Data Mining Libraries

Data Processing and Modeling

https://www.dataquest.io/blog/15-python-libraries-for-data-science/

Python Libraries for Data Science

•Data mining: Obtaining data that can be used to deduce conclusions.
•Data processing and modeling: Cleaning extracted data in order to prepare it for analysis and processing this cleaned data in order to reach conclusions. 
•Data visualization: Visually depicting data in various forms allowing for easy interpretation of conclusions.



Defining the Different Library Types for Data Science

The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners.    
  
Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.  

The Transformer in NLP is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. The Transformer was proposed in the paper Attention Is All You Need. It is recommended reading for anyone interested in NLP.

Hugging Face transformers

Data visualization tools, such as seaborn, Bokeh, and matplotlib, are essential for manually inspecting datasets. By plotting the data, practitioners can visually identify underlying data quality issues—like extreme outliers or recording errors—and develop an intuition for the type of problems they may need to address before feeding data into a model.

Data Visualization Libraries

Python's data science libraries can be broken down into three different categories:
- Data Mining
- Data Processing and Modeling
- Data Visualization

University of Michigan - Ann Arbor

Python is a powerful language that allows data scientists to perform data manipulation, analysis, and visualizations through its vast number of libraries such as numpy and pandas.

Data Science with Python

Categorizing Python Libraries for DS

Numpy: A library that can be used for array operations and data computing.

Pandas: A library that allows for the manipulation of data structures with dataframes and clean data presentation.

Matplotlib: A library that offers multiple data visualization options.

Seaborn: A library based on Matplotlib that allows for visualizing statistical models in  the medium of say a heatmap.

BeautifulSoup: A library that can be used for web crawling and data scraping.

SciPy: SciPy is an open source Python scientific computing library, which covers various tools in scientific computing, including statistics, integration, interpolation, optimization, image processing and so on.

StatsModels: A library provides many statistical models for statistical testing, exploration and visualization of statistical data.

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