Implementing Big Data & AI for Business Professionals (TTML5501) Training in Canada

Who Should Attend?

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.

This course introduces AI from a practical applied business perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will:

• Learn which data is most useful to collect now and why it’s important to start collecting that data as soon as possible
• Understand the intersection between big data, data science and AI (Machine Learning / Deep Learning) and how they can help you reach your business goals and gain a competitive advantage.
• Understand the factors that go into choosing a Data Science system, including whether to go with a cloud-based solution
• Explore common tools and technologies to aid in making informed decisions
• Gain the skills required to build your DS/ AI team

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