Implementing Artificial Intelligence 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.
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
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, data science and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This course is intended to be an introduction to machine learning for non-technical business professionals. This course goes beyond the hype of machine learning and focuses on how it is used in business. Attendees might include:
- 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
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.
Part 1: What is Data Science?
The Story of Data - How we Got Here
- 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 Data Science (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
Modern Practical Case Study
- Explore a high-profile project failure and best practices for Data Science success
What the Future Holds
Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.