Where is data
analytics heading in 2021? It’s reckoned that data analytics will hit through
the roof with thousands of projects underway at the top data analytics course in Bangalore.
Data is part of our lives in more ways
than we can ever think of. We are producing data at a massive volume, which
leads many analysts to believe that without data, we will extinct. The data
that exists in our life controls what we see and what we interact with on our
gadgets. According to a recent report, globally we will produce close to 80 zettabytes
of data in 2021, which will double its rate of production by 2024 -- something
that can only be called astronomical in the modern context. However, it’s
important to understand how data works and how various techniques and
approaches influence the data analytics lifecycle.
In this article, we will discuss the data
analytics lifecycle and how this influences the final outcomes of any Big Data
project.
But,
first what is the data analytics lifecycle?
Data Analytics is the scientific
approach to Big Data projects that represent or simulate the actual parts of a project.
These address the various aspects of data collection, management, analysis, and
repurposing of data at various levels of operation.
Here are the various steps that we
will discuss here:
Data
Discovery
Data discovery is an important and
fastest emerging Business Intelligence component. It helps BI teams to automatically
prepare and organize enterprise data in a meaningful manner.
Data Discovery is often confused for a
tool, rather than a process. Actually, data discovery entails the use of
various analytical tools that iteratively work to guide a data analytics
process. From augmenting data to automatically understanding patterns, data
discovery is critical to be successful with any modern
data analytics course in Bangalore.
Data
Preparation
Prior to utilizing data for analytics
in advanced modules, it is important to clean the raw data for meaningful
context. Data preparation involves cleaning up raw data and combing it to
extract patterns before they are actually analyzed using tools.
There are many data preparation tools
that help BI analysts to discover, process, blend, refine, enrich and transform
data. A good data preparation tool would ensure that data cleansing yields
better analytical results in the long run.
Model
Planning
After preparation comes to another
stage -- model planning which involves learning from a data analytics course
related to establishing a relationship between variables and constants.
BI teams are engrossed in training, testing,
and production purposes using advanced tools like MATLAB and Statistica. The next
step involves Model Building which provides the BI team with adequate insights
into how models will run the various advanced stages of data management and
operationalize results based on requirements.
Data
Communication and Operationalization
When you work in advanced analytics,
it’s important to coordinate the process with other team members and managers.
Using tools like WEKA, SQL, and Octave, you can influence the operations for a workbench,
especially if you are building open-source AI ML platform.
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