Guest Post by Danish Wadhwa
Data Science has gained a lot of popularity in the last few years. This field’s primary focus is to convert meaningful data into marketing and business strategies which helps a company grow.
The data is stored and researched to get in a logical solution. Previously only the top IT companies were involved in this field but today businesses from various sector and fields such as e-commerce, health care, finance, and others are using data analytics.
There are various tools available for data analytics such as Hadoop, R programming, SAS, SQL and many more.
However the most popular and easy to use tools for data analytics is Python. It is known as a Swiss Army knife of the coding world because it supports structured programming, object-oriented programming as well as the functional programming language and others.
According to the StackOverflow survey of 2018, Python is the most popular programming language in the world and is also known as the most suitable language for data science tools and applications.
Python also won the heart of developers in the Hackerrank 2018 developer survey which is shown in their love-hate index.
Python: The Best Fit for Data Science
Python has a unique attribute and is easy to use when it comes to quantitative and analytical computing. It is an industry leader for quite some time now and is being widely used in various fields like oil and gas, signal processing, finance, and others.
Further, Python has been used to strengthen Google’s internal infrastructure and in building applications like YouTube.
Data Science Python is widely used and is a favorite tool along being a flexible and open sourced language. Its massive libraries are used for data manipulation and are very easy to learn even for a beginner data analyst.
Apart from being an independent platform it also easily integrates with any existing infrastructure which can be used to solve the most complex problems.
Most of the banks use it for crunching data, institutions used it for visualization and processing, and weather forecast companies like Forecastwatch analytics also use it.
Why is Python preferred over other data science tools?
# Powerful and Easy to use – Python is considered a beginner language and any student or researcher with just basic knowledge can start working on it.
Time spent on debugging codes and on various software engineering constraints are also minimized.
As compared to other programming languages such as C, Java, and C# the time for code implementation is less which helps developers and software engineers to spend more time to work in their algorithms.
# Choice of Libraries – Python provides a massive database of libraries and artificial intelligence and machine learning.
Some of the most popular libraries include Scikit Learn, TensorFlow, Seaborn, Pytorch, Matplotlib and many more.
Many data science and machine learning tutorials and resources are available online which can be easily accessed.
# Scalability – As compared to other programming languages like Java and R, Python has proved itself as a highly scalable and faster language.
It provides flexibility to solve problems which can’t be solved using other programming languages. Many businesses use it to develop rapid applications and tools of all kinds.
# Visualization and Graphics – There are varied visualization options available on Python. Its library Matplotlib provides a strong foundation around which other libraries like ggplot, pandas plotting, pytorch, and others are built.
These packages help to create charts, web-ready plots, graphical layouts, etc.
How Python is used in each stage of Data Science and Analysis?
# The First Stage – Firstly we need to know and understand what type of form does a data take. If we consider data as a huge excel sheet with lakhs of rows and columns, then you should know what to do with it?
You need to derive insights by performing some functions and looking for a particular type of data in every row as well as column.
It can consume a lot of time and hard work to complete this type of computational task. Hence, you can use the libraries of Python like Pandas and Numpy which can quickly perform the job by using parallel processing.
# The Second Stage – The next hurdle is getting the necessary data. As data is not always readily available to us, we need to scrape data from the web accordingly. Here the libraries of Python Scrapy and BeautifulSoup can help to extract data from the internet.
# The Third Stage – In this stage, we need to get get the visualization or graphical representation of the data. It becomes difficult to drive insights when you see so many numbers on the screen.
The best way to do this by representing data in the forms of graphs, pie charts, and other formats. To perform this function the libraries of Python Seaborn and Matplotlib are used.
# The Fourth Stage – The next step is machine learning which is a highly complex computational technique. It involves mathematics tools like probability, calculus and matrix functions of over lakhs columns and rows.
All of this can become super easy and efficient using the machine learning library Scikit-Learn of Python.
All of the discussed steps were of data in the form of text but what if it is in the form of images. Python is well equipped to handle this type of operations also. It has an open source library opencv which is dedicated only for image processing.
Python’s Popularity in Data Science Groups and Communities
Python’s compatibility and easy to use syntax makes it the most popular language in the data science communities and groups. Those who don’t have engineering and science background can also learn with within a quick time.
It is most suited for prototyping and machine learning and the availability of online courses which is suitable for beginners. Its versatility and easy to understand makes Python the most sought after-skills that big organizations are looking in a data science professional.
The deep learning frameworks in its APIs along with its scientific packages makes Python incredible productive.
According to the website Towards Data Science, in the last two years, there has been a lot of improvement and evolution since the release of the library TensorFlow. It is also said that where AI takes a lot of research, one can validate their ideas in just twenty code lines in Python.
Machine Learning scientists and developers also prefer Python for building applications and tools like sentiment analysis and NLP (natural language processing).