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

Several Python libraries are commonly downloaded and used for the processing, visualization, and analysis of data. These include:

- **scikit-learn**: A machine learning library.
- **NumPy**: A library for scientific computing.
- **pandas**: A library for data manipulation and analysis.
- **Matplotlib**: A plotting library.

Core Python Libraries for Data Science

- **NumPy**: Used for array operations and scientific/data computing.
- **Pandas**: Used for data manipulation, cleaning, and analysis via dataframes.
- **Matplotlib**: A core plotting library offering multiple data visualization options.
- **Seaborn**: Built on Matplotlib, used for creating attractive statistical graphics like heatmaps.
- **BeautifulSoup**: Used for web crawling and scraping data from HTML/XML files.
- **SciPy**: An open-source scientific computing library covering statistics, integration, optimization, and image processing.
- **StatsModels**: Provides classes and functions for estimating and testing various statistical models.

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