How to Carrying out data analysis

1. TheAnalysis
How To Carry Out
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2. What Is Data Analysis?
Data analysis is the process of collecting, modeling, and analyzing data to extract
insights that support decision-making.
Data analysis is a central activity in applied epidemiology providing underlying evidence for
public health policy formulation and action.
Data may come from studies with survey, cohort or case control designs, or from health
system surveillance or monitoring.

3. Public health professionals or other professionals use
data to understand how different factors (predictors) may
be associated with health outcomes of interest. Data
analyses are also used to determine the extent to which
different interventions may be effective.
Why Is Data Analysis
Important?

4. CATEGORIES OF ANALYSIS
Descriptive analysis - What happened.
Starting point to any analytic process, and it
aims to answer the question of what
happened?
Main aim of the exploratory analysis is to explore. Prior to it,
there's still no notion of the relationship between the data and
the variables.
Exploratory analysis - Exploring data relationships.
Diagnostic analysis - Why it happened.
Diagnostic data analytics empowers analysts by helping them
gain a firm contextual understanding of why something
happened.

5. CATEGORIES OF ANALYSIS
Predictive analysis - What will happen.
Uses the results of the previously mentioned
descriptive, exploratory, and diagnostic analysis,
in addition to machine learning (ML) and
artificial intelligence (AI). Like this, you can
uncover future trends.
Prescriptive data techniques cross over from predictive
analysis in the way that it revolves around using
patterns or trends to develop responsive, practical
medical strategies.
Prescriptive analysis - How will it happen.

6. 6
Cluster analysis
01
Regression analysis
02 03
Cohort analysis
7 ESSENTIAL TYPES OF DATA ANALYSIS
The action of grouping a set
of data elements in a way
that said elements are more
similar (in a particular
sense) to each other than to
those in other groups.
This type of data analysis
method uses historical data
to examine and compare a
determined segment of users'
behavior. A useful tool to
start performing cohort
analysis method is Google
Analytics.
The regression analysis uses
historical data to understand how
a dependent variable's value is
affected when one (linear
regression) or more independent
variables (multiple regression)
change or stay the same.
The Linear Regression Tool in
EXCEL creates a simple model to
estimate values, or evaluate
relationships between variables
based on a linear relationship.

7. 7
Neural networks
04 05
Factor analysis
7 ESSENTIAL TYPES OF DATA ANALYSIS
The neural network forms the
basis for the intelligent algorithms
of machine learning. There are BI
reporting tools that have this
feature implemented within them,
such as the Predictive Analytics
Tool from DATAPINE.
The factor analysis, also called
“dimension reduction,” is a type of data
analysis used to describe variability
among observed, correlated variables in
terms of a potentially lower number of
unobserved variables called factors.
Available in Excel with the XLSTAT
statistical software.
Part 2

8. 8
Data mining
06 07
Text Analysis
7 ESSENTIAL TYPES OF DATA ANALYSIS
A method of analysis that is the umbrella
term for engineering metrics and insights
for additional value, direction, and
context. An excellent example of data
mining is DATAPINE intelligent data
alerts. With the help of artificial
intelligence and machine learning, they
provide automated signals based on
particular commands or occurrences
within a dataset.
Text analysis, also known in the
industry as text mining, is the process
of taking large sets of textual data and
arranging it in a way that makes it
easier to manage. Aylien is equipped
with Natural Language Processing
(NLP) used to analyze all types of text,
from survey responses and emails to
tweets and product reviews.
Part 3

9. 1.Collaborate
your needs.
2.Establish your
questions.
3.Data
democratization
4.Clean your
data.
HOW TO PERFORM YOUR
ANALYSIS

10. 5.Set your KPIs. 6.Omit useless data. 7.Build a data
management
roadmap.
8.Integrate
technology.
HOW TO PERFORM YOUR
ANALYSIS
Part 2

11. 9.Answer your
questions.
10.Visualize your
data.
11.Interpretation
of data.
12.Build a narrative.
HOW TO PERFORM YOUR
ANALYSIS
Part 3

12. 13.Consider
autonomous
technology.
14.Share the load. 15. Data analysis tools.
HOW TO PERFORM YOUR
ANALYSIS

13. CONCLUSIONS
We’ve pondered the data analysis meaning and drilled down
into the practical applications of data-centric analytics, and
one thing is clear: by taking measures to arrange your data and
making your metrics work for you, it’s possible to transform
raw information into action - the kind of that will push your
research to the next level.

14. THANK YOU!