Consider these well-paid and fascinating career paths as you continue your Python journey.
Learning Python is an incredibly useful skill if you want to pursue a tech career in 2024. Python is one of the most popular programming languages in the world—in fact, it was the third most commonly used programming language among developers worldwide in 2023 [1].
This language powers the programs behind Netflix’s recommendation algorithm to the software that controls self-driving cars. It’s used to develop many different programs and isn’t specialized for any specific problems. Python’s versatility and beginner-friendly nature make it one of the most widely used programming languages today.
Let's take a look at six potential careers that requires Python.
Because Python is a general-purpose language, it can be used across data science, software and web development, automation, and machine learning. It is used to develop websites, software, and applications, as well as in data analysis and task automation. Even those who aren’t developers, like scientists, accountants, and product managers, can use Python to organize finances, code research data, and more.
Simply put, Python is used to write programs that solve problems in web development, software testing, automation, data analysis, and machine learning.
Here, we cover six career paths you can consider that require Python expertise.
Data analysts collect, clean, and interpret data sets to solve problems. They can work in industries like business, finance, medicine, government, and technology. Data analysts might answer questions such as:
What type of customers should we target in our next marketing campaign?
What patterns in behavior are linked with fraud?
A data analyst may use Python to develop predictive models and produce data visualizations. Most commonly, they’ll use a combination of Microsoft Excel or Google Sheets, SQL, and Tableau to analyze data. Advanced data analysts will use libraries like NumPy or Pandas, and machine learning tools like TensorFlow or PyTorch. No matter your focus as a data analyst, your Python skills will be put to good use.
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Read more: Python or R for Data Analysis: Which Should I Learn?
Data scientists solve business problems by using data to answer them. They often solve these problems by developing predictive models for forecasting and theorizing based on large data sets.
A data scientist finds patterns and trends in data to reveal insights and inform strategy. They use programming languages like Python, R, SAS, and SQL to analyze data, creating algorithms and models to forecast outcomes. Data scientists use Python to develop machine learning techniques that can, in turn, improve the product or service at hand.
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Generative AI, Data Science, Model Selection, Data Analysis, Python Programming, Data Visualization, Predictive Modelling, Numpy, Pandas, Dashboards and Charts, dash, Matplotlib, Cloud Databases, Relational Database Management System (RDBMS), SQL, Jupyter notebooks, Machine Learning, Clustering, regression, classification, SciPy and scikit-learn, CRISP-DM, Methodology, Data Mining, Github, Jupyter Notebook, K-Means Clustering, Data Science Methodology, Rstudio, Big Data, Deep Learning, Quering Databases, Data Generation, Career Development, Interviewing Skills, Job Preparation, Resume Building
Machine learning engineers research, build, and design the artificial intelligence (AI) responsible for machine learning and improving existing AI systems. This career path requires knowledge of Python and other programming languages, statistics, communication, collaboration, and project management.
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Keras (Neural Network Library), Transformers, LLMs, PyTorch (Machine Learning Library), Deep Learning, Artificial Intelligence, Neural Networks, Artificial Intelligence (AI), Artificial Neural Network, Machine Learning, keras, Bidirectional Representation for Transformers (BERT), Positional encoding and masking, Generative pre-trained transformers (GPT), Language transformation, PyTorch functions, Tokenization, Hugging Face Libraries, NLP Data Loader, Large Language Models, PyTorch, Clustering, regression, classification, SciPy and scikit-learn, Softmax regression, Activation functions, Convolutional Neural Networks, Reinforcement Learning, Proximal policy optimization (PPO), Reinforcement learning, Direct preference optimization (DPO), Hugging Face, Instruction-tuning, Convolutional Neural networks CNN, TensorFlow Keras, Generative Adversarial Networks (GANs), Retrieval augmented generation (RAG), In-context learning and prompt engineering, LangChain, Vector databases, Chatbots, Logistic Regression, Gradient Descent, Linear Regression, TensorFlow, Generative AI applications, Vector Database, Gradio, Vector database, N-Gram, PyTorch torchtext, Generative AI for NLP, Word2Vec Model, Sequence-to-Sequence Model, Fine-tuning LLMs, LoRA and QLoRA, Pretraining transformers
Read more: 9 Best Python Libraries for Machine Learning
Cybersecurity analysts protect computer networks from cyber attacks and threats. They take actions to prevent attacks, defend against cyber threats, and respond to security breaches when they occur. This career path plays an important role in protecting an organization’s data and personal information.
Cybersecurity professionals frequently use Python to assess and define against such threats, often using it to create models for natural language processing (NLP) or automating tasks. Cybersecurity analysts might use Python to build NLP and AI models that protect networks and software against breaches, theft, loss, or unauthorized access.
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Cloud Computing Security, Computer Security Incident Management, Network Security, Penetration Test, Threat mitigation, AI in Cybersecurity Analysis, Cybersecurity, Information Security (INFOSEC), Encryption techniques, threat intelligence, Compliance techniques, Cybersecurity planning, Record management, Data Management, Cloud Architecture, Computer Architecture, Cloud Computing, Operating Systems, Threat Model, Access Control, Asset Management, Cybersecurity strategies, Regulatory Compliance, Security Analysis, Event Management, Security Response, System Testing, Security Testing, Authentication Methods, Access Management, Enterprise security, Identity governance, Network Monitoring, Computer Network
One of the most popular career paths for those with Python skills is back-end development. Back-end developers work on server-side software, which comprises everything that you can’t see on a website or application. They take appropriate measures so websites perform correctly and promptly, focusing on the symbiosis of databases, application programming interfaces (APIs), servers, architecture, and back-end logic. They must be familiar with languages like Python to build a website or app's structure and ensure the back-end performance is responsive to front-end user requests.
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Django (Web Framework), API endpoints, MySQL, HTML, Production environments, JavaScript, Cascading Style Sheets (CSS), Version Control, Github, Bash (Unix Shell), Web Development, Linux, Data Structure, Computer Science, Algorithms, Communication, Pseudocode, Filtering and ordering, Serializers and deserializers, Authentication and authorization, Debugging, REST APIs, Build a data model, Design a web application, Computer Programming, Python Programming, Application Programming Interfaces (API), Cloud Hosting, database administration, Database (DBMS)
Read more: Being a Python Developer: What They Can Do, Earn, and More
Database administrator is another career path ideal for those looking to apply their Python skills. This role is responsible for setting up the database structure and applications behind the scenes according to a company’s needs, such as database security or documentation, data storage, or system administration. Database administrators ensure the company’s data and networks are secure, organized, and accessible.
Read more: Your Database Administrator Career Guide
Is Python the right language for me?
Not sure if you should learn Python or another programming language? Take a look at these reads to compare and contrast:
- Python vs. C++: Which to Learn and Where to Start
- Python vs. Java: Which Should I Learn?
- Ruby vs. Python: Pros, Cons, and Where to Start
You can learn the basics of Python in two to six months, depending on how much time you devote to learning.
Prepare for a career that utilizes Python by advancing your skills with Coursera. Consider enrolling in the Python for Everybody Specialization offered by the University of Michigan. In this program, you'll learn how to develop programs and gather, clean, and visualize data using Python. Upon completing this Specialization, gain a shareable certificate to include in your CV, resume, and LinkedIn profile.
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This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of ...
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