The world of data through the eyes of data science
Data science also called statistics, data
analytics, machine learning is increasingly in demand in the last quarter
century, because of huge data collection possibilities and a rise in
computational power. As a result, there is a demand for multiple job profiles
like engineers, computer scientists, mathematicians, statisticians, etc. Each
and every branch of science, engineering, and business is dipped in the
technology of data analytics. If you are here, you too are interested in
becoming a data science or have an interest in data science.
Technical requirements to become a data
scientist
·
Programming
The first thing that comes to our mind
when reading the word technical is programming. Any technology to be learned is
always supported by programming. So, you need to have good knowledge of various
programming languages. Some of the most essential are Python, Java, C/C++,
Perl, and SQL. All the current data science roles use Python as their primary
programming language.
·
Knowledge of analytical tools
Valuable insights can be extracted from
raw data with the help of analytical tools. Various popular analytical tools
are Hadoop, Pig, Hive, Spark, and R.
·
Handling unstructured data
Data inflows from various sources in
different formats. This data is unstructured data. Handling data of various
formats and from multiple resources is the key requirement of a data scientist.
Non technical requirements to become a
data scientist
·
Communication skills
You understand the data and its insights
efficiently if you are a good data scientist. You must also be able to express
and explain your understanding of the nontechnical user of the data.
·
Strong business understanding
In
order to create a successful business model, you should know how and on what
points the business work, what opportunities need to be explored, what
potential challenges and problems the business face, etc
·
Data intuition
It is one of the most important skills
needed. With experience and proper analyzation, one can understand the
important and unimportant data. The sixth sense and intuition play an important
role here. The human brain is the best reasoning source. The features of data
can be understood with the help of these reasoning abilities of a human.
Machine Learning and Data Science
Machine learning is an important part of
data science. Machine learning cleanses the data and extracts meaningful
information with the help of several statistics and algorithms. Big data is
very big in terms of volume and variety and hence it becomes difficult for a
data scientist to work on it.
Machine learning helps to solve this
problem. Machine learning uses techniques like classification, regression,
clustering, and more to create business models.
You can see the use of machine learning
and data science in daily life as well. When you watch videos on Netflix or
YouTube, you start getting recommendations based on the videos you have
watched.
Skills needed to be a machine learning
expert are programming languages, statistics, probability, modeling skills.
Data science includes the aspects of
machine learning for its underlying functionality. Machine learning, data
analytics, artificial intelligence are all interrelated to each other.
Resource box
If you are ambitious enough to break all
the barriers in the field of data science, then data science course in pune [https://www.excelr.com/data-science-course-training-in-pune] is the best option for you. This course will cover all the technical
and nontechnical aspects of data science field.
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