Big Data And Artificial Intelligence Principles And Practices

Category: Big Data And Artificial Intelligence Principles And Practices

Course Description

This course is a combination of the two back-to-back courses: Big Data Principles and Practices and Artificial Intelligence Principles and Practices (2 days).  It provides a discounted, 5-day option to participants who choose to learn about both topics and their roles in Data Management.

The rise of Big Data has accelerated the pace of disruption in virtually every industry, creating vast ambiguity and unease. However, these changes are also creating enormous opportunities, as the tools to prosper during the age of Big Data disruption are becoming more accessible and available. To become a disruptor, embracing Big Data and deep diving into the technology is only a part of the answer. It needs to be combined with understanding how Big Data can drive value within our functions, companies, and industries.

Furthermore, organizations are creating an avalanche of data, and with Artificial Intelligence (AI) technology we can put that data to work in order to increase benefits and reduce costs. With modern technology we can use structured and unstructured data and apply Artificial Intelligence to bring new possibilities to improve decision making, improve company performance and augment human capabilities. However, this new field of science comes with new terminologies, technologies, jobs and organizational processes.

During this course participants gain a thorough understanding of how Big Data and Artificial Intelligence are creating new insights that are enabling organizations to develop better products, to service clients in a more personal manner, and to make supply chain processes more efficient. Participants also gain insight into the transformation their organization can go through to become data driven. 

Course Methodology

This courses applies a variety of interactions, ranging from team-work on case studies, to individual work on applying templates to their own experience, to group discussions about joint challenges. 

Course Objectives

By the end of the course, participants will be able to:

  • Assess and explain the value that Big Data and AI can deliver to their industries, companies, and functions
  • Demonstrate Big Data and AI technologies and their benefits
  • Develop the maturity of Big Data within their organization
  • Apply a variety of use cases to drive ideation
  • Build an organization-wide Big Data program
  • Discuss on a qualified level with business and data specialists on relevant topics

Target Audience

This course is designed for: organizational managers and leaders looking to understand Big Data and Artificial Intelligence, and take ownership of the data management agenda within their companies; functional leaders that are designing the Big Data and/or AI Roadmaps for their function; Big Data practitioners ready to take a business view on Data Management; and experienced practitioners looking to gain latest insights.

In short, this course is for managers wanting to identify what Big Data and Artificial Intelligence can do for them and to drive the Data and Digital Transformation, rather than understand the technical methodologies of what happens underneath its hood. 

Target Competencies

  • Formulate business objectives for Big Data
  • Project management of Big Data and/or AI
  • Big Data and AI change management
  • Big data technology sensing
  • AI Best Practice Application

Course Outline

  • Introduction to Big Data technologies
  • Trends in Big Data
  • Big Data applications, use cases and best practices across industries and functions
  • Data sourcing strategies and challenges
  • Ideation phase: creating first successes
  • From ideation to Proof-of-Concept and minimum viable product
  • Big Data Maturity model
  • Developing a Big Data roadmap
  • What does good look like: determining your Big Data end game
  • Orchestrating Big Data maturity across data, technology and people
  • Lean/agile working in support of Big Data transformation
  • Key success factors for adoption of Big Data at speed
  • Required skills & competencies for successful digital transformation
  • Understand the mindset of digital disruptors
  • AI as a concept and appearances
  • AI as a combination of technologies
  • AI in historical perspective
  • AI: sense, reason, act
  • The thinking in AI: Machine learning
  • 9 building blocks
  • Supervised learning and applications
    • Classification: Algoritms like Naïve bayes
    • Regression: Algorithms like Linear regression and decision trees
  • Semi supervised learning and applications
    • Algorithms like Q-Learning, SARSA
  • Unsupervised learning and applications
    • Clustering: Algorithms like kMeans and hierarchical
  • Practice with building blocks and use cases
  • Creative garage approach to ideate and define an AI Project
  • Successful use cases by Porter’s value chain
    • Primary activities: Inbound operations and outbound marketing and sales and service
    • Supporting activities: Admin and finance, HR, research and development, procurement
  • Successful use cases by technology
    • NLP
    • Image recognition
    • Machine learning
  • Project process
    • Ideation & problem definition
    • Exploratory data analysis
    • Model development
  • Implementation
  • Skills and capabilities
  • Organizational changes
  • 10 pitfalls
  • Technologies: R, Python, Spotfire, Hadoop etc
  • Platforms: Ms Azure, IBM Watson, Google Tensorflow
  • Roadmap development
  • Develop your strategy and tactics to realize an AI project funnel