Get to know more about Prescriptive, Predictive analytics like market basket analytics plus the data structure and variables to apply the analytics. for more info you can ping me at google #bobrupakroy

1. Types
Of analytics

2. There are three types of analysis that really
support a business.
 Prescriptive – This type of analysis reveals what actions should be
taken. This is the most valuable kind of analysis and usually results in
rules and recommendations for the next steps.
 For example: If you have a flood data then you analyze the data,
why? and what? are the reasons of floods and the losses related .
Assume that the data says its the drainage systems that is already
jam or block, we can actually fix them and prevent the over flood
from happening next time by analyzing and providing a conclusion
and/or a recommendation
 Predictive – It is an analysis of likely or similar scenarios of what
might happen. The outcomes are usually a predictive forecast.
 For example: Flood data it simply tells you what will be the
outcome if the flood continues the same, or contrary that will be a
probability to be damage by flood data after taking the
necessarily preventive steps.
Rupak Roy

3.  Descriptive – it is what is happening now
by giving you a quick peek of the new
incoming data to be able to describe it.
 For example: The frequency of median ,
maximum, minimum of the flood data will
simply describes the summary of the
data.
Rupak Roy

4. Applicable examples of predictive analytics:
If a person is buying a particular product then
this person is likely to buy another product
related to that particular product.
This is often used in retail markets which is also
known as, market basket analysis , allowing to
make predictions on probabilities for a
particular product and related to other similar
products.
Rupak Roy

5. Market Basket Analysis
Is a modeling technique based on the
theory that if you buy a certain group of
items, you are more (or less) likely to buy
another group of items. For example,
people who buy flour and casting sugar,
also tend to buy eggs because a high
chance or probability of them that they are
planning to bake a cake.
Rupak Roy

6. Prescriptive Analytics
It is a new kind of analytics that has raised
where it describes, predicts and
recommends what to do.
Prescriptive analytics is also the third and
the final phase of analytics related to both
descriptive analytics and
predictive analytics.
Rupak Roy

7. The Structures of
Data and Variables

8. The Structure of Data
Data can be found in any form and
anything that can be digitized can be
analyzed.
Rupak Roy

9. The data Structure
is organize in a typical
tabular design, data is
made up of rows and
columns .
Each row represents a
record and
observation in the
data and each
column represents
the field of
information.
Each data in the
column represents a
variable .

10. What is a variable ?
“A specific piece of information about an
observation or record in a data set “
Rupak Roy

11. Types of Variables
1. Categorical variable: are the variables
that can be categorized. For example
male/female or good/bad.
2. Continuous variable: are the infinite
number of values that can be collected
for example weight and height or in a
mathematical setting the definition of
infinite number values that can be
generate between 0 to 1 for example
0.01,0.001,0.000001… to infinity.

12. Types of Variables
3. Discrete Variable: is a variable that can
only have certain number of values.
For example: The number of cars in a
parking lot is discrete because a parking lot
can’t only hold so many cars.
4. Independent variable: is a variable that
is not affected by anything or by any other
variables .
Rupak Roy

13. Types of Variables
5. Dependent variable: is a contrary to independent
variable that is reliant on other variables.
Dependent variable vs. Independent variables:
In the direction to determine by: How long a student
sleeps affects test scores?.
The independent variable is the length of time spent
sleeping (since test scores have no effect on the time
spent sleeping)
The dependent variable is the test score(as the time
spent sleeping has affected the test score ).

14. Types of Variables
6. Nominal variables: It is an another name for categorical
variable that holds more than two categories, such as red
/green /blue cars.
7. Ordinal variables: It is similar to a categorical variables, but
there is a clear order. For example: The ranks of income
levels in order from low/medium/high.
8. Dummy variables: It is used in regression analysis when you
want to assign relationships to unconnected categorical
variables.
 For example categories like young, middle age and old age,
can be expanded into 3 dummy each describing its
relationships to each other, for instance assign 1 to a variable
if the age is young else 0, the same can be repeated for
another dummy variable if the age is middle than mark it as 1
else 0 and again for the 3rd dummy variable if age is old
assign 1 else 0

15. Types of Variables
9) Indicator variable: It is another way to define
a dummy variable .
10) Binary variable: It is a variable that can have
two values usually 1/0 or yes/no.
11) Derived Variables: Are the variable that are
originates from other variables when
combining individual variables in to a whole
new variable.
For example:(radio + TV ) = Media
Rupak Roy

16.  Next summary statistics and their types
with Skewness and Kurtosis .

17.  To be continued…