Introduction to the world of data:
Well as we already know it, big data
has become one of the largest components in the world of technology that neighbors
us today and thanks to all the analytics that Big Data provides us. We can
pretty much gain insights from the data and this can result in businesses which
can be superior, which can understand how the data works and then use it to its
betterment. Well if you already know the amount of data that surrounds us, we
have petabytes and petabytes of data being put out by these companies and even
they would require the same. Twitter, Facebook, Instagram and YouTube are the
companies who use a huge amount of data stored, so this should bring us to the
question of what Data Science is.
Introduction to Data Science:
Data Science is a vast field which is
focused on basically finding some actionable perceptions from a large set of
raw data, large sets of unstructured data and structured data as well you might
be wondering what the objective for Data Scientists is. A Data Scientist’s main
goal is to ask questions, locate wherever he can make use of the data and know
where the data can be put forth to answer specific questions using the data,
add emphasis and find the right questions that he needs to ask towards the data.
Data Scientists are concerned with all the data that moves into their hand from
the data engineers who engineer all the data and bring in the data to the form
and these Data Scientists pretty much go on examining the data most of the
time. It's performing some sort of computations, analysis and
eventually it involves applying machine learning, artificial intelligence or
any of these very big concepts to make sense of the data and later this is
passed onto the Data Analytics team.
Introduction to Data Analytics:
Data Analytics actually is, well as it
is mentioned, data engineers drive the data into the company, Data Scientists
work with the data to get it to a stage where Data Scientists take the raw data
and convert it into information and this information is used by Data Analysts
who pretty much go on processing this data and performing a lot of statistical
analysis on all the existing data sets which are provided by the Data Scientist
or they may pick up something new and perform some analysis on the same. But
then analysts also concentrate on creating methods where our data can be
visualized because at the end of the day the data is just numbers so seeing
just numbers might not make sense at all times. So converting these numbers
into very good looking visuals, graphs and driving meaningful insights from
there to drive the business basically to success is the job of a Data Analyst.
So the Data Analyst will find out all the current problems and establishes the
best way that they deem fit to solve the problem.
Data Science vs. Data Analytics:
What is the difference between Data
Science and Data Analytics?
1) Definition and major difference: Well the first main thing that you need to
know is that while many people use it together there is a very keen difference
because the main difference is the scope in which data science spreads its
wings in and Data Analytics is good and so we can consider Data Analytics to be
a part of Data Science. So Data Science is the umbrella and the Data Analytics
is a part of that. Data Science is a complete term for a group of fields which
is basically used to mine large data sets, which basically means that we'll be
looking at hunting into large data sets and driving meaningful insights from
them. But at the same time, Data Analytics is a more focused version which is
considered to be just a larger part of the entire process because at the end of
it, all the data collected by the Data Scientists can be used by the Data
Analytics team to go about doing visualizations, analyzing trends and so much
more.
2) Aim: Data
Science isn't concerned with answering any specific queries with respect to a
specific set of data. It works in a very wide way, but when you talk about Data
Analytics, Data Analytics works extremely well when you have a small scope to
work with, when it is focused and you know the questions which need the answer
to. And these questions and the answers that you can derive from these
questions are basically from the existing data which is already present. So
this is another simple difference between Data Science and Data Analytics.
3) Working: Data
Analytics is all about the present data. But Data Science concentrates on the
questions which should be asked based on the past data where you can mine the
data and make sense of it. This is the Data Sciences team but then coming to
Data Analytics, here we basically emphasize and drive into discovering answers
to questions that are being asked at that very moment, or to produce a trend or
an analytics for the future case.
4) Applications: Data
Science in majority of the fields in today's world is used in machine learning;
it is used to achieve artificial intelligence. Search engine engineering,
corporate analytics is used in the field of medicine as well it is used in the
field of manufacturing and so much more. Data Science has its foot deep in
today's world of technology. And coming to Data Analytics, it has been a boon
to the field of healthcare, gaming, travel, a lot of other industries. Again,
manufacturing industries where the data requirement is immediate and the
analysis and the analytics which goes with it is also immediate.
5) Skills: Let’s quickly check out all the skills of a Data Scientist and a Data Analyst. Basically a Data Scientist as we have discussed, works with all the data, he or she gets and applies a lot of mathematics on it, applies a lot of statistics on it. The primary skill that a Data Scientist requires is having a strong knowledge of languages such as Python, R, SAS, Scala. Then the second thing is knowing how the person can work with unstructured data because at the end of the day a Data Scientist can be working with images, can be working with videos, can be working with music or text data and then this brings us to some back into development where the person as the Data Scientist needs to have some knowledge over back-end development with respect to databases, data warehousing, handling data on the backend and making the backend talk to the front end. And this brings us to the fourth skill which is having the knowledge of machine learning and sometimes deep learning as well because all of these concepts achieve artificial intelligence and make the data a little smarter. This brings us to the skills that make a Data Analyst. When a Data Analyst works with the data, he or she after all creates visuals out of the data and gives us future trends, predictions. Having the knowledge of statistics is one of the most vital skill for a Data Analyst but then having very good statistical skills without implementing it in the world of computer science would not make sense. Hence a Data Analyst would also be benefited if he or she knew a bit of programming in terms of Python or language such as R. Then coming to data wrangling, we know how data can be so much messy especially if it's unstructured or data can be a very unruly entity. So data wrangling is again basically like picking up all the data that you particularly require in a hunt, it's like hunting for a needle in a haystack. A good Data Analyst will have very good data wrangling skills. A Data Analyst should also be skilled with data visualization soft-wares.
6) Average salaries: So now you might be wondering what the average salary is for a Data Scientist and for a Data Analyst. Well a Data Scientist gets an average pay of 120 thousand dollars in the United States and over 15 lakhs per annum. Then for a Data Analyst it's around 90 thousand American dollars in USA. Guys these numbers have been squished bound because of the lower threshold and the upper threshold from the data found across various sites but then even a Data Scientist can earn up to 40 or 50 lakhs per annum and Data Analyst can go all the way till 30 lakhs per annum as well. So make sure that you check your relevant experience and you have extremely good certification so that you can get a very high paying job regardless of it being a Data Scientist’s role or a Data Analyst’s role.
At the end of the day we also know that Data Scientist’s roll and the Data Analyst’s roles are one of the most trending jobs in the world which are present today. We hope this article was helpful and informative. See you then!!
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