Every data scientist must know programming languages that will help them clean, manage and organise unstructured sets of data. There are a number of programming languages used by data scientists across the globe like Perl, Python, Java, C/C++, SQL and so on. However, Python stands out to be the most common and popular coding language for data scientists amongst all other languages.
Let’s learn why and how the Python programming environment is so commonly used in data science and machine learning:
1. Python Programming: Best programming language for data science and machine learning
Artificial Intelligence and machine learning are being implemented in a number of industries. As investments grow in this field, the demand for professionals in machine learning and data science is increasing simultaneously. According to the trend search results on Indeed.com, Python is the most common and popular language used for data science and machine learning. Being a multi-paradigm programming language, Python is capable of handling a multitude of tasks from data mining and website development to running embedded systems, all in one language.
The inherent readability and simplicity of the language makes Python easy to learn. Python’s availability of abundant libraries gives data scientists access to download tailored packages for free. Because of its extendibility, Python has exploded in popularity amongst data science and analytics professionals.
2. The growing demand for Python skills in the global job market and why Python is being widely used
From the development phase to deployment, Python has shown its versatility in its use and applications. Its popularity due to its millions of benefits has made it an in-demand skill across various job profiles in the global job market. Python’s reliability has big companies like Google, Pixar and Spotify using its services in their operations.
Let’s have a look at a few reasons why Python has been so extensively used across industries:
Great choice of libraries
Python libraries provide their users base level items so users don’t have to code from scratch every time. Machine learning and AI require continuous processing of data and Python helps you access, manage and transform the data. Some of the widespread libraries used for AI and machine learning are:
- Machine Learning– Keras, Scikit-learn and TensorFlow
- Data Analysis – NumPy and Pandas
- Data visualisation – Seaborn
Easy to use
While working on machine learning and data science, professionals deal with big data sets that need to be processed in a convenient and effective way. The simplicity of the Python language makes it easy for data scientists to learn before they start using it for ML and AI development. Python’s simple syntax helps data scientists work with complicated systems with ease whilst ensuring clear connections between the system elements.
Python used for machine learning development can be easily run on any platform – Windows, MacOS, Linux, Unix and others. With the help of Python, very small-scale changes can transfer processes between platforms and modify codes with ease.
Being an open-source language, Python gives both beginners and professionals access to a number of resources, which is mostly available online. Python communities and forums are great places for programmers in both beginner and professional levels to discuss errors and help each other solve problems.
3. How the Python programming environment is being used by data professionals on the job
A data scientist has a massive task to extract meaningful and insightful information from big data sets, statistics and registers. Usually, this data is unstructured in nature and difficult to find connections between different metrics with meaningful accuracy.
With machine learning, a data science professional can discover connections between datasets with a required sophisticated programming language. In these situations, Python becomes an important element in making data easy to read in a spreadsheet format. Apart from that, Python is used by data scientists in predictive analytics, prescriptive analytics and so on which makes it a powerful skill of every data science professional.
4. Applications of Python programming in different industries
The simplicity, readability, versatility and flexibility of Python makes it the best language used by many data professionals across a number of industries. Let’s discuss some examples of industries that use Python:
A number of online travel booking platforms have invested in data science in order to explore more areas of opportunities with the goal to enhance the overall customer experience and satisfaction.
Chatbots, Recommender Systems, Predictions (flight fares, availability of hotel rooms, etc.,) and content curation make use of AI algorithms to extract meaningful insights and convert them into personalised service offerings and enhance customer experience. For instance, Skyscanner used Python programming language to predict the behaviour of new flight routes.
Data science and AI is extensively used in the financial services industry to solve problems in risk management, fraud prevention, automation, personalised banking and tools that offer high-quality customer service. Some common banking softwares built on Python are Venmo, Affirm and Robinhood.
Big players in the transportation industry such as Uber are using a machine learning platform called Michelangelo PyML built with Python. This technology is used to solve day-to-day tasks based on online and offline predictions.
The healthcare industry deals with large amounts of data that are highly confidential in nature that requires high security. Python apart from all other programming languages can deal with security threats. Through Python’s strong and devoted community of professionals, the vulnerabilities and threats are quickly detected and fixed.
5. Is it necessary to learn the Python programming language to become a data scientist?
According to Cloud Academy 2018, 66% of data engineering job advertisements mentioned Python. Python is a key skill for entering into data science in any industry including engineering, healthcare, machine learning, AI and more. Like any other language, Python does take time to master. If you are motivated and willing to learn Python, there is a massive opportunity to fill the demand for data science professionals in the job market.
Here are some quick ways you can become more familiar with Python and how it is used for data science:
a) Join Python meet-ups on the Meetup app and get started with learning the languages with the help of the Python developer’s community. This is a great way to learn new skills whilst building strong professional networks on the go.
b) Read guides, books or blogs specific to the applications of Python tailored for data analysis and data science.
Here are some great books to get you started:
- Automate the Boring Stuff with Python
- A Byte of Python
- Learn Python, Break Python
- Python Data Science Handbook
c) Building mini projects using easily available data will help you solidify your understanding of the basic concepts of Python.
d) NumPy and Pandas are great Python libraries to get access and play with data. Make use of these libraries to practice data analysis and visualisation.
With the demand for data scientists increasing steadily every day, having skills in Python will help you gain a competitive edge while breaking into the data science industry. In addition to the foundational skills in Python, a course in data science will help you kick start your career as a data scientist. Continue exploring your career prospects in Python and data science by booking a consultation with an Institute of Data consultant now. Click here to schedule a call.