Explain about making sense of data and types of dataset in detail.



 

MAKING SENSE OF DATA – TOPICS

  1. Managing Data

  2. Separating Different Types of Data

  3. Organizing Data

  4. Presenting Data for Analysis

  5. Purpose of Making Sense of Data

TYPES OF DATASET – TOPICS

1. Numerical Data

  • A. Discrete Data

  • B. Continuous Data

2. Categorical Data

  • A. Binary Categorical Variable

  • B. Polytomous Variable

MEASUREMENT SCALES – TOPICS

  1. Nominal Scale

  2. Ordinal Scale

Making sense of data is the process of organizing, arranging, and presenting the collected data into a clear and viewable format such as tables, charts, and graphs, so that patterns, trends, and meaningful insights can be easily identified for accurate decision-making.

2. Numerical Data (Quantitative Data)

Numerical data is data that represents measurable quantities and consists of numeric values such as age, height, weight, and temperature.

A. Discrete Data

Discrete data is numerical data that is countable and can take only fixed, distinct values with no intermediate values between them.

B. Continuous Data

Continuous data is numerical data that can take an infinite number of values within a specific range and can be measured on interval or ratio scales.

3. Categorical Data (Qualitative Data)

Categorical data is data that represents qualities or characteristics and categorizes objects into groups such as gender, marital status, or movie genre.

A. Binary Categorical Variable

A binary categorical variable is a variable that can take exactly two possible values, such as success/failure or yes/no.

B. Polytomous Variable

A polytomous variable is a categorical variable that can take more than two possible values such as marital status (married, widowed, unmarried).

4. Nominal Scale

A nominal scale is a measurement scale that assigns numbers or labels only for naming or identifying objects, without implying order or arithmetic meaning.

5. Ordinal Scale

An ordinal scale is a measurement scale that establishes order or rank among variables, but does not specify the exact magnitude of difference between the ranks.




MAKING SENSE OF DATA

Making sense of data is the process of organizing, preparing, and understanding collected data so meaningful insights can be derived. Before analysis, data must be arranged in a presentable format using tables, charts, and graphs to observe patterns and trends clearly.

Key Steps in Making Sense of Data

• Managing Data

  • Organize data so analysis can start smoothly.

  • This includes collecting, listing, and arranging data in a proper system.

  • Proper filing, sorting, and classification help in quick access.

• Separating Different Types of Data

  • Separate data based on type (numerical or categorical).

  • Classification helps handle data efficiently and avoids confusion.

• Organizing Data

  • Organize data chronologically, topic-wise, or document-wise for faster access.

  • Maintain copies properly and track all updates.

• Presenting Data for Analysis

  • Convert data into viewable formats like tables, charts, and graphs.

  • Visual presentation helps identify patterns, trends, and anomalies easily.

• Purpose

  • The purpose of making sense of data is to prepare raw data for meaningful interpretation, ensure clarity, and support accurate decision-making.

TYPES OF DATASET

Datasets in EDA are categorized into Numerical Data and Categorical Data.

Numerical Data (Quantitative Data)

Numerical data involves measurement and contains numerical values.
Examples: age, height, weight, blood pressure, temperature.

Numerical data types:

A. Discrete Data

  • Countable values with fixed distinct numbers.

  • Examples: number of heads in coin flips, rank of students, number of family members.

B. Continuous Data

  • Values can take infinite numbers within a range and can be measured on interval or ratio scales.

  • Examples: temperature, weight, height.

Categorical Data (Qualitative Data)

Represents characteristics, labels, or categories.
Examples: gender, marital status, blood type, movie genre.

Categorical variable types:

A. Binary Categorical Variable

  • Contains two possible values.

  • Example: success/failure.

B. Polytomous Variable

  • Contains more than two values.

  • Example: marital status (married, unmarried, widowed).

Measurement Scales for Categorical Data

Nominal Scale

  • Used for labeling or naming without order or arithmetic meaning.

  • Examples: languages, blood group, movie categories.

Ordinal Scale

  • Shows rank or order but not the magnitude of difference.

  • Examples: Likert scale (Strongly Agree → Strongly Disagree).



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