We all know
what data is. It’s the information we exchange, the data we process and convert
to something meaningful. Data science is performing meaningful operations that
make it function in different ways. According to recent estimations, the demand
for data science is expected to grow to 40%. In India, there’s only 10% working
in it. There is a need for more professionals in this industry, which is why we
are focusing our attention on this topic. We might be struggling to manage
small amounts of data at times.
We might not be able to focus on our data carefully due to our busy schedules. Figuring out how the data must be managed takes a lot of time which is why we will now discuss a basic methodology to work on data.
We might not be able to focus on our data carefully due to our busy schedules. Figuring out how the data must be managed takes a lot of time which is why we will now discuss a basic methodology to work on data.
Data Science Training in Pune |
1. Frame your question wisely: let me give you an example. Consider the voting system database. You
may want to extract 2 particular cities out of the national database system.
When you need to do this, there are two possible ways to accomplish the task.
ü What is the voting ratio between the
western coast and the eastern coast?
ü What is the ratio in Maharashtra and
Orissa?
Both
of these questions, request the same kind of data, but when you have to know
about one particular place or a category in general, you must specify the exact
category from which you want the data to be collected. The first question has
to move through the entire east and west coasts which takes time and is quite a
load on a CPU. Whereas the second question focuses on the states of Maharashtra
and Orissa. This allows for faster processing and reduces time complexity.
2. Read your data clearly: after we
extract data, we cannot be certain that the data is in the format we need. It
is usually in a messy format, with a lot of junk, which is probably not what is
required. So, even before we check on the actual data we’re looking for, we
need to perform some data cleaning. This process removes the junk and then you
can actually provide a format which will allow you to view the data as needed.
3. Check the data: after you receive data
that has been cleaned and formatted, you should check the details in it.
Details as in rows, columns, number of lines, etc.
4. Check the margins of data: in order to
make sure that the data is safe and can be read or scanned anywhere, you need
to check the data from top to bottom. This also helps to retrieve and analyze
data for some operations.
5. Check the updates that are occurring: this is used to learn the
various operations that were performed previously on that data for faster
access. When you know every version of the data, you will know what else can be
done and be able to perform better operations on them.
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As you can
see, there are many factors to be considered while dealing with data and there
are many kinds of data coming in from everywhere and you need to work on them.
This can become a good career option for you because it is a never-ending
process. Excelr provides data science Training in Pune for you. Welcome, cherish and grow.
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