Hours for Spring 2024
Welcome to the landing page for data and data analytics resources at Richardson Library. Data science is a vasy and quickly epanding domain that has an integral role in modenr research.
There are man ydifferet ways to approach data including hte Data one Lifecyle, the Cross-industry standard process for data mining (CRISP-DM) scheme for organizing approaches to data analysis, and many more
The Earl >S Richadson librayr uses a simple approach to planning, and organziing analyses with data:
Data Capture/ Collection which includes all aspects of choosing an exisitng dataset for analysis or collecting data through research
Data Exploration (Exploratory Data Analysis) including techniques for understanding basic descriptive statistics like central tendency, distribution, variance, correlation etc. and identifying data quality issues like null values,m imabalnced data sets, encoding issues and making decisions about how to handle these.
Data Processing involves all aspects of modifying data to meet the requirements of the sleected analytic technique(s)
Data Analysis includes the expansive range of techniques for diagnostic examination of a data set to derive meaningful insihgts into the nautre of the data.
Data Visualization includes deicsions about what graphic devices to use for representing the data, along with deicisons about color representation, formating , and othe rgrpahic elements of deisgn.
Prallele to theforegoing is a component of data management, including decisoins about dat aformat,s storage, documenttaiton and the like,
Data Management
From here you can access other guides for different topics and applications in data science and analytics.
Please also see the data source s guide to the left for data sources grouped thematically and topically.
A data science project can be generalized into the following categories:
The following links will take you to other Library Guides with specific resources and guidelines on different phases of a data project:
Special Topics:
