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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

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Interactive and Visualized Online Experimentation System for Engineering Education and Research
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Published on: November 24, 2021

Ad hoc efforts for advancing data science education.

Orianna DeMasi1, Alexandra Paxton2,3, Kevin Koy4

  • 1Department of Computer Science, University of California, Davis, California, United States of America.

Plos Computational Biology
|May 8, 2020
PubMed
Summary
This summary is machine-generated.

Extracurricular data science education efforts offer benefits beyond technical skills, complementing formal curricula. Surveyed organizers provided insights for improving these popular, innovative educational initiatives.

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Area of Science:

  • Data Science Education
  • Computational Science
  • Educational Technology

Background:

  • Extracurricular or ad hoc education efforts are increasingly vital for data science training.
  • These efforts help address the rapid innovation in data science practice and formal curricula.
  • Existing documentation on ad hoc data science education often lacks details on needs, limitations, and best practices.

Purpose of the Study:

  • To holistically understand the role of various ad hoc data science education formats.
  • To identify the strengths and areas for growth in current ad hoc data science education efforts.
  • To gather practical recommendations from organizers for future ad hoc educational initiatives.

Main Methods:

  • A survey was conducted among organizers of ad hoc data science education efforts.
  • Organizers were asked to provide perceptions of their events, including successes and challenges.
  • Recommendations for future organizers were collected based on past experiences.

Main Results:

  • Perceived benefits of ad hoc data science education extend beyond technical skill development.
  • These efforts show potential for sustained benefits when used alongside formal curricula.
  • Insights were gained into the practical aspects of organizing successful ad hoc data science education.

Conclusions:

  • Ad hoc data science education plays a crucial role in supplementing formal training.
  • Further investigation is warranted into the synergistic benefits of ad hoc and formal data science education.
  • Lessons learned from surveyed organizers offer actionable suggestions for improving and sustaining future ad hoc efforts.