Faysal AzizFA-0762Data & AnalyticsAI for Leaders & Business
Data Science for Executives
Understand AI, machine learning and big-data decisions without coding
Introduction
Why this course
Build a clear business-level understanding of data, AI, machine learning and big data. The course explains how these concepts relate and how to assess a potential initiative without requiring programming or advanced mathematics.
Use discussions and illustrative cases to examine suitable problems, technical requirements, team skills, platforms and costs. This is decision-making orientation, not tool-development training.
Learning outcomes
Learning outcomes
- Distinguish computer science, algorithms, programming, data science, ML, AI and big data.
- Explain an introductory machine-learning workflow and when ML may be appropriate.
- Identify relevant team skills and implementation requirements.
- Compare big-data tools, challenges and cost considerations at a business level.
- Critically discuss illustrative business uses without assuming guaranteed benefits.
Prerequisites
Prerequisites
Basic computer and internet familiarity. No coding or advanced mathematics is required.
A computer and reliable internet are needed for remote delivery or shared examples.
Training outline
2 modules
·
01Day 1 — Core concepts and machine learning6 topics
- Computer science, algorithms and programming: accessible definitions.
- Structured, semi-structured and unstructured data; how data relates to business questions.
- Big data, AI, ML and data science: differences and connections.
- Computing capabilities, technology impact and suitable versus unsuitable ML problems.
- An illustrative five-step learning workflow, with feedback and iteration rather than a universal mandatory process.
- Applying ML to a data-science problem; starting points, platforms and an illustrative case.
02Day 2 — Big-data choices and executive review7 topics
- Why Big Data?
- Tools of the trade
- Implementation
- Challenges
- Comparative analysis
- Cost factor
- What to look for when choosing to utilize Big Data
- Assess implementation requirements and team skills.
- Compare the proposed approach with simpler alternatives and review expected costs and constraints.
- Concluding discussion and questions.
A programme built around your team.
Share your training goals and requirements.