- Implementation Science
- Clinical Text Mining
- R Programming
- Computational Phenotyping
- Data Quality Assessment
Clinical Data Science 특화 과정
Launch your career in Clinical Data Science. A six-course introduction to using clinical data to improve the care of tomorrow's patients.
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배울 내용
Describe how each type of clinical data are generated, specifically outlining who creates the data, when and why the data are generated.
Write SQL code to combine two or more tables using database joins.
Write R code to manipulate and tidy data including: selecting columns, filtering rows, and joining data sets.
Write markdown formatted text and combine with R code in RMarkdown documents.
귀하가 습득할 기술
이 전문 분야 정보
응용 학습 프로젝트
Each course in the specialization culminates in a final project that is a practical application of the tools and technique you learned throughout the course. In these projects you will apply your skills to a real clinical data set using the free, fully hosted online data science environment provided by our industry partner, Google Cloud.
Some programming experience and an interest in Clinical Data Science are required.
Some programming experience and an interest in Clinical Data Science are required.
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

이 전문 분야에는 6개의 강좌가 있습니다.
Introduction to Clinical Data Science
This course will prepare you to complete all parts of the Clinical Data Science Specialization. In this course you will learn how clinical data are generated, the format of these data, and the ethical and legal restrictions on these data. You will also learn enough SQL and R programming skills to be able to complete the entire Specialization - even if you are a beginner programmer. While you are taking this course you will have access to an actual clinical data set and a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
Clinical Data Models and Data Quality Assessments
This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
Identifying Patient Populations
This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
Clinical Natural Language Processing
This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals underlying NLP, as well as how to write regular expressions and handle text data in R. You will also learn practical techniques for text processing to be able to extract information from clinical notes. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop text processing algorithms to identify diabetic complications from clinical notes. You will complete this work using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
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하나의 강좌에만 등록할 수 있나요?
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해당 강좌를 무료로 수강할 수 있나요?
이 강좌는 100% 온라인으로 진행되나요? 직접 참석해야 하는 수업이 있나요?
I live in an area that restricts access to Google products. Will I be able to complete the specialization?
전문 분야를 완료하는 데 얼마나 걸리나요?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.