In a world full of data, you can find data scientists and several different kinds of data science professionals everywhere. Data is not just another thing, it is the fuel that drives insightful decision-making in all types of organizations, big or small. This widespread use of data science has led to a rapid growth of the data science industry.
According to Verified Market Research, the data science market size is expected to reach a market value of $674.51 billion by 2030 exhibiting a CAGR of 31.10%. And this growth will also lead to an increased demand for skilled data science professionals. And for them, the data science frameworks are the best companion to work with.
In this article, we will explore some of the most popular frameworks widely used in the data science industry.
What are Data Science Frameworks?
A data science framework refers to the collection of data science tools and libraries that help data scientists perform their day-to-day tasks efficiently. It helps them provide a well-set structure for different data science processes, be it data collection, cleaning, analyzing, modeling, or deployment.
A Data Science Framework can be used to:
- Streamline the data science process by automating several tasks involved in the process thus helping in more free time for data scientists to focus on other creative works.
- Makes data science projects more reproducible by providing a consistent set of tools and libraries. The work that has already been done can be easily replicated with a data science framework
- Promotes collaboration among data scientists by providing a common platform for them to work on.
Now let us look at popular data science frameworks.
Most popular data science frameworks
TensorFlow
TensorFlow is an open-source ML library developed by Google. It is best suited for numerical computation using data flow graphs. The graph can be easily understood as its nodes represent mathematical operations and edges represent multidimensional data arrays (tensors) communicated between them.
The data science professionals looking to develop and experiment with deep learning architectures and use graphs, SQL tables, and images, would benefit a lot by using this framework. It is lightweight and portable and thus can be used in any kind of device and operating system. It also comes with regular updates from its developer making it highly popular with clientele like Airbus, IBM, Twitter, etc.
Pandas
Pandas is like a helpful toolbox for the Python programming language. It’s a free and open-source library that works a bit like Microsoft Excel but for Python. This tool is great for looking at, changing, and showing data in a way that’s easy to understand.
Imagine you have a bunch of messy data, like numbers and dates all mixed up. Pandas can help organize and clean up that data. It’s handy for tasks like putting different pieces of data together, changing how the data looks, or just focusing on specific parts.
People in different fields like engineering, finance, social sciences, and statistics find Pandas super useful. If you’re good at using Pandas, it’s a plus when you’re looking for jobs related to analyzing data or being a Python expert. So, think of Pandas as your go-to tool for making sense of data in Python!
Pytorch
PyTorch is another very popular framework in the data science industry that has seen a surge in adoption lately. Researchers and developers who like to work with dynamic computation graphs prefer PyTorch for their work. It is developed by Facebook’s AI Research Lab (FAIR) and it provides an intuitive and easy-to-use interface that allows dynamic computation. This further helps in easy to debug and experimenting with models.
Since it is a great tool for research, it is widely used in educational environments. It can be easily integrated with other popular deep learning libraries like Torchvision and Torchaudio further reinforcing its position as a popular choice among data science professionals.
SciKit-Learn
If you are a beginner in machine learning, then SciKit-Learn is probably the first thing you will come across. It is a Python library offering a simple and efficient toolkit for data analysis and modeling. It is very easy to use as well as highly accessible and provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction.
It has also got a consistent API design and well-designed documentation that makes it handy for beginners as well as expert data scientists.
Apache Spark
Apache Spark is popular for handling big data. With each passing day, the amount of data generated is humongous. Around 375 million terabytes of generated every day and organizations are getting access to a huge chunk of data for processing. Apache Spark is known for its speed and ease of use and allows data scientists to perform complex analyses on large data sets with ease. It also supports a variety of programming languages including Python, Java, Scala, etc.
Conclusion
If you want to succeed in your data science career, then you must know how to properly use different data science frameworks. By enrolling in the best data science certification courses, you can learn about all these frameworks and take your data science career to the next level.
