Making Data Science Work for Clinical Reporting from Coursera

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Syllabus

Making Data Science work for clinical reporting

In this module we will introduce this course. We will provide context on clinical reporting in general, describing how clinical trials work at a high level, as well as providing resources to learn more. We will then focus on motivating the course, describing the benefits of applying data science in the context of clinical reporting

The burden of being faultless and transparent

In this module we explore how data scientists are able to share their work confidently with the right people. We will look at important concepts related to data and results sharing, quality assurance and data access restrictions.

Bringing DevOps practices and agile mindset to clinical reporting

In this module we explore how to make the most out of data science by developing the best mindset.

Version control and git flows for reproducible clinical reporting

In this module we introduce the idea of version control, and git in particular. We show how you can use git effectively to manage your code during clinical reporting, and how it can be used as a tool for collaboration. We also look at making an R project in particular reproducible

Making code reusable and robust in clinical reporting — a call for InnerSourcing

In this module we will discuss benefits of InnerSourcing, OpenSourcing and developing our own R packages. We will review some of the core principles and tools of R package development. Finally, we will learn how to set up a CI/CD workflow for R package development.

Assessing and managing risk

In this module we will review the tools and approaches used to understand risk in a codebase used to derive datasets and insights. By the completion of this module you will get some hands on experience applying these principles against a specific open source library.

Conclusion

In this final module we will briefly review the course, and suggest next steps in your learning journey

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches foundational principles and practical tools for effective data science in clinical reporting, which is crucial for regulatory compliance and ensuring data integrity

Led by reputable instructors from the pharmaceutical industry and academia, including Daniel Sabanes Bove, Kamila Duniec, and Holger Langkabel, who are recognized for their expertise in data science and clinical research

Provides hands-on experience with version control, reproducible workflows, and risk assessment in clinical reporting, equipping learners with industry-standard practices

Covers topics from data management to risk assessment, offering a comprehensive understanding of data science principles and their application in clinical reporting

Emphasizes the importance of data transparency, quality assurance, and regulatory compliance, ensuring learners are well-versed in ethical and legal considerations

Requires a strong foundation in data science or clinical research, as it does not provide introductory material for these topics

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Making Data Science Work for Clinical Reporting with these activities:

Compile a curated collection of resources for clinical reporting

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Gather and organize relevant resources, such as articles, tutorials, and tools, to support your learning throughout the course.

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Use search engines or databases to identify resources
Organize the resources using a tool like Zotero or Mendeley
Create a bibliography or list of references for easy access

Review prerequisites

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Refresh your memory on the core concepts covered in the course prerequisites. Understand the key concepts.

Browse courses on Data Visualization

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Review lecture notes from prerequisite courses
Work through practice exercises from prerequisite courses
Complete a short online quiz to assess your understanding

Engage in peer discussion on applying data science to clinical reporting

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Connect with fellow learners to exchange ideas and learn from different perspectives. Discussing complex concepts with peers can enhance your understanding and critical thinking.

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Join an online forum or discussion group dedicated to clinical reporting
Engage in discussions, ask questions, and share your insights with other participants

Three other activities

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Contribute to an open-source project related to clinical reporting

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Get involved in the open-source community by contributing to projects that align with your interests in clinical reporting.

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Identify open-source projects related to clinical reporting
Find a project that matches your skills and interests
Contribute to the project by submitting bug reports, feature requests, or code changes

Mentor a peer who is new to clinical reporting

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By mentoring others, you can reinforce your own understanding of clinical reporting while helping others to learn and grow.

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Identify a peer who would benefit from your guidance
Offer your assistance and provide support as they navigate the course
Share your knowledge and experience to help them overcome challenges

Participate in a data science competition focused on clinical reporting

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Test your skills and knowledge by participating in a data science competition centered around clinical reporting. This is an excellent opportunity to apply your learning in a practical setting.

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Identify data science competitions related to clinical reporting
Form a team or work individually to participate in the competition
Develop and implement a data science solution to the competition problem

Career center

Learners who complete Making Data Science Work for Clinical Reporting will develop knowledge and skills that may be useful to these careers:

Data Science Manager

Individuals in this role oversee data science teams and projects. They are responsible for planning, budgeting, and executing data science initiatives. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting. This could be particularly valuable for Data Science Managers who are responsible for developing and implementing data science solutions in the healthcare industry.

See salaries and explore the career path for Data Science Manager

Machine Learning Engineer

These individuals design, develop, and implement machine learning models. They work closely with data scientists to ensure that machine learning models are accurate and reliable. The Making Data Science Work for Clinical Reporting course may be useful as it provides a foundation in data science principles and methods. This could be particularly valuable for Machine Learning Engineers who are working on projects in the healthcare industry.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations for improving business decisions. The Making Data Science Work for Clinical Reporting course may be useful as it provides a foundation in data science principles and methods. This could be particularly valuable for Data Analysts who are working on projects in the healthcare industry.

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Software Engineer

Software Engineers design, develop, and implement software applications. They work closely with other members of the software development team to ensure that software applications are efficient, reliable, and user-friendly. The Making Data Science Work for Clinical Reporting course may be useful as it provides a foundation in data science principles and methods. This could be particularly valuable for Software Engineers who are working on projects in the healthcare industry.

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Statistician

Statisticians collect, analyze, and interpret data to make informed decisions. They use their findings to solve problems and improve decision-making processes. The Making Data Science Work for Clinical Reporting course may be useful as it provides a foundation in data science principles and methods. This could be particularly valuable for Statisticians who are working on projects in the healthcare industry.

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Clinical Research Associate

Clinical Research Associates (CRAs) are responsible for managing clinical trials. They work with investigators, sponsors, and other members of the research team to ensure that clinical trials are conducted safely and ethically. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Medical Writer

Medical Writers create and edit medical content. They work with physicians, scientists, and other healthcare professionals to ensure that medical information is accurate, clear, and concise. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Regulatory Affairs Specialist

Regulatory Affairs Specialists ensure that drugs and medical devices meet regulatory requirements. They work with government agencies and other stakeholders to ensure that products are safe and effective. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Quality Assurance Analyst

Quality Assurance Analysts ensure that products and services meet quality standards. They work with other members of the quality assurance team to identify and resolve quality issues. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Project Manager

Project Managers plan, execute, and close projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to the required quality standards. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Business Analyst

Business Analysts analyze business processes and identify opportunities for improvement. They work with stakeholders to develop and implement solutions that improve business outcomes. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with other members of the data team to ensure that data is available and accessible for analysis. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

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Data Scientist

Data Scientists collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations for improving business decisions. The Making Data Science Work for Clinical Reporting course may be useful as it provides a foundation in data science principles and methods.

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Health Informatics Specialist

Health Informatics Specialists use data science and other technologies to improve the efficiency and effectiveness of healthcare delivery. They work with healthcare providers, patients, and other stakeholders to develop and implement solutions that improve healthcare outcomes.

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Clinical Data Manager

Clinical Data Managers are responsible for managing clinical data. They work with investigators, sponsors, and other members of the research team to ensure that clinical data is accurate, complete, and secure. The Making Data Science Work for Clinical Reporting course may be useful as it provides an understanding of how data science can be used to improve the efficiency and effectiveness of clinical reporting.

See salaries and explore the career path for Clinical Data Manager

Making Data Science Work for Clinical Reporting

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