1. The Role of Python for Data Science
In the United States, over 36,000 weather forecasts are issued
every day, covering 800 different regions and cities. All that
analysis, collection, and reporting of data from different sources.
about 40% of data scientists use Python in their day-to-day
work for computing, analyzing and reporting of data.
Big organizations such as NASA, Google, and CERN also use
Python for almost every purpose of programming.
2. The Role of Python for Data Science
All About Data Analysis
Data analysis is the method of accumulating important data and processing that data to get
useful insights. A data scientist utilizes the major techniques that are related to data
manipulation and data visualization.
Python vs R: Which is Good for Data Science?
Both Python and R are popular choices for data scientists. Both languages have some pros
and cons when it comes to data-centric software development services. While both of these
languages are competing to be the language of choice for data scientists, let’s have a quick
look at their share and compare 2016 with 2017.
Now the below-mentioned picture shows the number of jobs that are related to data
science by any programming languages. Data Source: StackOverflow
Also, it is seen that Python developers are more loyal to this platform. Have a look at
below stats:
3. The Role of Python for Data Science
Why is Python Awesome for Data Science?
five exciting things about Python that makes it the best programming language for data scientists.
1. Prebuilt Libraries
One of the most important characteristics of Python is the list of libraries and frameworks that makes
coding easier and saves a lot of development time
2. Simplicity
Python is well known for its incisive and readable code. Also, no other language can compete with it for
being easy to use and simple.
3. Enough support
Python is an open-source programming language supported by a plethora of resources as well as high-
quality documentation.
4. Popularity
Since python is among the topmost programming languages as it is simple to understand syntax structure.
5. Not Platform Specific
It can be said that this language is not platform-specific. Instead, it is platform-independent.
