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Skill Enhancement Course (SEC) – I
Data Analytics – I
Unit – I
Classification of Data
Shambhu Rout
Lecturer in Economics
Rayagada Autonomous College, Rayagada

What is Data?
• Data is a word we hear everywhere nowadays. In general, data is a
collection of facts, information, and statistics and this can be in various
forms such as numbers, text, sound, images, or any other format.
“data is numbers, characters, images, or other method of recording, in a
form which can be assessed to make a determination or decision about a
specific action.”
What is Information?
• Information is data that has been processed , organized, or structured in a
way that makes it meaningful, valuable and useful. It is data that has been
given context , relevance and purpose. It gives knowledge, understanding
and insights that can be used for decision-making , problem-solving,
communication and various other purposes.

Why data is important ?
• Data helps in make better decisions.
• Data helps in solve problems by finding the reason for underperformance.
• Data helps one to evaluate the performance.
• Data helps one improve processes.
• Data helps one understand consumers and the market.

Data Classification :
• Process of classifying data in relevant categories so that it can be used
or applied more efficiently. The classification of data makes it easy for
the user to retrieve it. Data classification holds its importance when
comes to data security and compliance and also to meet different types
of business or personal objective.
Types of Data Classification :
• Data can be broadly classified into 3 types.

1. Structured Data
• Structured data is created using a fixed schema and is maintained in tabular format. The elements
in structured data are addressable for effective analysis. It contains all the data which can be stored
in the SQL database in a tabular format. Today, most of the data is developed and processed in the
simplest way to manage information.
Examples –
• Relational data, Geo-location, credit card numbers, addresses, etc.
• Consider an example for Relational Data like you have to maintain a record of students for a
university like the name of the student, ID of a student, address, and Email of the student. To store
the record of students used the following relational schema and table for the same.
S_ID S_Name S_Address S_Email
1001 A Delhi A@gmail.com
1002 B Mumbai B@gmail.com

2. Unstructured Data
• It is defined as the data in which is not follow a pre-defined standard or you
can say that any does not follow any organized format. This kind of data is also
not fit for the relational database because in the relational database you will see
a pre-defined manner or you can say organized way of data. Unstructured data
is also very important for the big data domain and To manage and store
Unstructured data there are many platforms to handle it like No-SQL Database.
• Examples –
• Word, PDF, text, media logs, etc.

3. Semi-Structured Data
• Semi-structured data is information that does not reside in a relational
database but that have some organizational properties that make it
easier to analyze. With some process, you can store them in a
relational database but is very hard for some kind of semi-structured
data, but semi-structured exist to ease space.
• Example –
• XML data.

Features of Data Classification :
• The main goal of the organization of data is to arrange the data in such a form
that it becomes fairly available to the users. So, it’s basic features as following.
• Homogeneity – The data items in a particular group should be similar to each
other.
• Clarity – There must be no confusion in the positioning of any data item in a
particular group.
• Stability – The data item set must be stable i.e. any investigation should not
affect the same set of classification.
• Elastic – One should be able to change the basis of classification as the
purpose of classification changes.

Types of Data
Generally data is of two type - Qualitative and Quantitative
• Qualitative Data: Data that is represented either in a verbal or
narrative format is qualitative data. These types of data are collected
through focus groups, interviews, opened ended questionnaire items,
and other less structured situations. A simple way to look at
qualitative data is to think of qualitative data in the form of words.
• Quantitative Data: Quantitative data is data that is expressed in
numerical terms, in which the numeric values could be large or small.
numerical values may correspond to a specific category or label.
• Mixed Data: Combination of both numerical and categorical data.

Scale of Measurement
Data can be classified as being on one of four scales: nominal, ordinal,
interval and ratio. Each level of measurement has some important properties
that are useful to know.
Properties of Measurement Scales:
• Identity – Each value on the measurement scale has a unique meaning.
• Magnitude – Values on the measurement scale have an ordered relationship
to one another. That is, some values are larger and some are smaller.
• Equal intervals – Scale units along the scale are equal to one another. For
Example- the difference between 1 and 2 would be equal to the difference
between 11 and 12.
• A minimum value of zero – The scale has a true zero point, below which no
values exist.

1. Nominal Scale –
• Nominal variables can be placed into categories. These don’t have a
numeric value and so cannot be added, subtracted, divided or
multiplied. These also have no order, and nominal scale of
measurement only satisfies the identity property of measurement.
• For example, gender is an example of a variable that is measured on a
nominal scale. Individuals may be classified as “male” or “female”,
but neither value represents more or less “gender” than the other.

2. Ordinal Scale –
• The ordinal scale contains things that you can place in order. It
measures a variable in terms of magnitude, or rank. Ordinal scales tell
us relative order, but give us no information regarding differences
between the categories. The ordinal scale has the property of both
identity and magnitude.
• For example, in a race If Ram takes first and Vidur takes second place,
we do not know competition was close by how many seconds.

3. Interval Scale –
• An interval scale has ordered numbers with meaningful divisions, the
magnitude between the consecutive intervals are equal. Interval scales do not
have a true zero i.e In Celsius 0 degrees does not mean the absence of heat.
• Interval scales have the properties of:
• Identity
• Magnitude
• Equal distance
• For example, temperature on Fahrenheit/Celsius thermometer i.e. 90° are
hotter than 45° and the difference between 10° and 30° are the same as the
difference between 60° degrees and 80°.

4. Ratio Scale –
• The ratio scale of measurement is similar to the interval scale in that it also represents
quantity and has equality of units with one major difference: zero is meaningful (no
numbers exist below the zero). The true zero allows us to know how many times greater
one case is than another. Ratio scales have all of the characteristics of the nominal,
ordinal and interval scales. The simplest example of a ratio scale is the measurement of
length. Having zero length or zero money means that there is no length and no money
but zero temperature is not an absolute zero.
Properties of Ratio Scale:
• Identity
• Magnitude
• Equal distance
• Absolute/true zero
For example, in distance 10 miles is twice as long as 5 mile.

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Data analytics – I: classification of data and measurement scale

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