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Top Data Science Libraries

by akshara

"Python is a general-purpose language and is often used for things other than data analysis and data science"
Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, presented in charts, images, tables, and graphs. It is the process of collecting, modeling, and analyzing data to extract insights that support decision-making.
Process of Data Analysis Data Identification Data Collection Data Cleaning Data Analyzation Data Interpretation Methods in Data Analysis Descriptive analysis Exploratory analysis Diagnostic analysis Predictive analysis Prescriptive analysis

Math

NumPy is one of the most essential Python Libraries for scientific computing and it is used heavily for the applications of Machine Learning and Deep Learning. NumPy stands for NUMerical PYthon. Machine learning algorithms are computationally complex and require multidimensional array operations. SciPy (Scientific Python) is the go-to library when it comes to scientific computing used heavily in the fields of mathematics, science, and engineering. It is equivalent to using Matlab.

Data Mining

BeautifulSoup is an amazing parsing library in Python that enables web scraping from HTML and XML documents. It automatically detects encodings and gracefully handles HTML documents even with special characters. Scrapy is a Python framework for large-scale web scraping. It provides all the tools needed to efficiently extract data from websites, process them as we want, and store them in preferred structure and format.

Data Exploration and Visualization

Pandas is an open-source package. It helps you to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data. Matplotlib is the most popular library for exploration and data visualization in the Python ecosystem. Matplotlib offers endless charts and customizations from histograms to scatterplots, matplotlib lays down an array of colors and other options to customize and personalize our plots. Plotly is a free and open-source data visualization library. It is one of the finest data visualization tools available built on top of visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework. Seaborn is a free and open-source data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Machine Learning

Sklearn is the Swiss Army Knife of data science libraries. Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction. PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments. TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL-powered applications. Keras is a deep learning API written in Python, which runs on top of the machine learning platform TensorFlow. It provides a much better "user experience", Keras was developed in Python and hence the ease of understanding by Python developers. PyTorch is a Python-based library that provides maximum flexibility and speed. Some of the features of Pytorch are as follows: Production Ready, Distributed Training, Robust Ecosystem, Cloud support

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