Data Science

Subdisciplines

Data science is a broad field with many different subdisciplines. Read below to learn about some of the most popular specializations.

Data Analytics

Data analytics denotes the latter stages of the data science lifecycle, during which information is analyzed and insights are drawn out using statistical tools and software. Typical outcomes include predictive models, explanations of past events, or data visualizations that communicate insights clearly to stakeholders using statistical and computational tools.

Data Mining

Data mining is the process of gaining information from large datasets through the use of machine learning, statistical analysis tools, and databases. Data miners are skilled at sifting through data to identify patterns and correlations that would not otherwise be readily apparent. Data miners play a crucial role in uncovering hidden insights that empower organizations to make data-driven decisions and gain a competitive edge.

Database Management

Database management involves the development, maintenance, and administration of databases, which are structured systems used to store and organize data. Database managers work to ensure the stability, security, performance, and accessibility of their systems, while continually adapting them to meet the evolving data needs of their organization. Their role is crucial in ensuring that data remains available, secure, and optimized for analysis and business operations.

Machine Learning

Machine learning fits within the data science umbrella as a tool that data scientists use to effectively work with data. Machine learning algorithms automate tasks like prediction, classification, and anomaly detection, allowing data scientists to analyze massive datasets at scale with greater efficiency and accuracy. This field is evolving with advancements in deep learning, allowing for greater capabilities from those tools.