Data Analytics: What It Is & How It’s Used

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Data Analytics What It Is How Its Used
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What Is Data Analytics

• Data analytics is the study of examining raw data
  to gain insights about that data.
• A business can boost productivity, maximize
  profit, or make more strategically sound
  decisions with the use of data analytics.
• In order to act on raw data for human
  consumption, data analytics methodologies and
  procedures have been mechanized into mechanical
  operations and algorithms.
• Data analytics can be used to examine a variety
  of topics, including what occurred, why it
  occurred, what will occur, what should be done
  next, or all of the above (descriptive,
  diagnostic, and predictive analytics)
  (prescriptive analytics).
• Data analytics uses a variety of software tools,
  such as spreadsheets, data visualisation and
  reporting tools, data mining applications, or
  open-source languages, for the most thorough data
  manipulation.
• The term "data analytics" is general and
  encompasses a variety of data analysis methods.
  Any sort of information can be subjected to data
  analytics techniques to obtain insight that can
  be used to improve things.

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Data Analysis Steps

• Data analysis is the most important steps which
  involves various different phases, including
• The first step is to identify the data
  requirements or how the data is gathered. Data
  may be broken down depending on age, gender,
  income, or other criteria. Both qualitative and
  numerical data values are possible.
• The second stage of data analytics is the data
  collection procedure. To do this, a variety of
  resources can be used, including computers, the
  internet, cameras, environmental sources, and
  human workers.
• After it has been collected, it must first be
  organized so that it may be studied. On a
  spreadsheet or other piece of software that can
  handle statistical data, this could happen.
• The data is then cleaned up for analysis. This
  means that it has been edited and checked twice
  to make sure there are no errors or missing
  pieces of information. This step helps to fix any
  errors before the data is given to a data analyst
  for analysis.
• Analyzing and altering the data is one of the
  final processes in the data analysis process.
  There are numerous ways to accomplish this such
  as data mining, data visualization etc..

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Types Of Data Analytics

• There are four main categories of data analytics.
  They are
• Descriptive Analytics This summarizes what has
  occurred over a specific time frame. Has there
  been an increase in views? Are sales this month
  better than last?
• Diagnostic Analytics This is more focused on the
  causes of occurrences. For this, more different
  data inputs are required, coupled with some
  supposition. The weather had an impact on beer
  sales. Has the most recent marketing campaign had
  an impact on sales?
• Predictive Analytics Let's now discuss what is
  most likely to happen soon. When did we last
  experience a sweltering summer? Why did sales
  decline? How many weather predictions predict a
  hot summer?
• Prescriptive Analytics This implies an approach
  to take. If the average of these five weather
  models predicts a hot summer and it is above 58,
  we should hire a second tank and add an evening
  shift to the brewery to improve production.

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Data Analysis Techniques

• Data analysts may use a range of analytical
  techniques and procedures to analyze data and
  extract information. Some of the more popular
  methods are listed here.
• Regression Analysis entails looking at how the
  dependent variables are connected to determine
  how changing one can affect changing the other.
• Factor Analysis includes shrinking a substantial
  data set to a manageable one. It is intended that
  by utilising this method, tendencies may be found
  that might be more challenging to notice in the
  past.
• Cohort Analysis is the division of a data set
  into sets of related data, frequently divided
  into a consumer demographic. This enables data
  analysts and other data analytics users to go
  deeper into the statistics pertaining to a
  certain subset of data.
• Time Series Analysis collects data through time
  and establishes a link between the importance of
  a data point and its occurrence. This method of
  data analysis is frequently employed to identify
  cyclical patterns or to forecast financial
  outcomes.

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