Python is one of the most beloved programming languages today. There are tons of reasons to learn Python language—one of the primary reasons being its applications in Data Science. Today, we will learn why Data Scientists love Python so much.
Data is the new oil. Everyday, internet users generate about 2.5 quintillion bytes of data. And with this plethora of available data, every other business wants to extract insights from it. Enterprises want to plan for the future based on the insights provided by data. In simple words, data science is the extraction of actionable insights from raw data.
In 2001, the term “Data Science” was first used in a publication by William Cleveland. Today, it is famous as the sexiest job of the 21st century.
WHY DATA SCIENTISTS PREFER PYTHON OVER OTHER DATA SCIENCE TOOLS?
As of now, you must have understood that data scientists must go through various stages to complete any data science project. Every data scientists need the best tools to leverage techniques that can turn data into insights.
There is an availability of prominent languages such as C, C++, Java, R, Python, and JavaScript, each offering unique features depending upon your needs. Python has established itself as a preferred choice of language as it provides great value in bringing data science jobs to completion.
Python is an open-source, cross-functional, maximally interpreted language with lots of advantages to offer for data science applications. It offers high stability, an extensive library framework, and ease of use. Let us discuss the reasons why data scientists is the most preferred language in data science applications:
Easy to Learn
One of Python’s most important qualities is that it is easier to learn and promotes a shorter learning curve. When compared to other programming languages like R and MATLAB, Python focuses on simplicity and readability.
Thus, beginners can efficiently utilize its easy-to-understand syntax to build effective solutions for their problems. Data scientists prefer Python because it is easy to code, understand, and maintain.
Phenomenal Scalability
Python is much faster and scalable when it comes to scalability than STATA, MATLAB, Go, and R programming language. In Python, data scientists can approach a single problem in multiple ways rather than being stuck in one approach.
It is because of its scalability; YouTube migrated its process to Python.
Choice of data science libraries
Python is one of the most supported languages today; a massive choice of free libraries for users. And the best thing about these libraries is that they continuously grow and keep providing robust solutions.
Python comes with advanced statistical and numerical libraries like StatsModels, NumPy, SciPy, Pandas, Scikit-Learn, etc., and some deep learning libraries like PyBrain, Tensorflow, etc.
Data Science Python Community
Python is an open-source language, which means it employs a community-based development model. As the data science community embraces Python, more and more volunteer developers create libraries for data science applications.
Today, solutions for any issues faced by data scientists is just one search away.
Graphics and visualization
Python comes with visualization packages to represent your information on charts, graphical plots, and web-ready interactive plots. Python helps you make a good sense of your data as visual representation is much easier to understand.
Some of the popular Python data visualization libraries are Matplotlib, Seaborn, Bokeh, Plotly, Altair, Geoplotlib, etc.
How Python is Used in Data Science
Python is emerging as one of the most preferred languages because of its use case in various applications. Data science is no exception. You can read in details about various use cases of Python on our blog: Python in Action: Top 5 Python Use Cases.
Let us learn about how Python is used in the field of data science.
Data Collection and Cleansing
Python allows you to play with almost all formats of data available today. You can use the Python framework PyMySQL to easily connect with a MySQL database to execute queries and extract data. And if you are looking to scrap data, frameworks like BeautifulSoup help you read XML and HTML type data.
Once you have collected the data, you need to clean, transform, and prepare raw data before processing and analysis. Data cleansing and preparation can be very time consuming. But it is essential to put data in context to turn it into insights.
Data Exploration
Data exploration and deep understanding of the data are among the most critical skills every data scientist should have. Now, you need to figure out the business problems you are trying to solve and develop it into a data science question. Before that, you need to identify the data properties and segregate them into data types such as numerical, ordinal, nominal, categorical, etc and treat accordingly.
There are some very powerful libraries to perform data exploration in Python. Some of the most commonly used libraries are NumPy, Matplotib, Seaborn, and Pandas.
Data Modeling
The process of creating a data model for the data to be stored in a Database is termed as Data Modeling. It is a very crucial phase in the data science process. Once our data is put into context, the next step is to determine the methods and techniques to draw the relationships between variables. Some of the standard tools used for model planning are R, SQL Analysis Services, and SAS/Access.
Once the model is planned, the next step is to go ahead and build it. In this phase, you will analyze techniques like classification, association, and clustering to construct the model. Some of the standard tools used to build the model are SAS, MATLAB, Python, and WEKA, etc.
Python has many libraries like Numpy, SciPy, Scikit-learn to help you perform the tasks involved in data modelling.
Data visualization & interpretation
Data visualization is the final stage of any data science project. Here, you identify and communicate the key findings, patterns to stakeholders and analyze the project’s effectiveness.
Python offers multiple visualization libraries to interpret your data in interactive or even highlt customized plots .
Here are a few popular libraries for data visualization:
- Matplotlib: low level, provides lots of freedom
- Pandas Visualization: easy to use interface, built on Matplotlib
- Seaborn: high-level interface, great default styles
- Plotly: can create interactive plots
- Bokeh: create interactive, web-ready plots
- geoplotlib: creating maps and plotting geographicalareas