Data and Statistics, Data Mining, Data & Variables, Data Analysis, etc.

2. DATA & STATISTICS
• Data is individual pieces of factual information recorded and used for the purpose
of analysis. It is the raw information from which statistics are created.
• Statistics are the results of data analysis - its interpretation and presentation. In
other words some computation has taken place that provides some understanding
of what the data means. Statistics are often presented in the form of a table, chart,
or graph to provide better view of information.

3. Data Mining and Data Analyzing
Data Mining
• Exploration of data to get some result
• Trial and error
• Uncertainty
Data Analyzing
• Objective
• Measurement
• Test to be applied

4. Data & Variables
Data refers to a set of values,
which are usually organized by
variables and observational units
and Variables are of different
types and can be classified in
many ways.
Numerical and Categorical.
Variable
• A variable is defined as
anything that has a quantity
or quality that varies.
• A variable is an attribute
that describes a person,
place, thing or idea.
• It may change from group
to group, person to person,
or even within one person.

5. Types of Variables
Quantitative/Metric/Numerical
• It deals with numbers and things
you can measure objectively:
dimensions such as height, width,
and length. Temperature and
humidity. Prices. Area and
volume.
Qualitative/Non-Metric/Categorical
• It deals with characteristics and
descriptors that can't be easily
measured, but can be observed
subjectively—such as smells, tastes,
textures, attractiveness, and color.

6. Quantitative Variables
Discrete
• It is a count that can't be made
more precise. Typically it involves
integers. For instance, the number
of children (or adults, or pets) in
your family is discrete data,
because you are counting whole,
indivisible entities: you can't have
2.5 kids, or 1.3 pets.
Continuous
• It could be divided and reduced to
finer and finer levels. For example,
you can measure the height of your
kids at progressively more precise
scales—meters, centimeters,
millimeters, and beyond—so height is
continuous data.

7. Qualitative Variables
Nominal
• Nominal variable is defined as data
that is used for naming or labelling
variables, without any quantitative
value.
• Nominal data can be both qualitative
and quantitative. However, the
quantitative labels lack a numerical
value or relationship (e.g.,
identification number).
• Names of people, gender, and
nationality are just a few of the most
common examples of nominal
variables.
Ordinal
• In Ordinal variable the values follow a
natural order. One of the most notable
features of ordinal variable is that the
differences between the variable
values cannot be determined or are
meaningless.
• The Likert scale that you may find in
many surveys is one example. The
Likert scale lists the categories of the
psychometric scale such as “Strongly
Agree,” “Agree,” etc.

8. Ready for a quiz?

9. Scales
Nominal Scale
Ordinal Scale
Interval Scale
Ratio Scale
Scales of Measurement

10. How likely you will
recommend our
services to your
friends?
Very Likely
Likely
Neutral
Unlikely
Very Unlikely
A) Nominal
B) Ordinal
C) Interval
D) Ratio

11. What color
hair do you
have?
Brown
Black
White
Pink
A) Nominal
B) Ordinal
C) Interval
D) Ratio

12. What's your
age?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
…………….

13. What's your
nationality?
American
Indian
German
Japanese
A) Nominal
B) Ordinal
C) Interval
D) Ratio

14. What's your
monthly income?
0 - 50000
50000 - 100000
100000 - 150000
150000 - 200000
A) Nominal
B) Ordinal
C) Interval
D) Ratio

15. THANKS