Clinical Data Management

Clinical Data Management is a critical process in clinical research, which leads to the generation of high-quality, reliable, and statistically sound data from clinical trials. Coursera's Clinical Data Management catalogue teaches you about data collection, data cleaning, data integration and transformation, as well as database design in the context of clinical trials. You'll learn how to develop and implement protocols for data collection, ensure data quality and compliance with regulatory standards, and use statistical methods to analyze and interpret clinical data. This knowledge is essential for careers in clinical research, healthcare, pharmaceuticals, and biotechnology, enabling you to contribute significantly to the development of new treatments and therapies.
6credentials
20courses

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Results for "clinical data management"

  • Skills you'll gain: Data Import/Export, Health Informatics, Cloud API, Google Cloud Platform, Health Information Management, Data Store, Medical Records, Clinical Data Management, Cloud Management, Medical Imaging, Cloud Computing, Application Programming Interface (API), Data Modeling

  • Status: Free Trial

    University of Minnesota

    Skills you'll gain: Payment Systems, Medical Devices, Healthcare Industry Knowledge, Medical Equipment, Cost Benefit Analysis, Clinical Trials, Health Technology, Clinical Data Management, Program Evaluation, Pharmaceuticals, Health Policy, Risk Analysis, Health Care Procedure and Regulation, Health Care, Regulatory Affairs, Stakeholder Analysis, Probability & Statistics

  • Status: Free

    Skills you'll gain: Tidyverse (R Package), Data Visualization, Clinical Data Management, Data Manipulation, Predictive Modeling, R Programming, Data Processing, Data Cleansing, Predictive Analytics, Data Pipelines, Feature Engineering, Applied Machine Learning, Machine Learning, Statistical Modeling, Performance Tuning, Hospital Medicine

  • Status: Free Trial

    University of Glasgow

    Skills you'll gain: Data Mining, Deep Learning, Clinical Data Management, Health Informatics, Responsible AI, Applied Machine Learning, Feature Engineering, Machine Learning, Predictive Modeling, Artificial Intelligence, Artificial Neural Networks, Time Series Analysis and Forecasting

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