Implementing AI in Business: Stakeholder Strategies for the Modern Data Driven, Automated Enterprise

Implementing Big Data & Artificial Intelligence (AI) for Business Professionals is an introductory-level course that delves into the core AI and how AI can be practically exploited in the modern business sense. This one-day class explores the possibilities that exist to transform your business, and significantly improve KPIs across a broad range of business units and applications.

Part 1: What is Data Science?

The story of Data

• How Big Data exploded and what has changed to make data the new oil

AI and Machine Learning

• The history of AI to ML to DL and an introduction to Neural Networks.

Why is this data useful?

• What it means to be data driven and how our paradigm is changing

Use Cases for Data Science

• 20+ of the most common business use cases

Understanding the Data Science ecosystem

• Overview of the key concepts related to Data Science to include open source, distributed computing, and cloud computing

Part 2: Making Data Science work for your organization

How can Data Science help guide your strategy

• Use Data Science to guide strategy based on insights into your customers, your product performance, your competition, and additional factors

Forming your strategy for Big Data and Data Science

• Step by step instructions for scoping your data science initiative based on your business goals, stakeholder input, putting together project teams, and determining the most relevant metrics

Implementing AI & Machine Learning (Analytics, Algorithms, and Machine Learning)

• How to select models and the importance of agile to realize business value

Choosing your tech

• Choosing your technology for your proposed use case

Building your team

• The key roles that need to be filled in Big Data and Data Science programs and considerations for outsourcing roles

Governance and legal compliance

• Principles in privacy, data protection, regulatory compliance and data governance and their impact on legal, reputational, and internal perspectives.
• Discussions of:
  • PII
  • GDPR

Case Study

• Explore a high-profile project failure and best practices for Data Science success

What the Future Hold

Students attending this class should have a grounding in Enterprise computing. While there s no particular class to offer as a prerequisite, students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI.

• Traditional enterprise business decision makers: Product Managers, Tech Leads, Managing Partners, IT Managers
• Analytics Managers who are leading a team of analysts
• Business Analysts who want to understand data science techniques
• Analytics professionals who want to work in machine learning or artificial intelligence
• Graduates looking to build a career in Data Science and machine learning
• Experienced professionals who would like to harness machine learning in their fields to get more insight about customers