Machine learning and big data applications have seen a surge in usage over recent years. This is due to many factors but the most prominent can be attributed to the demand for businesses to possess data-driven insights. Inevitably this has forced data scientists to find the most efficient methods when building applications and machine learning models that can manage data in this way.
Python is a data scientist's best friend largely because of its simplicity as well as its range of libraries and frameworks that are specifically designed to create applications and manage data. What separates a Python framework or library from a language like R, Java, or Julia for data science is mainly its simplicity and the range of libraries and frameworks available.
In the realm of data science, there’s so much variety when it comes to how you can approach app development. Python is highly flexible, which will always be a major draw for data scientists. With that said, it’s important to not just know your options but ultimately how to leverage them. Here are some of the top choices for data scientists when it comes to Python libraries:
TensorFlow
This is a great choice when integrating machine learning that allows data scientists to get a visual understanding of how data flows through neural networks or processing nodes. It’s an open-source software library that Google created for users to build and deploy machine learning models and then train them at scale.
Pandas
This tool is super powerful for data manipulation and analysis as it provides structures and functions that work with pre-existing data. It also allows data scientists to easily transform and preprocess data which in the long run allows them to extract more of those valuable insights that we mentioned were in demand. Pandas’ ability to handle large datasets and integrate them with other libraries makes it a fundamental tool in a data scientist's toolkit.
OpenCV
As the name implies, this is another open-source library used for real-time computer vision tasks. With OpenCV, data scientists can do things that contribute to a much broader ideal for Artificial Intelligence. This includes tasks such as object detection, facial recognition, image stitching, and video analysis.
Theano
This is one of the first open-source software libraries for deep-learning. It’s known for its speed (due to its ability to optimize) and efficiency when handling mathematical computations, especially those found in model development for machine learning. Of course, now TensorFlow is the renowned favorite when it comes to deep learning but the two collaborate well and offer unique advantages.
PyTorch
PyTorch is another popular deep-learning framework with dynamic computational graphs and highly productive GPU acceleration (Great for data-intensive apps). It provides an intuitive programming interface that’s flexible and has gained popularity because of how easy it is to use and the level of support it offers for research and prototyping deep learning models.
NumPY
This is an imperative library if you’re dealing with numerical and scientific computing in Python. Healthcare, Finance, Manufacturing, Research, and Education among other industries for example will all utilize NumPY to solve problems and manipulate large datasets unique to their needs.
Matplotlib
This library is your go-to for data visualization and analysis. It works with other Python libraries such as Pandas and NumPy which allows data to easily be manipulated and integrated. For app dev, this will help with the data-driven aspect through its range of features and plotting functionalities.
Seaborn
Seaborn is a library built on top of Matplotlib for data visualization. It provides a higher-level interface and a variety of statistical visualizations. It simplifies the process of creating visually appealing and informative plots, which makes it valuable for data exploration and sharing results.
Statsmodels
As the name implies, this is a library for statistical modeling and hypothesis testing. It offers a comprehensive set of tools for regression analysis, time series analysis, survival analysis, and other statistical techniques. Statsmodels are very widely used in fields such as economics, social sciences, and finance.
Scikit-learn
This is a widely used machine learning library that provides a range of algorithms and tools for classification, regression, clustering, and dimensionality reduction. It's known for its user-friendly API and comprehensive documentation, making it an excellent choice for both beginners and experienced data scientists.
Choosing What’s Best For You
There’s a lot for data scientists to consider when narrowing down what libraries and frameworks are best for the task at hand. When it comes to Python, the number of options is a huge benefit but it doesn’t come without its challenges.
If you don’t have expertise in particular libraries, it can be difficult to navigate integration, and learning them on the fly is not easy, nor is it efficient.
Some quick notes about what you’ll generally want to look for include the following:
Compatibility and integration: Ensure the library works well with your existing tools and frameworks.
Performance and efficiency: Look for libraries that are optimized for speed and that can handle large amounts of data efficiently.
Documentation and resources: Look for libraries with clear documentation that explains and provides examples of how to use it.
Community support: Choose libraries that have an active community of users.
Scalability and extensibility: If you anticipate your project growing or taking on larger datasets, choose libraries that can scale and work well with distributed computing.
Long-term viability: Choose libraries that are regularly maintained and updated. You’ll want to make sure the library will be compatible with newer versions of Python, that it receives bug fixes, and incorporates new features over time.
The Takeaway
In Canada alone, around 90,000 SME's (Small and Medium Enterprises) disappear annually. This is prior to the introduction of AI, which means that “Staying competitive” is going to take on a whole new meaning in the years to come. You can’t fight it, but you can plan for it by adopting the right approach and consulting with experts who know how to navigate this change.
Written By Ben Brown
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