, exploratory analysis and visualisation and strategies for working with Big Data.Throughout the course you will engage with activities and discussions with one of our Data Science technical specialists. Two of the course modules will allow you to complete ‘low or no’-code practical labs in order to test and compare the capabilities of Python and R, and to see a Machine Learning or AI workflow using Orange – giving you enough to start some ideas flowing and try things in your workplace or continue learning on one of our technical training routes into Data Science, Machine Learning, and AI with a firm grounding in key Data Science concepts.

  • Compare commonly used Data Science tools with taster activities to try out R and Python
  • Use a no-code drag and drop tool to create a simple Machine Learning model and develop ideas for Data Science projects
  • Discuss the need for Data Governance in supporting Data Scientists creating Machine Learning and AI systems for extracting value from data
  • Follow a typical Data Science project lifecycle for providing AI models
  • Understand typical workflows for exploring and visualising data for analysis
  • Identify practical ways in which AI and Machine Learning models can be reported in order to facilitate governance oversight
  • Understand that legal and regulatory frameworks for AI are evolving and discuss some of the most recent developments
  • Identify the storage and analytics challenges that Big Data might pose in Data Science projects
  • Begin to develop a plan for personal and organisational learning towards Data Science

Mám záujem o vybraný QA kurz