Data Analytics Fundamentals
Expert-defined terms from the Postgraduate Certificate in Health Data Analytics course at London School of Planning and Management. Free to read, free to share, paired with a globally recognised certification pathway.
Data Analytics Fundamentals #
Data analytics fundamentals refer to the basic concepts and techniques used to a… #
In the Postgraduate Certificate in Health Data Analytics course, students will learn the foundational principles of data analytics and how to apply them in the context of healthcare data.
- Data Analysis: The process of inspecting, cleaning, transforming, and m… #
- Data Analysis: The process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
- Health Data Analytics: The practice of analyzing healthcare data to imp… #
- Health Data Analytics: The practice of analyzing healthcare data to improve patient outcomes, reduce costs, and optimize healthcare delivery.
- Descriptive Analytics: The analysis of historical data to understand wh… #
- Descriptive Analytics: The analysis of historical data to understand what has happened in the past.
- Predictive Analytics: The use of statistical algorithms and machine lea… #
- Predictive Analytics: The use of statistical algorithms and machine learning techniques to identify future trends and outcomes based on historical data.
- Prescriptive Analytics: The use of data and algorithms to recommend spe… #
- Prescriptive Analytics: The use of data and algorithms to recommend specific courses of action to achieve desired outcomes.
Explanation #
Data analytics fundamentals cover a wide range of topics, including data collect… #
In the context of healthcare data analytics, understanding these fundamentals is crucial for healthcare professionals to leverage data effectively and drive evidence-based decision-making.
Examples #
- A healthcare organization may use data analytics fundamentals to analyze patie… #
- A healthcare organization may use data analytics fundamentals to analyze patient records and identify patterns that can help improve treatment outcomes.
- Public health officials may use data analytics fundamentals to track disease o… #
- Public health officials may use data analytics fundamentals to track disease outbreaks and allocate resources more efficiently.
Practical Applications #
- Predictive modeling to forecast patient readmissions and proactively intervene… #
- Predictive modeling to forecast patient readmissions and proactively intervene to prevent them.
- Cluster analysis to segment patient populations and tailor interventions to sp… #
- Cluster analysis to segment patient populations and tailor interventions to specific groups.
- Time series analysis to predict patient flow in hospitals and optimize staffin… #
- Time series analysis to predict patient flow in hospitals and optimize staffing levels.
Challenges #
- Data quality issues such as missing values, outliers, and inaccuracies can imp… #
- Data quality issues such as missing values, outliers, and inaccuracies can impact the results of data analytics.
- Lack of data literacy among healthcare professionals can hinder the adoption o… #
- Lack of data literacy among healthcare professionals can hinder the adoption of data analytics in healthcare settings.