Data Science and Analytics
Data science and analytics covers the tools used to explore, clean, analyze, and draw conclusions from data -- from spreadsheets through the Python scientific-computing stack.
Who this is for: Developers and analysts who want to work with real datasets.
Suggested starting point: Data Science
Technologies (7)
Data Science
Extracting insight from data using statistics, code, and domain knowledge.
Excel
The most widely used spreadsheet tool for data entry, analysis, and reporting.
NumPy
The foundational Python library for fast numerical arrays.
Pandas
The standard Python library for tabular data manipulation.
R
A language and environment purpose-built for statistical computing.
SciPy
Scientific computing algorithms built on top of NumPy.
XML
A structured, tag-based data format, common in enterprise and legacy systems.