AI Basics with AK

Season 03 - Introduction to Statistics

Arun Koundinya Parasa

Episode 02

Types of Data: The Foundation of Statistics 🌊

Agenda

  • What are Data Types?
  • Categorical vs Numerical Data
  • Structured vs Unstructured Data
  • Why Data Type Matters in Analysis
  • Real-Life Examples & Visual Demo
  • Key Takeaways

What Are Data Types?

Data is the raw material for statistics and AI.

  • Different types of data require different tools.
  • Understanding data types helps us choose the right methods.
  • Think of data types as the language of information. 🧠 Imagine data types as different kinds of ingredients in a kitchen.

Categorical vs Numerical Data

Categorical Data

  • Represents categories or groups.
  • Examples: Colors (Red, Blue), Types of fruit, Yes/No answers.
  • Can be nominal (no order) or ordinal (with order). Like sorting fruits by color or type.

Numerical Data

  • Represents quantities or measurements.
  • Examples: Height, Age, Temperature, Scores.
  • Can be discrete (countable, e.g., number of apples) or continuous (any value, e.g., weight). Like measuring the length of a table.

Structured vs Unstructured Data

Structured Data

  • Organized in rows and columns.
  • Easy to store and analyze.
  • Examples: Excel spreadsheets, databases. Like a neatly arranged filing cabinet.

Unstructured Data

  • Not organized in a predefined way.
  • Harder to analyze directly.
  • Examples: Images, text, videos, audio. Like a messy desk with papers everywhere.

Why Data Type Matters in Analysis

  • Determines which statistical methods apply.
  • Impacts data visualization choices.
  • Influences machine learning algorithms. 📌 Choosing the wrong approach can lead to wrong conclusions.

Real-Life Examples

Data Type Example Use in AI/Stats
Categorical Customer Gender (Male/Female) Grouping, classification
Numerical Daily Temperature (°C) Trend analysis, forecasting
Structured Sales Data in Excel Easy querying and statistics
Unstructured Customer Reviews (Text) Sentiment analysis, NLP

Identifying Data Types

🧠 Try classifying items around you by these types!

Feature Sample Values Data Type
Favorite Color Red, Blue, Green Categorical
Age 25, 30, 22, 28 Numerical
Customer Feedback “Great service”, “Too slow” Unstructured
Sales Records Rows of date, amount, product Structured

Key Takeaways

  • Data types are the building blocks of analysis.
  • Categorical vs Numerical: groups vs quantities.
  • Structured data is organized; unstructured is raw and complex.
  • Knowing your data type helps you make smarter decisions. ## ✨ Coming Up Next

Next Episode 03: Descriptive Statistics

🔹 Mean, Median, Mode

🔹 Range, Variance, Standard Deviation

🔹 Summarizing Data Effectively

Stay curious! 🧠✨

Thank You