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Defining Data Analysis | Modern Data Ecosystem|Data Analytics Lecture 4 | Data Analytics 2022 | Anurag | YouTubeToText
YouTube Transcript: Defining Data Analysis | Modern Data Ecosystem|Data Analytics Lecture 4 | Data Analytics 2022
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analysis is the process of gathering
cleaning analyzing and mining data
interpreting results and reporting the findings
findings
with data analysis we find patterns
within data and correlations between
different data points and it is through
these patterns and correlations that
insights are generated and conclusions
are drawn
data analysis helps businesses
understand their past performance and
informs their decision making for future actions
actions
using data analysis businesses can
validate a course of action before
committing to it saving valuable time
and resources and also ensuring greater success
success
we'll explore four primary types of data
analysis each with a different goal and
place in the data analysis process
descriptive analytics helps answer
questions about what happened over a
given period of time by summarizing past
data and presenting the findings to stakeholders
stakeholders
it helps provide essential insights into
past events
for example tracking past performance
based on the organization's key
performance indicators or cash flow analysis
analysis
diagnostic analytics helps answer the
question why did it happen
it takes the insights from descriptive
analytics to dig deeper to find the
cause of the outcome for example a
sudden change in traffic to a website
without an obvious cause or an increase
in sales in a region where there has
been no change in marketing predictive
analytics helps answer the question what
will happen next historical data and
trends are used to predict future
outcomes some of the areas in which
businesses apply predictive analysis are
risk assessment and sales forecasts it's
important to note that the purpose of
predictive analytics is not to say what
will happen in future its objective is
to forecast what might happen in the
future all predictions are probabilistic
in nature
prescriptive analytics helps answer the
question what should be done about it
by analyzing past decisions and events
the likelihood of different outcomes is
estimated on the basis of which a course
of action is decided
self-driving cars are a good example of
prescriptive analytics they analyze the
environment to make decisions regarding
speed changing lanes which route to take
etc or airlines automatically adjusting
ticket prices based on customer demand
gas prices the weather or traffic on
connecting routes now let's look at some
of the key steps in any data analysis process
process
understanding the problem and desired
result data analysis begins with
understanding the problem that needs to
be solved and the desired outcome that
needs to be achieved where you are and
where you want to be needs to be clearly
defined before the analysis process can begin
begin
setting a clear metric
this stage of the process includes
deciding what will be measured for
example number of product x sold in a
region and how it will be measured for
example in a quarter or during a
festival season gathering data once you
know what you're going to measure and
how you're going to measure it you
identify the data you require the data
sources you need to pull this data from
and the best tools for the job
cleaning data having gathered the data
the next step is to fix quality issues
in the data that could affect the
accuracy of the analysis
this is a critical step because the
accuracy of the analysis can only be
ensured if the data is clean
you will clean the data for missing or
incomplete values and outliers for
example a customer demographics data in
which the age field has a value of 150
is an outlier you will also standardize
the data coming in from multiple sources
analyzing and mining data
once the data is clean you will extract
and analyze the data from different
perspectives you may need to manipulate
your data in several different ways to
understand the trends identify
correlations and find patterns and variations
variations
interpreting results
after analyzing your data and possibly
conducting further research which can be
an iterative loop it's time to interpret
your results
as you interpret your results you need
to evaluate if your analysis is
defendable against objections and if
there are any limitations or
circumstances under which your analysis
may not hold true
presenting your findings
ultimately the goal of any analysis is
to impact decision making the ability to
communicate and present your findings in
clear and impactful ways is as important
a part of the data analysis process as
is the analysis itself reports
dashboards charts graphs maps case
studies are just some of the ways in
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