FDS - Week 4 - Different Types Of Data Examples

As part of week 4 we studied the various types of data i.e Qualitative (Nominal and Ordinal) and Quantitative (Discrete and Continuous).

Trying to identify examples of such data in the various domains,
1. Nominal -> Type of Motorcycles (Cruisers, Sport bikes, Touring bikes, Standard motorcycles, Dirt bikes)
2. Ordinal -> # of valves (2, 4), Compliance (BS4, BS6)

1. Discrete -> Transmission (5 speed, 6 speed)
2. Continuous -> Engine Displacement (197.6 cc, 147.25 cc)



A correction to make here:
Nominal data has categories which do not intersect to one another. For ex: Paint color on cars.
In your example, Sport Bikes and Dirt Bikes can also tend to intersect for some models.


Banking Sector:

Nominal: Payment mode - Cash, Cheque, NEFT, UPI
Ordinal: Customer feedback on services - (Very Poor, Poor,OK, Good, Very Good)

Discrete: Number of savings accounts in a branch
Continuous: Account balance in all the branch accounts


Hmm. I thought they shouldn’t intersect. I wanted to avoid the obvious paint color point.
So in this case how would you classify the type of motorcycle? :thinking:
Was referring to the below site to understand how in real life these segregation/categorization might happen.

Motorcycle, Scooter and SportBikes would be Nominal? I see these are common sections on most automobile manufacturers sites.

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It depends on the context we’re talking about. for instance, different bike brands can be considered nominal.



  1. Nominal -> Account Type (current, Saving), Transaction Type(Credit, Debit), Payment Mode (Cash, Cheque, NEFT)
  2. Ordinal -> Customer Feedback


  1. Discrete -> FD Maturity Period, No of account, No of cards
  2. Continuous -> Account Balance, Interest Rate
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  1. Nominal -> Insurance Type (Life, Home, Medical),
  2. Ordinal -> Premium frequency (Annually, Quarterly, Monthly)


  1. Discrete -> Coverage Period, No of family members covered
  2. Continuous -> Insurance coverage amount, Premium amount
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  1. Nominal -> Subject (Eng, Math, Science …), Student/Teacher Gender
  2. Ordinal -> Student Standard (I, II, … VI, VII, VIII, IX, … ), Student grade (A+,A, B+,…)


  1. Discrete -> No of students in a class, No of teacher in school, Student Age
  2. Continuous -> Student Height, Student mark in percentile.

Sports: Cricket
Qualitative: Nominal

  1. Shots - Cover drive, Pull shot, Flick,Sweep
  2. Wicket - LBW, Bowled,Caught

Qualitative: Ordinal
Format of the Game: T20, One day, Test

Quantitative: Discreet

  1. Number of runs in an over, ODI, T20, Test Match
    1.Number of balls in an over


  1. Bowling speed
  2. Net Run Rate
  3. Bat Speed

Banking, Insurance : Customer Service Rating can be an example for Qualitative Ordinal data type
Education: The syllabus followed (CBSE, ICSE , IB) can be Qualitative Nominal
Agriculture: Soil type can be Qualitative Nominal where as yields, amount of fertilizer, water utilized can be Quantitative Continuous

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Banking domain:
Qualitative data: Customer’s Gender:Male, Female , Others(nominal) and Credit Card Type- Premia> Platinum>Gold (Ordinal) or Risk (High risk customer, low risk customer, medium risk customer)

Quantitative data: Credit Score: 200, 201.3,202.5 and account number, number of fds in the bank 5

Insurance Domain:
Quantitative data: Sum insured and No of years of coverage
Qualitative: Name of the policy and name of the policy holder

Quantitative: Number of schools in the country, literacy rates in each state
Qualitative: Ranking of the schools, Location of the top ten schools


Quantitative: Runs scored by a batsman in a match, Overs bowled by a bowler, Batting average in the last 5 years of a batsman

Qualitative: Ranking of players, Bowling action

Isn’t Transmission(5 speed, 6 speed) data a Ordinal Qualitative data ? For me, these values looks more categorical.


I agree because these are finite set of no. and they have a natural ordering also… moreover the distance between the two of a particular bike can also be fixed…


Healthcare (Clinical Trials):
Qualitative :
Nominal: Therapeutic Area - Breast Cancer, Lung Cancer, Prostate Cancer
Ordinal: Clinical Trial Phase - Phase 1, Phase 2, Phase 3

Quantitative :
Discrete: No. of patients enrolled in a trial, No. of geographies/centers in which the trial is being conducted
Continuous: Trial Endpoint Measures - Overall Response Rate (Proportion of patients whose tumor is reduced by a trial drug), Duration of Response (Length of time that a tumor continues to respond to a trial drug without lthe cancer growing or spreading)emphasized text

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Hi @BanuPrakashReddy,
Sorry, took some time to get to revision :slight_smile:
Tranmission - they do look ordinal at first. But as the Professor explains (at 14:01 in the video) the distance between the various options like 4 speed, 5 speed or higher is constant i.e. 1 Based on that understanding I feel these would be discrete.

Hi @Pankaj_Rana, If the distance is fixed then they would ideally fall under discrete, correct?

@Shyamal Discrete is an attribute with a finite value which makes it different from Continuous. However, the no. of values can be infinite. Moreover, there is no concept of distance in discrete attribute. For example no. of student passing out per year, the value is off course discreet, and there can n no. of students every year, however, there is no concept of distance between these values. The distance is fixed in ordinal type data.

Hi @Pankaj_Rana, My response was regarding the transmission not being an ordinal type. Referring to the Professor at 14:06 where he explains that the concept of distance in Ordinal (Qualitative) attributes is not defined. Though the numerical difference between 1 star, 2 star and 3 star is just 1, the distance is not same when taken in context of Very Poor, Poor and Ok. Hence I classified Transmission as discrete and not ordinal.

Lets see what others have to comment on this thread!!! :thinking:

I think it would be continuous more that discrete.