Do you want to know why Python is the most loved language 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 take a behemoth of heavy analytical horsepower, but various online software tools do it all with ease.
As per a survey by industry analyst O’Reilly, 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.
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. The data processing techniques are widely used to expose the most valuable insights which allow the companies to formulate some effective strategies and to make an even better decision for their growth.
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. SQL is far ahead, followed by Java and Python. R ranks 5th.
Also, it is seen that Python developers are more loyal to this platform. Have a look at below stats:
Here are 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. Some of the most popular libraries incorporated in python are Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning-based computation.
For Natural Language Analysis and Sentiment Analysis, data scientists often prefer python as it has various libraries available. Also, various other popular frameworks of python that are widely used in data science and machine learning are TensorFlow, CNTK and Apache Spark.
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. Particularly, if we talk about new developers and coders, python is the simplest one. Therefore, many web development company don’t find it difficult to find the Python developers.
Since data computation includes quite difficult algorithms and multiple staged workflows, it will be good if any developer worries less about code problems and focuses more on searching solutions to issues behind the successful completion of the project.
In addition to this, Python is popular for simple syntax. It also means that it is quite faster in web app development project as compared to other programming languages like C++ and C. Moreover, it enables a developer to test algorithms without even implementing it.
3. Enough support
Python is an open-source programming language supported by a plethora of resources as well as high-quality documentation. Moreover, it also has a big community of developers who wish to offer advice and help through all stages of the development process.
4. Popularity
Since python is among the topmost programming languages as it is simple to understand syntax structure. It is quite easy to keep command over it. Hence, this language can be the one language that can help python developers for data computation projects.
Moreover, in a Developer Survey performed by StackOverflow, Python was found as one of the top 10 most popular programming languages.
5. Not Platform Specific
Python offers flexibility to offer an API from an existing language that provides the utmost flexibility. It can be said that this language is not platform-specific. Instead, it is platform-independent.
With doing slight changes in codes, you can get your app up and work on a new operating system. In this way, a developer can save its time for testing on various platforms and then migrating the code.
6. Python allows its users to focus on real-time problems
Python is pretty simple to comprehend for scientists who are not trained enough in the field of computer science. It allows you to skip various complexities that come when you try to drive external libraries.
Since Numeric, Ipython Notebooks, Matplotlib, and other tools came into development, python permitted scientists to majorly focus on solutions to several real problems instead of technology required to drive these solutions.
7. Python has numerous features that seem perfect for scientific computing
The most relevant cause is that NumPy and SciPy libraries permit the scikit-learn project, which is a quite important tool for machine learning. Python is very simple to read the language. Python developers take advantage of this feature and can understand the code of their peers.
Also, they can change, copy or share this code among each other. Since there is no chance of conflicts, it results in the effective exchange of algorithms, ideas, and tools between Machine Learning professionals.
8. Python is Robust
The lines of code in Python have relatively small quantities, making it less prone to issues, easily maintainable and easy to debug. This is the major attribute that is required for big data computation. According to the securities exchange mechanism, they have stated Python as the “waterfall” programming language. Moreover, companies like DropBox, Google, and YouTube have made Python as one of its official programming languages. In addition, Python is very fast compared to other programming languages.
9. Python is Flexible
Language scalability is crucial for data scientists. In the year 2007, YouTube migrated its system from PHP to Python just for scalability reasons, stating that Python allows flexibility. Python is widely used in a wide gamut of industries and from a long list of different usages, from web applications to system administration, desktop applications, and voice over IP. Python is having a huge developer community to sort all your issues.
Python was not originally built to answer a special requirement, Python is not driven by specific APIs or templates, and is well-suited to rapid development for all types of applications. Moreover, businesses that want to create their application on Python will really like this flexibility.
Currently, all python applications support all major operating systems. You can even use interpreters in python to run the source code using specific tools and platforms. Besides, it allows you to run the same source code on multiple platforms without recompiling the code.
Therefore, you are not required to recompile the source code after making any changes. In addition, you can run the modified source code without recompiling. After that, python developers can check the impact of changes made to the source code. This feature makes it easier for Python developers to make changes in the source code without taking more development time.
When you hire web developer, you can use them to develop the prototype of the software application rapidly. Moreover, developers can build software applications directly using the prototype simply by altering the python source code.
Python programming makes it easier for developers to do coding and testing together simultaneously by using the TDD (Test Driven Development) approach. Developers can easily write required tests before writing the source code and use test cases to the source code of python application continuously. These tests can also be used for checking if the python applications meet the requirements of the source code.
Python is known as the general-purpose programming language. Therefore, developers can use this programming language to develop both desktop and web applications. Moreover, you can use python for developing numeric and complex data-driven scientific applications. Python is also designed for performing in-depth data analysis and visualizations.
Developers can take advantage of data analysis features for creating custom big data solutions without putting extra resources and time. Similarly, python provides APIs and libraries for data visualization to help you in visualizing and displaying the data in a more effective and appealing way. In addition, many python developers are using python for doing AI (Artificial Intelligence) and ML (Machine Learning) tasks.
Being a versatile programming language for developing Machine Learning applications, Python is emerging as the most effective and worth programming language to create various software based on big data.
With the increase in popularity of Python and data science, businesses are shifting towards python for data-centric software development. Many software development company are offering services such as Cognitive Insight, Cognitive Engagement, Cognitive Automation, Data Mining, and Robotic Process automation.
Startups can also implement machine learning in their existing software or they can create advanced ML tailored software with the help of a software development company. Instead of software, there are other options like developing an ML-based mobile app or web app.
Credit: BecomingHuman By: Kristy Hill
