Day 8 | Salesforce AI Associate | Data for AI | AI Bootcamp | Sanjay & Nikita

Day 8 | Salesforce AI Associate | Data for AI | AI Bootcamp | Sanjay & Nikita

Hello, everyone! Welcome back to Sanjay Gupta's Tech School. I'm Sanjay Gupta, and today, I'm joined once again by Nikita, who will share her deep knowledge on AI and data science. In this ongoing series, we aim to prepare you for the Salesforce AI Associate Certification by covering significant topics like Data for AI.

Recap of Previous Sessions

In the past seven sessions, we discussed the fundamentals of AI. Moving forward, we will focus on data, which is crucial for AI, especially within Salesforce.

Introduction to Data for AI

Data is the new oil and forms the foundation for AI. The different types of data—structured, unstructured, and semi-structured—each play a vital role in the AI ecosystem.

Types of Data

Structured Data

• Defined Format: Organized in tables, rows, and columns.
• Examples: Relational databases, spreadsheets, and CSV files.
• Characteristics: Easily searchable, consistent, has defined data types.
• Advantages: Efficient, scalable, accurate, and automatable.
• Disadvantages: Rigid structure that limits flexibility.

Semi-Structured Data

• Flexible Schema: Does not adhere to a rigid schema but is still organized.
• Examples: Emails, XML, JSON files.
• Characteristics: Flexible, hierarchical structure, self-describing, mix of data types.
• Advantages: Flexible, ease of use.
• Disadvantages: Complex, storage issues.

Unstructured Data

• No Fixed Schema: Varied formats such as text, images, videos.
• Examples: Text documents, emails, social media posts, multimedia.
• Characteristics: High volume, rich in information, flexible.
• Advantages: Flexibility, comprehensibility.
• Disadvantages: Complex, requires substantial storage, challenging to process.

Importance of Data in AI

The importance of data spans various sectors:

• Healthcare: Patient data aids in diagnosis and treatment.
• Finance: Analyzes stock markets and financial decisions.
• Manufacturing: Optimizes production processes using sensor data.

Significance of Data in AI

Data provides valuable insights that help improve decision-making. As we harness its potential, its significance continues to grow in many areas including healthcare, finance, and manufacturing.

Characteristics and Examples of Data

Structured Data

• SQL Tables: Clearly defined fields and relationships.
• CSV Files: Easily readable, comma-separated values.

Semi-Structured Data

• XML and JSON: Nested, hierarchical data structures.

Unstructured Data

• Various Formats: Text, images, videos, etc.

Data Analytics: The Next Session

In the next session, we will dive into data analytics, covering descriptive and prescriptive data. This will be crucial in understanding how to manage and interpret the enormous data sets AI generates.

Conclusion

We hope that today's session gave you comprehensive insight into the types of data pertinent to AI. If you still have any questions, please put them in the comments below. Stay tuned for the next session on data analytics, crucial for your AI Associate Certification.

Thank you, Nikita, for such an informative session. Thank you, everyone, for your participation. Stay tuned for updates on full data science courses coming soon.

Keywords

• Data for AI
• Structured Data
• Semi-Structured Data
• Unstructured Data
• Salesforce AI Associate Certification
• Data Analytics
• Data Importance
• Data Types

FAQ

Q: What are the types of data important for AI? A: The three main types of data crucial for AI are structured data, semi-structured data, and unstructured data.

Q: Why is data referred to as the new oil? A: Data is invaluable today because it provides valuable insights that drive better decision-making in various fields.

Q: What is structured data? A: Structured data is highly organized information formatted in tables, rows, and columns, making it easily analyzable.

Q: How is semi-structured data different from structured data? A: Semi-structured data does not adhere to a rigid schema but still has some organizational properties such as tags and markers.

Q: Can you provide examples of unstructured data? A: Examples of unstructured data include text documents, emails, social media posts, multimedia content like images, videos, and audio recordings.

Q: What will be covered in the next session? A: The next session will cover data analytics, including descriptive and prescriptive data analysis, which is essential for managing and interpreting AI-generated data sets.