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Data Science Course Hyd .pdf

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Data scientist's job is entitled as the most “awesome job in the 21​st​ century”. Why
data science is becoming so much demanding nowadays? It is because of the
tremendous increment in the data generation process as well as the data used by
the companies has also enhanced magnificently. The need of the data, scientists is
increasing so much that the company Mc Kinsey declared that by the year 2018,
the demand and supply of the data scientists will have the ratio of 50%. Following
are the tasks which have been performed by the ​data​ scientists.
● Data analysis
● Data Modeling
● Data engineering
Let’s discuss them one by one. But before that include cleaning of data also as it is
one of the major components in any of the processing of the data.
Data cleaning is about finding out the issues in the data and clearing it all.
Like, for example, if there are some missing values in the data or there are
noise coming into the data which is making the data unfit for further use or
will reduce its accuracy, so that should be figured out before it goes under
further processing which is called ​data cleaning​.
It is an important step in the process as in this step a data scientist will
check and analyze the data that how he should plot the data in front of
others. He will try to understand the story, cause of the data. So that it can

be easily communicated and transferred to others. For example, on
facebook data scientists check that if an account is having a minimum 10
friends then it will not be blocked from the company. It is the most time
consuming process as we need to shape our data in a concise form and
data scientist gets juiced up in this process.
It basically varies on the basis of the background you are having like
whether you are a statistician or a modeler. If you consider the case of
statistics then it includes linear algebra, regression, probability which is a
pure mathematics thing. Whereas, in the other case, you will see the pure
theoretical knowledge of the field of data science. After ​cleaning of the
data​ and giving the data a particular shape data, scientists try to do the
predictions by comparing the results either with the similar data or data
resembling the most to the dataset taken. This is the age of machine
learning in which we are having numerous algorithms in a black box, giving
accurate results and data scientists are busy in making the results more
accurate by using the defined algorithms and creating the new one too.
Resource box:
After reading the process and steps mentioned above, one can get an idea
of how the whole data gets processed and used under a variety of algorithms for
predictions. In order to get a correct guidance one should join ​data science​.

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