Python Libraries: Pandas and NumPy

Study Notes

Spreadsheets (e.g. Microsoft Excel, Google Sheets) are used for basic data mining tasks, hosting data in an easily accessible and readable format, and pivoting tables to showcase specific aspects of data.
Add-ins available for Excel, such as the Data Mining Client for Excel, XLMiner, and KnowledgeMiner for Excel, allow for common mining tasks like classification, regression, and clustering.

R is widely used for statistical modeling and computations by statisticians and data miners.
R is packaged with hundreds of libraries built for data mining operations, including regression, classification, and text mining.
Popular R packages include tm and twitteR for text mining and social network analysis.
R Studio is a popular open-source Integrated Development Environment (IDE) for working with R.

Python libraries like Pandas and NumPy are commonly used for data mining.
Pandas is an open-source module for working with data structures and analysis, allowing for data upload, organization, and manipulation.
Pandas enables basic numerical computations, statistical calculations, and data visualization.
NumPy is a tool for mathematical computing and data preparation, offering built-in functions and capabilities for data mining.
Jupyter Notebooks are a popular tool for Data Scientists and Data Analysts when working with Python for data mining and statistical analysis.

SPSS is a closed-source software that requires a license for use.
SPSS is popularly used for advanced analytics, text analytics, and trend analysis, with an easy-to-use interface that requires minimal coding.
SPSS comprises efficient data management tools and is popular for its in-depth analysis capabilities and accurate data results.

IBM Watson Studio is a web-based environment for data analysis and data science, leveraging open-source tools like Jupyter notebooks and closed-source IBM tools.
Watson Studio enables team collaboration on projects, from simple exploratory analysis to building machine learning and AI models.
It includes SPSS Modeller flows for quickly developing predictive models for business data.

SAS Enterprise Miner is a comprehensive, graphical workbench for data mining.
SAS provides powerful capabilities for interactive data exploration, identifying relationships within data, and managing information from various sources.
SAS offers a graphical user interface for non-technical users and is easy to use due to its syntax and debugging capabilities.
SAS can handle large databases and offers high security to its users.