Is Python a skill? Here’s what we think at SkyHive

Python is a ubiquitous programming language. By simply using the Internet to access this article, you’ll have come across Python multiple times already. It’s one of the world’s most popular programming languages, with four out of five developers claiming that it’s their main language. Used by 1.4% of all websites according to Web Technology Surveys, behind it is a global jobs market of more than 10 million strong.

What is Python, exactly?

Python is an interpreted, object-oriented, general-purpose programming language that’s known for its simplicity. It’s very easy to write in Python due to its similarities with the English language and the fact that it was purposely created to be highly readable. As a result, Python enables programmers to express concepts in fewer lines of code compared to languages like C++ and Java.

But does being able to program in Python qualify as a skill?

To answer that question, we first need to know what a skill is. We’ve already covered this in another post on our blog—From job roles to skills: Embracing the shift to reskilling and skill-based hiring—but for the sake of simplicity, let’s recap.

What is a skill?

A skill is a specific ability or proficiency that an individual develops through practice and experience. Skills are often task-orientated and measurable, focusing on the capability to perform certain activities effectively. Skills can be categorized into various types, such as technical skills, soft skills, and transferable skills.

• A technical skill is a specialized ability needed to perform a task that’s related to a particular job or field. Examples include coding, data analysis, and machine operation. They are usually acquired through education, training, and experience—or a mixture of the three.

• A soft skill is an interpersonal or intrapersonal skill that affects how an individual interacts with others and manages their work. Examples of soft skills include communication, teamwork, problem-solving, and time management. Soft skills are typically developed through life experiences.

• A transferable skill is a versatile skill that can be applied across different areas. Examples include critical thinking, project management, and agility. These are the types of skills that help individuals to move between roles more easily.

Skills vs. competencies

Skills and competencies are often viewed as the same, but there’s a difference between the two. A competency is defined as the ability to do something successfully or efficiently. In practice, a competency is a comprehensive set of attributes that encompass the skills, knowledge, behaviors, and attitudes necessary to perform in a specific context.

Unlike skills which are specific and task-oriented, competencies provide a broader framework for understanding what it takes to perform well in a particular role or line of work. Skills make up one of four components of a competency, the others being knowledge, behaviors, and attitudes.

• Knowledge is the theoretical understanding needed to perform a job.
• Behaviors are the observable actions and conduct that contribute to performance.
• Attitude is the mindset and approach an individual brings to their work.

With this information then, we should be able to determine whether being able to program in the Python language constitutes a skill.

Is Python a skill?

Applying the important elements of the definition of a skill—(i) a specific ability or proficiency (ii) developed through practice and experience (iii) that’s task-oriented and measurable—tells us that Python is a skill.

Additionally, several other things make Python proficiency a valuable skill:

• Industry demand: Many technology companies prioritize Python skills because of its applications in emerging areas such as machine learning and data science. Python’s role in web development and DevOps also makes it a critical skill, with many organizations relying on Python for backend development and automation.

• Career opportunities: Proficiency in Python can open doors to various career opportunities. Roles in software development, data science, machine learning, and DevOps often list Python as a required or preferred skill.

• Large ecosystem: Python has a vast standard library and a rich ecosystem of third-party packages and frameworks. This high level of support means the language is always growing and being adopted. Python also has an active developer community, which makes it a skill that’s easy to maintain and develop.

While we at SkyHive say that Python is a skill, that’s not to say everybody agrees. In its recent report on the Emerging Skillstech Landscape, Northeastern University highlighted several examples that show how people define skills in different ways.

• French as a skill: You might be able to pass a vocabulary or even a written test, but could you hold a conversation in the language?

• Python: Sometimes the word Python alone is considered a skill whereas other times, a verb is used, such as “writing Python” or “teaching Python.” The two are obviously different.

• Project management: To some people, it’s a skill. To others, the term is far too broad.

Northeastern은 또한 조직마다 추적하는 스킬의 수가 얼마나 큰 차이를 보이는지 지적합니다. 미국 노동부에서 관리하는 O*NET에는 35개의 기본 및 교차 기능 스킬이 나열되어 있습니다. 일부 기업은 수만 개의 스킬을 나열합니다.

Job roles for people with Python skills

Python is one of the most common programming languages and is therefore a skill that’s in high demand. This has skyrocketed in recent years alongside Python use, which recorded a year-over-year increase of 22% in 2022 according to a report from GitHub. As such, there are various opportunities available to those who are proficient in the language, including:

• Software engineering: Software developers and engineers use Python to build, test, and maintain software applications and systems. Python's readability and extensive libraries make it ideal for developing web applications, desktop applications, and automation scripts. Developers often use frameworks like Django or Flask for web development and leverage Python's compatibility with other programming languages and tools to create robust and scalable software solutions.

• Data science: Data scientists use Python for data analysis, statistical modeling, and machine learning. Python's libraries, such as Pandas, NumPy, and SciPy, provide powerful tools for data manipulation and analysis. Libraries like Matplotlib and Seaborn help in visualizing data to uncover insights. Data scientists build predictive models using machine learning libraries like Scikit-Learn and TensorFlow.

• Data analysis: Data analysts use Python to process and analyze data, helping organizations make data-driven decisions. They often work with large datasets, using Python libraries to clean, transform, and visualize data.

• Cybersecurity: Cybersecurity analysts leverage Python to develop scripts and tools for identifying, analyzing, and mitigating security threats. They use Python to automate network scanning, vulnerability assessment, and incident response tasks. Python's libraries, like Scapy for network packet manipulation and Nmap for network discovery, are commonly used in this field.

• Machine learning: Machine learning engineers specialize in designing and implementing machine learning models and systems. They use Python for tasks such as data preprocessing, feature engineering, model training, and evaluation. Python libraries like TensorFlow, Keras, and PyTorch are essential tools in this field. Machine learning engineers work on projects ranging from recommendation systems and natural language processing to computer vision and predictive analytics.

Unlock your skills intelligence with SkyHive

SkyHive’s skills intelligence platform stores profiles of 1 billion anonymized workers and 60+ million companies; 3 billion job descriptions from 200 countries; and 3 trillion unique skill combinations required for current and future jobs. Intelligence at this scale gives you unparalleled insight into the skills of your workforce, lending a huge hand to long-term strategic decision-making.

Northeastern argues that what's most important is not just the total amount of data (though we believe that's quite important), but "how well employers can make use of skills-based approaches to meet their needs." Now that's an insight about skills inventories and skills intelligence we can all agree with.