Top 7 Python Libraries for Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. There are a lot of tools and programming languages that can be used for Data Science. There’s always a debate on “Is R better than Python?” however, only you can decide what works best.

At the moment, Python is widely used for Data Science as it offers some powerful libraries that are easy-to-use and effective.

You may want to know - What packages in the standard library useful for data science work?

Let’s quickly review some popular Python libraries for Data Science.

TensorFlow is an open-source library used to create large-scale neural networks. It also provides tools and libraries to build and deploy Machine Learning powered applications.

Numerical Python (NumPy) is the core library for scientific computing in Python. It is one of the most popular libraries used for array computing.

SciPy is another open-source python library used for scientific computing and technical computing. It is a part of the NumPy stack and is one of the major libraries in the SciPy stack.

pandas is a fast and powerful library for data manipulation and analysis. It is the most widely used tool for data wrangling.

scikit-learn is a free Machine Learning Python library. It is mainly used for Machine Learning and Statistical Modelling. It is widely used and is considered as one of the robust libraries in Python.

Keras is an open-source neural network library. It is a powerful deep learning API mainly used for developing Deep Learning models.

Matplotlib is a plotting library used for creating static, animated, and interactive visualizations.

What’s Next!
If you are new to Data Science and wish to build a career in this field, then this book is for you. 

Data Science Fundamentals and Practical Approaches

Python Data Science

This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data pre-processing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. By the end, you will be able to use different Python libraries for various data analysis techniques.

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