Big Data & Artificial Intelligence 101

Learning Objectives:
After this course, you will be able to:

1. Explore Perplexity AI’s interface and demonstrate its key research functionalities.
2. Apply Perplexity AI’s tools for source-verified content creation, deep research, and summarization.
3. Compare the features and benefits of free vs. professional tiers to optimize usage.

Curriculum
Module 1: Foundations of Perplexity AI – Free Tier

1. Understanding Generative AI & NLP Concepts
2. The 2025–2026 AI landscape: where Perplexity fits alongside ChatGPT, Gemini, and Claude
3. Core NLP concepts: perplexity scores, retrieval-augmented generation (RAG), and citation grounding
4. Evolution from GPT-3 to reasoning models (o3, Opus 4) and specialized search architectures
5. Why real-time web grounding matters: knowledge cutoffs vs. live indexing
6. Perplexity AI: History, Architecture & Differentiators
7. Major 2025–2026 milestones: Deep Research, Labs, Comet browser

Module 2: Interface Mastery & Core Search – Free Tier

1. Exploring Perplexity AI’s Interface & Research Capabilities
2. Focus modes: Web, Academic, YouTube, Reddit, and custom domain filters
3. Privacy controls: session toggles, the Snapshot widget, and opting out of data collection
4. Advanced fact-checking with parallel source analysis and reading citations
5. Learn Step-by-Step mode: interactive education for any topic (available to all users, 2026)
6. Privacy-first architecture: understanding data security vs. ChatGPT — Snapshot widget for session-level controls
7. Hands-on Exercises

Module 3: Deep Research & Spaces – Free Tier (limited)

1. Deep Research: autonomous multi-step queries across hundreds of sources → structured report
2. Creating a Space: setting topics, uploading reference docs, inviting collaborators, permission settings
3. Asking questions across uploaded files — Perplexity as a personal RAG engine
4. Team collaboration features and API credit management
5. Hands-on: Generate a Deep Research report on a current industry topic

Module 4: Real-World Use Cases & Workflows – Free Tier

1. Finance: real-time stock quotes, earnings data, and peer comparisons (launched Oct 2024, via Financial Modeling Prep)
2. Content creation: drafting blog posts, reports, and emails with live web research embedded
3. Technical research: code debugging, architectural reviews, and AI-assisted programming
4. Academic workflows: summarizing papers, finding counterarguments, building annotated bibliographies

Module 5: Labs, Model Switching & Advanced Pro Features – Pro Tier ($20/mo)

1. Perplexity Labs / Create Files & Apps (launched May 2025, Pro exclusive)
2. Multi-Model Switching (Pro exclusive)
3. Pro model roster (2026): GPT-5, Claude 4 Sonnet, Gemini 2.5 Pro, Perplexity Sonar
4. When to switch models: reasoning tasks vs. writing vs. broad research
5. Image generation: generating visuals directly in Perplexity’s interface (Pro exclusive)
6. Video generation: Veo 3.1 powered, up to 8-second videos with audio (Pro and above, 2025)

Learning Objectives
Learners will have the opportunity to:
1. Explore a practical framework for building audience-centered presentation narratives
2. Use AI tools to develop and refine story structure, messaging, and key takeaways
3. Practice techniques for making complex information accessible and memorable
4. Experiment with AI-assisted approaches to drafting, editing, and strengthening persuasive language
5. Evaluate AI-generated drafts and story structures critically, identifying where data evidence, human judgment, or audience knowledge should take precedence
6. Apply storytelling principles to a sample scenario and develop a presentation outline

Topic Outline
Foundations of Persuasive Storytelling
- Why stories persuade: the role of narrative, emotion, and logic
- Understanding your audience and what they need from you
- Defining your core message before you build
AI as a Storytelling Partner
- Where AI fits in the presentation development process
- Using AI to brainstorm, outline, and pressure-test your narrative
- Prompting strategies for clearer structure and stronger messaging
- Iterating with AI feedback to sharpen your argument
- Recognizing when AI gets it wrong, flat narratives, overconfident logic, and missed audience cues
Structuring Your Story
- Frameworks for organizing complex information
- Information hierarchy: leading with what matters most
- Building tension, context, and resolution into your narrative
- Matching your structure to your audience: technical, executive, and general readers
Language and Delivery
- Writing for a listening audience, not a reading one
- Using AI to refine word choice, tone, and concision
- Weaving data and evidence into a narrative structure
- Anticipating questions and objections
Putting It Together
- Applying the workshop frameworks to a sample scenario
- Developing a presentation outline with AI assistance
- Peer review and reflection

Prerequisites
No prior experience with AI tools is required. Participants should be comfortable creating and delivering presentations in a professional setting.

Date & Time:
5/26/2026 : 9am - 12 pm PST
5/28/2026 : 9am - 12 pm PST

Description:
Cultivate a deeper understanding of Artificial Intelligence (AI) with this engaging lecture. Our non-intimidating introduction unravels the distinctions between AI and Machine Learning, delving into the technology's core, including neural networks and Deep Learning. Explore the intricacies of how machines learn and grasp the paramount role of data. Gain insights into crafting AI projects through a guided demonstration. Discover the awe-inspiring capabilities of AI and learn to identify opportunities for its application within your organization. Join us on this illuminating journey into the world of AI and unleash its transformative potential.

Learning Objectives:
After this course, you will be able to:

• Understand the differences among AI, Machine Learning, and Deep Learning
• Understand how machines learn
• Understand the importance of data and how to acquire appropriate data
• Understand AI strategy and building AI projects
• Recognizing opportunities for AI
• Understand some of the tools employed in executing AI