With its flexibility and widespread use across industries, Python has emerged as one of the most popular programming languages in recent years. However, just knowing basic Python is no longer enough to excel as a Python developer today. You need developers with advanced skills to build complex solutions and make an impact.
This article discusses 7 critical modern and advanced skills that you can keep in mind while hiring Python developers.
Over the past decade, Python has seen a rise in popularity. According to IEEE Spectrum’s annual ranking, Python overtook long-time popular languages like C, C++, Java, and Perl to become the #1 programming language in 2022.
With tech giants like Google, Facebook, and Amazon using Python extensively, there is an exploding demand for skilled Python programmers across sectors. However, merely rehashing college curriculum Python skills is not sufficient to satisfy modern workplace needs.
The Python ecosystem is rapidly evolving, with new techniques, tools, and frameworks emerging constantly. Developers now need to know much more than just the basics to remain competitive and add value. They are expected to apply Python effectively across domains, adapt to new developments smoothly, and build complex large-scale applications.
7 Advanced and Modern Python Developer Skills
Investing in advanced skills allows your developers to unlock their full potential and prepare for the roles of the future. Here are 7 advanced areas your Python developers need to know:
1. Expertise in Core Python
Having strong expertise in Python’s core syntax, constructs, and concepts forms the foundation for advanced skills. Developers should spend time mastering Pythonic idioms and style guidelines like using meaningful variable names, functions over classes, list comprehensions, and so on to write clean, readable, and professional-grade code.
They need in-depth knowledge of core data types like lists, tuples, and dictionaries to represent data and structures like arrays and data frames from libraries like NumPy and Pandas to optimally organize data in tabular form.
A thorough grasp of control flow elements like loops, conditional statements, and list/dict comprehensions is vital for implementing program logic efficiently.
Object-oriented programming concepts like classes, inheritance, and polymorphism allow logical structuring of code and managing complexity in large programs. Knowledge of modules and packages enables the logical organizing of codebases into reusable components and prevents conflicts between identifiers.
Skills in reading/writing files, exception handling, logging, and concurrency techniques like multiprocessing and multithreading enable building robust programs that leverage multiple cores and distributed systems. The mastery of these core elements equips developers to efficiently use Python for data analysis, ML, web, and cloud application development.
2. Data Analysis with Python
With data expanding exponentially across industries, expertise in Python’s specialized data analysis libraries like NumPy, Pandas, Matplotlib, and Seaborn is crucial for developers today.
NumPy introduces critical data structures like multidimensional arrays and matrices that provide the foundation for most data tasks in Python.
Pandas builds on NumPy to provide flexible, high-performance data manipulation capabilities in tabular form using DataFrames. Developers skilled in Pandas can effortlessly handle large CSVs, time series, missing data, and other real-world data challenges.
Matplotlib and Seaborn enable the creation of rich static and interactive data visualizations and dashboards to uncover insights from data.
Advanced skills in NumPy let developers perform complex mathematical and matrix operations. Pandas provides easy data cleaning, preparation, joining, merging, and analysis at scale. Matplotlib and Seaborn facilitate customized statistical charts and plots for powerful reports and presentations.
Together, expertise in these core data analysis stacks greatly boosts the ability to handle large and complex data across domains.
3. Machine Learning and AI
For roles in the exciting domains of machine learning and artificial intelligence, Python developers should master libraries like TensorFlow, Scikit-Learn, Keras, and PyTorch.
- TensorFlow provides tools like Keras API, estimators, and TensorBoard for building, training, and monitoring complex deep-learning neural networks for tasks like computer vision, NLP, and voice/text generation.
- Scikit-Learn contains a vast array of classical ML algorithms like regression, classification, clustering, dimensionality reduction, and model selection techniques for tackling supervised and unsupervised learning problems.
- Keras provides a high-level API focused on rapidly iterating and prototyping neural network models using either TensorFlow or PyTorch as a backend.
- PyTorch offers key tools like torch, autograd, and torch.nn for GPU-accelerated tensor computation and deep learning model building.
With skills in these libraries, developers can quickly develop, evaluate, tune, and productize ML systems for fraud detection, recommendation engines, predictive analytics, self-driving vehicles, and other futuristic AI applications.
4. Building Web Applications
To become full-stack developers, expertise in Python’s popular web frameworks like Django, Flask, and FastAPI is highly sought after. Developers should know end-to-end design and development of web applications using these frameworks.
Key skills include creating clean URLs, cookie handling, request routing, designing flexible REST APIs and web services, integrating databases through ORM, and using templating engines like Jinja to separate business logic from presentation views.
They should also know to implement secure user authentication, authorization and session management, caching with Redis or Memcached for performance gains, and deployment of applications to production web servers like Nginx/Apache/IIS.
With competency in the complete web development stack encompassing front-end, back-end, APIs, databases, and DevOps, Python developers can rapidly build, connect, and deliver complete dynamic websites and web applications.
5. Software Testing and CI/CD
For accelerating software delivery without compromising reliability, Python developers need strong skills in test automation and CI/CD workflows.
This includes expertise in unit testing frameworks like pytest and unittest to test individual functions and classes and API testing using requests and responses to test endpoints and interfaces. Knowledge of integration and system testing methodologies and test coverage measurement using tools like coverage.py is vital.
Developers should know how to automate builds, tests, and deployment by integrating Continuous Integration/Delivery tools like Travis CI, Circle CI, and GitHub Actions with version control systems like GitHub.
Expertise in configuration and environment management with Ansible, Docker allows codifying and orchestrating deployment on diverse platforms.
These skills empower developers to release software faster and with confidence in quality using comprehensive test automation and continuous delivery pipelines.
6. Cloud and DevOps
As cloud platforms become ubiquitous, Python developers should upgrade their skills in provisioning infrastructure on major public cloud platforms like AWS, GCP, and Azure. This includes proficiency in managing servers, containers, databases, networking, load balancing, access controls, and other resources programmatically using Python and CLI tools.
Knowledge of containerization technologies like Docker and Kubernetes allows reliably deploying and managing containerized microservices applications in the cloud.
Expertise in infrastructure as code tools like Terraform, CloudFormation, and Pulumi enable programmatically provisioning reproducible cloud infrastructure and applications.
These skills equip developers with a cloud-native mindset to efficiently build, deploy, and operate distributed cloud-based systems using industry-standard provisioning, orchestration, and automation tools.
7. Practical Application
No amount of theory can substitute practical experience for developers. They should work on real-world projects and products to tackle challenges like:
- Architecting complex end-to-end systems with microservices, APIs, databases, and UIs
- Collaborating in large teams using version control, task management, and code reviews
- Shipping consumer and enterprise-grade applications
- Debugging and performance optimization using profilers and improved algorithms
- Dependency management with virtual environments and containers.
Some avenues to hone these skills are open-source contributions, freelancing gigs, and hackathons. Such hands-on application of knowledge accelerates learning and makes developers industry-ready.
Mastering advanced Python skills equips developers to build complex, scalable, and robust systems using industry-standard tools and techniques. With both depth in core concepts and cross-domain application, developers gain versatility to adapt smoothly to new technologies.
Fostering a culture of continuous skill progression will allow teams to fulfill Python’s immense potential in the age of data and the cloud.