With an exponential rise in the popularity of data science, artificial intelligence, machine learning, and more, Python has helped to open access to the programming world. Due to its versatility and efficiency in the development of complex numeric and scientific applications, Python is now the most in-demand programming language in a Stack Overflow survey.
But what has Python got to do with data science? Most importantly, why should you learn Python for data science?
Python is an object-oriented programming language that supports functional and structured programming patterns. With these features, Python can quickly process and correlate large and complex data sets. Python is widely used in Data Mining, Data Science, AI, Graphic Design applications, Gaming, Network development, Product development, Rapid Application Development, Web Development, Web Frameworks, Embedded Systems, Automation, and more.
Python is an efficiently-written alternative to complex languages and gaining popularity as the primary language for many applications. In this guide, I will cover a few advantages of Python in data science and why you should learn it.
Data Science: A Snippet
With the plethora of data, every organization wants to extract insightful information to measure progress, identify challenges and opportunities, make better decisions, and create efficient products at low costs. Data scientists are responsible for processing, analyzing, and organizing large sets of data to extract useful insights for the business. Based on these insights, the decision-makers make critical business-related decisions. Data Science Course For Beginners is recommended for developing expertise in this domain.
While insights from the data lead to innovation, handling large sets of raw data is no less than a nightmare for a data scientist. That’s why a data scientist requires an efficient set of tools and an easy to execute programming language to complete this mammoth task.
Python for Data Science
With the rise in emerging technologies, data science is gaining popularity with each passing day. As its gaining pace from the career prospect, it’s beneficial for a data scientist to know at least one programming language to obtain and clean data. Python is now the most preferred language for developers and scientists for multiple reasons.
From designing enterprise applications, training data using ML models to cleaning and sorting data, Python is now the most widely used by professionals. Due to its dynamic nature and inbuilt features, Python overtook JAVA, which has been the developer’s favorite language for the longest of time.
- In 2016 and 2017, Python overtook R as one of the most used tools by data scientists’
- In 2018, 66% of data scientists used Python regularly, which led it to the number 1 spot for language for analytics professionals.
Why Learn Python For Data Science?
In today’s time, understanding Python is emerging to be one of the crucial skills of a data scientist, and this trend will only increase with the development of the programming language. You might be beginning your journey with the language, but it’s reassuring to know that the language has a lot in store for you. Read on to learn more!
1). Clarity:
When you want to begin your journey as a data scientist, among all programming languages, Python is perfect to start with. It is a high-level and open-source language and has a large community. Also, there’s no complex syntax. For instance, in the below image you can see, you have to write three sentences in Java to print “hello world” whereas, in Python, one line is sufficient.
2). Portability & Flexibility:
Among the available programming languages, Python has the highest scalability, which makes it incredibly useful for sorting any in-app development issue. A newbie programmer who knows C++ design structures would find the language functional to locate memory leaks or segmentation issues.
Python is highly portable and runs on multiple platforms like Mac, Linux, or Unix without any adjustments in code. The extensible aspect of the language allows users to write Python code into programming languages like C or C++.
3). Libraries and Frameworks:
For years, data scientists relied on MATLAB for scientific research. However, it all changed with the release of Python numerical engines. Python has hundreds of libraries focused on Data Analytics and Machine Learning, which acts as massive support for the developers to save manual time. Some of which are:
- Pandas: Ideal for data analysis, data handling, and data manipulation control
- NumPy: A free library for high-level mathematical functions and data manipulations.
- SciPy: Related to scientific and technical computing and used for algebra, data optimisation and modification, and more
4). Use in Web development:
Python offers a wide range of framework for developing websites such as Flask, Pylons, Django etc. As these frameworks are written in Python, you can quickly and effectively complete your work.
Pythons Full Stack frameworks for web development are:
- Django
- Pyramid
- Web2py
- TurboGears
And micro-frameworks for web development are:
- Flask
- Bottle
- CherryPy
- Hug
5). Versatility:
Previously, data scientists used the R programming language for statistical analysis. As Python codes are more manageable and more scalable than R, it is most preferred by the professionals without advanced degrees in statistics or mathematical fields. Several packages are available that allow users to understand and transform data and code machine learning algorithms. These libraries enable you to analyse broad data trends to make working with large data sets much more manageable.
6). Availability of Analytics Tools:
Python’s in-built data analytics tools are the perfect tool for complex data processing or self-service analytics. Countless companies around the globe utilise Python to reduce data. Furthermore, Python can penetrate patterns quickly and correlate data in large sets and give accurate insights along with other crucial matrices for the evaluation of performance.
Python also has a diverse pack of graphics and visualisation tools which makes Python a must-have tool for data science. With the use of graphics and charts, you can make the data more accessible and easier-to-use.
7). Job Prospect and salary pay scale:
The report shows that Python engineers are highest paid worldwide leaving behind C++, Java, and JavaScript professionals. The average salary for a Software developer with Python skills is around $76,500. While a Data Scientist with Python skills can earn approximately $97,000, and a senior developer earns more than $130,000. A web developer with Python skills earns around $59,000; a senior developer earns over $75000.
And you know the good news? That number will only increase with time as the demand for a data scientist with Python skills is growing day by day. According to Quanthub, there are three times more job postings in data science for job searches – which means the demand for data scientists is on the rise.
Apart from the hefty salary package, Python certification opens various career opportunities for Data Scientists. You can consider a career switch for positions such as:
- Product Manager
- Financial Advisors
- Data Journalist
- Python Developer
- Educator
8). Widely used by corporate giants:
A report by iDataLabs states that 69% of the companies generating <$50M in revenue, 9% of the mid-size companies with $50M – $1000M and 16% large firm with >$1000M in revenue use Python.
Python is extensively used by companies to build the minimum viable product (MVP). Big firms like Google, Facebook, Netflix, and IBM use Python for its scalability. Google loves Python and is continually hiring Python developers. Other big firms like YouTube, Instagram, Mozilla, IBM, Yahoo, and others use Python for a wide range of services.
So if you want to maximize your chance of getting into the big league firms in any industry, get started with your training in Python and secure a Python Certification.
Conclusion:
The current market trends prove that Python will continue to grow and expand into new areas of computing as a primary IoT programming language. As a data scientist, there are many opportunities ahead, so you should consider learning this language to build robust products and boost business.
Python is an increasingly required skill for many data science positions. So, take up a hands-on course to master data science and analytics techniques using Python. The demand for data scientists will double up by 500% over the next few years. If you want to take a giant leap to data scientist, it’s high time to make your move to learn and master Python.
Kim Larry is a full-time web developer and a part-time Python trainer. He is also a programming help expert at MyAssignmenthelp.com, a reputed php assignment help service. Here, students connect with subject matter experts for Math, computer science and other Swift assignment help services.