You are good at Python programming and want to go for a specific use case of Python but don’t know what are those use cases?
You are someone who wants to learn Python and is curious about all the opportunities you can get after learning Python?
In this blog, I am going to talk about all the trending use cases of Python programming.
So, let’s get started!
Python is used almost everywhere from embedded systems to web development, but there are some use cases where Python is used a lot, and they are the following:
- Software Development (Web Application & Desktop Application)
- Data Science
- Machine Learning
- Automation Scripts
Each of the mentioned use cases are used a lot in today’s time. Let’s see some examples, projects that you can work on, work opportunities and technical skills needed for each of them.
Let’s not miss any of the important stuff related to each use case and follow along to know our best fit.
What Will You Learn?
Software Development
Software developers often use Python for different development tasks and software applications such as the following:
- Keeping track of bugs in the software code
- Automatically building the software
- Handling software project management
- Developing software prototypes
- Developing desktop applications using Graphical User Interface (GUI) libraries
- Developing simple text-based games into more complex video games
Server-side web development includes the complex backend functions that websites perform to display information to the user. For example, websites must interact with databases, talk to other websites, and protect data when sending it over the network.
Example
- Netflix: Uses Python for automation, data exploration, and visualization
- Google: Uses Python for a variety of web services and critical systems
Projects
- Building an Interactive Game — In this guided project, you’ll use basic Python programming concepts to create a functional and interactive word-guessing game.
- Exploring Hacker News Posts — Use Python string manipulation, OOP, and date handling to analyze trends driving post popularity on Hacker News, a popular technology site.
Opportunities
There are many full stack engineering jobs that require backend technology like Django and Flask.
In LinkedIn there are 2000+ jobs available for backend developers who are proficient in Python.
Technical Skills
- Basics of Python
- Python frameworks: Django, Flask, Fast API
- ORM technologies – SQLAlchemy
- Databases: MySQL, PostgreSQL etc.
- Deployment: AWS/GCP/Azure
Data Science
Data science is one of the most popular uses for Python. Data scientists and analysts use programming languages like Python and R to manipulate data for reporting, predictive analysis, and more. But while R can be a great choice, many Data Scientists prefer to learn Python because its English-like syntax can be easier to learn.
Example
Amazon: Provides machine learning and data science solutions, along with their AWS cloud services.
IBM: Specializes in digital insights and business intelligence, and offers cognitive solutions for data lakes and analytics-as-a-service.
Microsoft: Uses machine learning, data science, and Azure AI to improve experience for employees.
Projects
- The idea behind credit card fraud detection is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones.
- In today’s connected world, it’s become ridiculously easy to share fake news over the internet. To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using fake news detection.
- Building a forest fire and wildfire prediction system is another good use of data science’s capabilities. To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity.
Opportunities
There is no lack of opportunities in the field of data science. As long as you have experience of working with data you are eligible for most of the jobs.
There are lots of entry level opportunities in data science and many companies are hiring interns in data science so from there you can easily get into a company as a data scientist given you have the skills for this field.
Technical Skills
- Intermediate Python
- Data analysis libraries: Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn etc.
- Deep learning libraries: Tensorflow, PyTorch etc.
- Databases: MySQL, PostgreSQL
- Deployment: AWS/GCP/Azure
- Statistical models: Regression, Classification, Clustering
- ML Algorithms
Machine Learning
Machine Learning is included in data science but nowadays because of AI, there is a different type of job available in the market that is ML Engineer/AI Engineer.
Machine learning includes things like speech recognition, deep learning, artificial intelligence, financial services, even the recommendations Netflix serves up every time you log in that make you think, “How do they know?!” (Although, fun fact: Netflix also employs a team that manually tags videos as well.)
Example
Databricks: Offers a platform for machine learning teams to manage the complete ML lifecycle, from data preparation to production.
Yelp: Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently – no small feat when you’re dealing with tens of millions of photos.
Pinterest: Machine learning touches virtually every aspect of Pinterest’s business operations, from spam moderation and content discovery to advertising monetization and reducing churn of email newsletter subscribers.
Projects
- Media platforms like YouTube and Netflix recommend what to watch next using a tool called the recommender/recommendation system. Based on your preferences and input data, you can build either a content-based recommendation system or a collaborative filtering recommendation system.
- Speech contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. This project involves identifying and extracting emotions from multiple sound files containing human speech.
Opportunities
Machine learning jobs are exploding everywhere. Machine learning includes LLM which is one of the most trending technologies used in startups. Almost all the python related jobs need machine learning, so essentially learning machine learning skills opens a lot of opportunities for you.
Technical Skills
- Strong proficiency in Python and machine learning libraries such as TensorFlow, PyTorch, or Scikit-Learn
- Experience with data processing and analysis tools (e.g., Pandas, NumPy)
- Familiarity with cloud platforms (e.g., AWS, GCP) for deploying machine learning models
- Solid understanding of algorithms, data structures, and model evaluation techniques
- Exposure to Deep Learning – CNNs, LSTMs, Transformers, network architecture, network tuning, transfer learning, multi-task learning.
Automation Scripts
A scripting language is a programming language that automates tasks that humans normally perform. Programmers widely use Python scripts to automate many day-to-day tasks such as the following:
- Renaming a large number of files at once
- Converting a file into another file type
- Removing duplicate words in a text file
- Performing basic mathematical operations
- Sending email messages
- Downloading content
- Performing basic log analysis
- Finding errors in multiple files
Software testing is the process of checking whether the actual results from the software match the expected results to ensure that the software is error-free.
- Developers use Python unit test frameworks, such as Unittest, Robot, and PyUnit, to test the functions they write.
- Software testers use Python to write test cases for various test scenarios. For example, they use it to test the user interface of a web application, multiple software components, and new features.
Opportunities
Many companies are hiring automation testers who excel in python, also there is a high demand for automation engineers who can automate their different repetitive tasks with automation with Python.
Technical Skills
- Automation Framework Design using Python, CI/CD (Jenkins), Kafka, Docker
- Automation Framework Design in selenium, GIT
- PL/SQL and SQL
- Kafka and Rapid MQ
- Test frameworks like Pytest and BDD methodologies
Conclusion
From NASA to Facebook, and from Google to Instagram – leading technology giants all over the world use Python as a programming language for a wide variety of applications. Naturally, there’s a high demand for skilled Python professionals.
Enjoyed this blog? Want to read more? Go to my next blog 😉.
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