Which Python data science framework is best for beginners?

A data science framework is a group of libraries that offer data mining capability or ways to explore data, clean it up, and put it into a more usable format for data processing or machine learning activities. The entire procedure entails creating visualizations to draw recommendations from your data.

Python has a dynamic type system, and automatic memory management, and it supports a variety of programming paradigms, such as procedural, imperative, functional, and object-oriented programming. Its standard library is extensive and complete.

How to Pick The Best Python Data Science Frameworks?

For data analysis, data manipulation, and data visualization, Python supports a wide variety of frameworks. Choosing the best data science framework for Python can be a daunting task as there are several popular frameworks available, each with its own set of strengths and weaknesses. Here are some steps to help you pick the best data science framework for Python:

Identify your needs:

The first step is to identify your needs and requirements. What kind of data science work do you need to do? What are the specific tasks you need to perform? Do you need a framework for machine learning, data visualization, data preprocessing, or data analysis?

Research the popular frameworks:

Once you have identified your needs, research the popular data science frameworks for Python. Look at the features, capabilities, and limitations of each framework and see how they align with your requirements.

Consider the learning curve:

The learning curve is an essential factor to consider when choosing a data science framework. Some frameworks may have a steeper learning curve than others, which can affect your productivity and ability to complete tasks efficiently. Consider your experience level and the level of complexity you are comfortable with when selecting a framework.

Community and support:

The community and support behind a framework can also impact your decision. A large and active community can offer helpful resources, support, and documentation to help you troubleshoot issues or learn new techniques.

Performance:

Finally, performance is another critical factor to consider. Some frameworks may be faster and more efficient than others, depending on the task and size of the data you are working with.

Top Python data science frameworks for beginners

Tensor Flow:

It is a robust machine learning framework built on Python. It may be used for everything from making straightforward calculations to intricate neural networks. To generate Deep Learning models, Tensorflow published an add-on package named Keras in 2017. Keras offers high-level APIs and building blocks (much like Matlab).

Numpy:

The Python language’s Numpy library is a package that offers effective numerical operations. It works well for matrix manipulation and many other numerical operations. It can be used independently or in conjunction with other frameworks like Theano or Tensorflow.

Pandas

It is a Python module that offers high data structures and analytical tools. The data can be loaded from CSV or Excel files, altered, visualized using graphs or charts, etc. Everything you do with your data takes place in a Series (1D array) or a DataFrame, which is the fundamental idea behind pandas (2D Array). Working with DataFrames is quite simple using Pandas.

Matplotlib

It is a Python library for visualizing data. It offers some of the most popular data visualization modules for scientific and numerical data in Python. For your visualizations, you can select from a variety of back-ends including Qt, WX, etc.

Scikit-learn

Scikit-learn is a set of Python modules for machine learning. Performing classification, regression, clustering, and other machine learning tasks, Scikit-learn offers a wide range of common algorithms. Moreover, it supports pipelines, which are a series of connected estimators and transformers that make up a model.

Theano

Theano is a Python library to effectively construct, optimize, and evaluate mathematical statements involving multi-dimensional arrays. It can therefore be applied to machine learning applications that deal with complex computations.

Conclusion

The following chart shows the most popular Python frameworks for the year 2021.

If you’re looking to get ahead in the world of data science, learning how to use Python is a must. At Education Nest, we offer comprehensive courses in Python for data science that cover everything from the basics of programming to advanced techniques like machine learning and deep learning. With our expert instructors and hands-on training, you’ll be equipped with the skills and knowledge you need to succeed in the exciting world of data science.