Implementing Big Data & AI for Business Professionals (TTML5501)
About This Course
AI in Business Seminar Series: Explore How AI & Machine Learning Apply in Today’s Business Enterprise, Current Tools, Trends & More
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.
Course Outline
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
Learning Objectives
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
Material Includes
- Instructor-led training
- Training Seminar Student Handbook
- Collaboration with classmates (not currently available for self-paced course)
- Real-world learning activities and scenarios
- Exam scheduling support*
- Enjoy job placement assistance for the first 12 months after course completion.
- This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
- Government and Private pricing available.*
Requirements
- 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.
Target Audience
- 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