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Implementing AI for Business Professionals
Code:
TTML5501
Duration:
1 Day
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$995
USD
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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.
This course is available in the following formats:
Duration: 1 Day
Duration: 1 Day
| Call 800-798-3901 to enroll in this class! |
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 its 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
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
- 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
Students attending this class should have a grounding in Enterprise computing. While theres 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.