Python developers are one of the most sought-after talents today. Data Science is another emerging field, and the combination of both is something most organizations are looking for in today's time. It offers simplicity and is hence ideal for companies to leverage. Python is one rare language that can be used for simple and complex projects.
Python continues to be quite popular in this dynamic environment where everything changes quickly. It is a simple and easy-to-learn language that reduces development time in half with its attractive compilation feature and effortlessly read syntax. However, Python developers have to manage a wide range of concepts while they work on complex projects. Python programming language is highly essential for a data science professional. In this article, we will look at the python developer's role in data science teams.
Python and its growing dynamics
Today, Python is used in fields like artificial intelligence, scientific computing, data science, and many more. The language is easy to write and read and also a reliable one. For these reasons, it is highly useful for data science professionals.
- Python was the third most in-demand language by employers in 2022
- Python is widely used in data analytics, a sector with a projected market value of USD 655.5 billion by 2029.
- Python is reportedly the primary language used by four out of every five developers.
- One of Google's recognized official languages is Python.
Some drivers behind Python’s growth are:
- Data analytics: It is undoubtedly the top reason why people are switching to Python; it's estimated that each individual in the world generates 17MB of data each second. Data analytics with Python makes sense because there has never been a more pressing need to manage and manipulate this data.
- Applications programming: Python is highly flexible and can be used to create all different types of applications. Python is a great option for programming applications.
- Data visualization: Python is an excellent choice for data visualization to make straightforward visualizations or something more interactive. Visualizing using Python will give developers a wide range of choices and enable them to transform their data into insightful understandings because of the wide variety of libraries available.
What does a Python developer do?
A Python developer is responsible for writing code for different projects that may include web applications, back-end systems, and desktop applications. Python developers work in a team, and their job may include working around debugging code, and databases and providing support to end users.
A Python developer frequently collaborates with data collecting and analytics to produce insightful responses to issues. Python is used in scientific computing, AI, academic research, machine learning, and web development. It is now widely accepted by the expanding data science community, which uses AI and ML.
Some vital tasks that Python developers work on are-
- Web development
Python is primarily used for creating the backend in web application development. Python frameworks handle a variety of backend application development-related operations, including interpreting HTTP requests, producing HTTP answers, gaining access to databases, authentication, and so forth.
- Data examination
Data examination or analysis is a method for gathering, transforming, and organizing data in order to make future predictions and well-informed data-driven judgments. Proper data examination helps in finding potential answers to business problems.
- Service for automation deployment
By adopting automated procedures, deployment automation enables the transfer of software between production and testing environments. As a result, deployments across the software delivery cycle become repeatable and dependable. Deployment automation eliminates human intervention in application deployments, allowing developers to release new features and applications more quickly and often.
- Web frameworks
Python Web framework is a group of modules or packages that enables programmers to create Web services or applications. Developers can avoid dealing with low-level issues like protocols, sockets, and process/thread management by using it. These frameworks aid developers with producing responses, interpreting requests, and storing data persistently.
Python is a great tool for data science, but what makes it so?
Before we answer the questions, let us discuss a few more features of Python. Python allows developers to quickly deploy programs and prototypes and speeds the development process. If required, a project can be converted to more advanced languages such as C or Java once it is on its way to becoming an analytical application or tool.
Below are key reasons to use Python in data science projects:
- Learnability: Python is popular because it is easy to learn, unlike other programming languages. It comes with simple functions and syntax and only takes a few lines of code to create an entire function.
- Community: Python is an open-source language and has large community support. You will find many world-class data science libraries and updated tools.
- Resources: Large library databases are available for usage by developers and data scientists. Additionally, the community freely distributes a tonne of tutorials on using Python tools.
Roles of Python developers in data science projects
Let us get to the most important part of the article - what does a python developer do in a data science project:
- For a data science application, a Python developer has to develop back-end components.
- A Python developer needs to work on the integration - connecting applications with third-party web services.
- Work with a design team or architect to formulate a technical solution for the application.
- Given data science projects deal with large datasets, a Python developer needs to create a scalable, testable, and efficient code necessary to handle programs that work with large datasets.
- Identify production issues and bugs and come up with a strategy or approach to fix them.
- Writing documentation for the project.
- Coordinating work with data scientists and other developers.
- Perform application automation, enhancement, balancing, tweaking, and usability.
What are the Data Science tools that Python developers use?
Some of the Data Science tools that Python developers use are as follows:
- Python developers, machine learning specialists, and data scientists all vouch for the open-source Scikit-Learn tool for data mining and analysis.
- A high-level neural network library created in Python, Keras is simple to use and ideal for deep learning and machine learning.
- Python's Theano library is handy for analyzing mathematical operations since it works well with NumPy.
- The scientific and technical computing fields also use SciPy.
Key skills of Python Developers
Python developers working on data science projects must also have:
- Strong experience with programming and an understanding of big data frameworks.
- Experience with different Python frameworks like Flask, Django, etc.
- Previous hands-on experience with object-oriented programming.
- Strong problem-solving skills as working with large data sometimes can be a challenge.
- Should have knowledge about libraries like Pandas, Numpy, Matplotlib, and Scipy.
- Should possess strong analytical abilities, substantial experience, and a solid understanding of the system's underlying algorithms.
- Deep understanding of front-end technology is required.
- Should have an understanding of Data Science such as data analysis, data cleaning, data visualization, and data wrangling.
Data science projects are complex. Businesses cannot afford to hire the wrong team for it. They must spend time and effort hiring the right candidates for their application. Developers looking to work on data science projects must prepare well for the interviews, understand the roles and responsibilities and work to improve in areas of weakness.
Python developers now have a sizable community surrounding themselves as a result of topping the index to reach number one. As Python gains popularity, employers are keen to hire coders. Python developers are among the highest-paid professionals since it enables data analysis and machine learning with backend development.