Python is the most valuable skill you can have when it comes to data-focused careers, but it’s also incredibly versatile for Web Developers and Software Engineers. Learning Python alone won’t get you a job, but it’s the best place to start for some of the most lucrative and fulfilling careers.
In combination with JavaScript, HTML and CSS, SQL, and Git, Python can help you launch your career as a developer. Learning data science concepts, SQL, machine learning, and mathematics alongside Python will prepare you for a data career.
One of the biggest advantages of using Python for both data professionals and developers is its massive collection of libraries and frameworks, collectively known as packages. Python packages enable more efficient coding, building secure and complex applications, and make Python versatile enough to be used for everything from building social media platforms to predicting the stock market.
Data Scientists and Data Analysts use Python and its packages to find useful insights in big data. Python is the most popular programming language among data professionals. Proficient Python programmers can delve deeper into data by specializing in machine learning. Machine Learning Engineers train computers using algorithms and data.
Python is also quickly becoming widely used in web development. Web developers are using Python’s most popular frameworks like Django for creating large scale platforms for business websites, news, and social media.
You can specialize even further within web development with jobs like Python Developer. Python developers can also get Back End Developer and Full Stack Developer positions by learning more about web development, JavaScript, Git, HTML, and CSS.
Python Career Options
Here are a few career paths for which you’ll likely need Python skills in order to succeed. We also include additional info about their average salaries, their day-to-day responsibilities, and a link to learn more about each career.
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Web Developer
Web developers build webpages using coding languages such as HTML, CSS, and JavaScript. They program functionality and identify/troubleshoot errors in code. Web developers can work on front-end development (the part of the website you see in a web browser), or on back-end development (the logic and database functionality that runs on the web server). Others work as full-stack developers, providing end-to-end (front to back) expertise.
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Software Engineer
Software engineers use their extensive knowledge of user experience design, operating systems, and programming languages to develop software. They can create different types of software, from games to operating systems. After analyzing a client's needs, they design, develop, and test software to meet that need. Software engineers can be divided into two distinct career categories: application engineers and systems engineers.
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Back End Developer
A Back End Developer builds the server-side of a web application and integrates front end development components.
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Python Developer
Python Developers typically choose to focus on back end web development, data science or analysis, scripting, or product development. They build the server side of websites, processes for data analysis, and create automation scripts.
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Full Stack Developer
Full Stack Developers build web applications for both the visible front end that users see and the back end that powers the applications.
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Machine Learning Engineer
Machine Learning Engineers create computer programs that enable machines to take actions without being specifically directed to perform those tasks. This job combines computer programming and data science to enable systems to learn and improve from experience automatically by using machine learning, a subset of artificial intelligence.
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Data Engineer
Data Engineers create the infrastructure for data and format data into a useful system which Data Scientists use to analyze large amounts of data. Data Engineers can specialize in pipelines, databases or platforms, warehouses or infrastructure, or be generalists.
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Data Scientist
Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine data sets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making.
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Data Analyst
Data analysts review large amounts of data to summarize, analyze, and visualize it and provide insights. Working from data from multiple, relevant sources, they create and maintain databases, and use statistical techniques to analyze the collected data. Data analysts must be able to communicate with others about what the data shows and to be able to provide realistic recommendations based on their analysis. Many industries such as healthcare, advertising, and retail rely on the work of data analysts to inform their business decisions and strategy.
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