К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Making Data Science Work for Clinical Reporting · LearnSpace
Назад в каталог
courseraАнализ данных

Making Data Science Work for Clinical Reporting

Курс от Genentech
Средний≈ 11.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course is aimed to demonstate how principles and methods from data science can be applied in clinical reporting. By the end of the course, learners will understand what requirements there are in reporting clinical trials, and how they impact on how data science is used. The learner will see how they can work efficiently and effectively while still ensuring that they meet the needed standards.

Навыки, которые вы освоите

R ProgrammingAgile MethodologyPackage and Software ManagementData QualityVersion ControlDevOpsData SharingMaintainabilityQuality AssuranceGitHubAgile Software DevelopmentRisk AnalysisMedical PrivacyClinical TrialsRisk ManagementClinical Data ManagementStatistical ReportingR (Software)

Программа курса

7 модулей · 94 учебных материалов

01Making Data Science work for clinical reporting6 материалов

Module Introduction

Making data science work for clinical reportingВидео

Lesson 1 :Introduction to Clinical Trials

Introduction to Clinical TrialsВидеоLearning more about clinical trialsЧтение

Lesson 2: Why Data Science?

Why use data science in clinical reporting?

Учитесь у экспертов

Dinakar Kulkarni

Principal Data Scientist

Kamila Duniec

Data Scientist

Kamil Wais

RWD Insights Engineering Team Lead

Daniel Sabanes Bove

Statistical Engineering Lead

James Black

Senior Director, Insights Engineering

Holger Langkabel

Senior Data Scientist

Making Data Science Work for Clinical Reporting
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 11.4 ч

7 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Видео

Module Review

Module ReviewВидеоModule reviewЗадание
02The burden of being faultless and transparent26 материалов

Introduction

IntroductionВидеоMotivationВидеоModule StructureВидеоTransparency vs. ReproducibilityВидео

Lesson 1: Data and Results Sharing

IntroductionВидеоCDISC StandardsВидеоDictionariesВидеоMore Details on MedDRAЧтениеMore Details on WHO Drug DictionaryЧтениеCoding StandardsВидеоReams of (Virtual) PaperВидеоIndustry DevelopmentsВидео

Lesson 2: Quality Assurance

IntroductionВидеоStandard Operating Procedures (SOPs)ВидеоQualification & ValidationВидеоData Quality ControlВидеоQuality Control of Analysis ProgramsВидеоReams of (Virtual) PaperВидео

Lesson 3: Data Access Restrictions

IntroductionВидеоPseudonymization & AnonymizationВидеоFSPs & CROsВидеоUnblindingВидеоReams of (Virtual) PaperВидео

Module Review

Module ReviewВидеоModule AssessmentЗадание
03Bringing DevOps practices and agile mindset to clinical reporting13 материалов

Module introduction

Introduction to Module 2ВидеоLinks and resources for Module 2Чтение

Lesson 1: Data Science as a new way of thinking

Data Science as a new way of thinkingВидеоTime to reflectОбсуждение

Lesson 2: Why are agile and DevOps a good fit?

Introduction to agileВидеоDevOps practicesВидеоThe Data Science mindsetВидеоLesson 2 QuizЗадание

Lesson 3: Changing together

Getting startedВидеоPilots and doing agileВидеоScaling upВидеоLesson 3 QuizЗадание

Module review

Module 2 RecapВидео
04Version control and git flows for reproducible clinical reporting20 материалов

Module Introduction

Version control and git flows for reproducible clinical reportingВидео

Lesson 1: Introduction to git and version control

Lesson 1 IntroductionВидеоThe whats and whys of version controlВидеоWhat is Git?ВидеоKey ideas in GitВидеоCollaboration via GithubВидеоFurther Reading on GitЧтение

Lesson 2: Git Flows

Introduction to Lesson 2ВидеоWorkflows in GitВидеоGit FlowВидеоSelecting workflows for clinical useВидеоUsing Git for AgileВидео

Lesson 3: Reproducible Projects in R

Introduction to lesson 3ВидеоUsing Git in RStudioВидеоBeing truly reproducible in RВидеоWell Structured ProjectsВидеоR LibrariesВидеоR VersionВидео

Module Review

Module ReviewВидеоModule AssessmentЗадание
05Making code reusable and robust in clinical reporting — a call for InnerSourcing21 материалов

Lesson 1: InnerSource & OpenSource

Introduction to Module 4ВидеоWhat is an InnerSourcing?ВидеоWhen to OpenSource?ВидеоModule readingsЧтение

Lesson 2: Developing our own R packages

Why should we use R packages for code development?ВидеоDifferent types of R packagesВидеоModule readingsЧтение

Lesson 3: Core principles (and tools) for R package development

Environment for R package developmentВидеоR package structure and contentВидеоR package documentationВидеоClean codeВидеоCode smells ВидеоDevelopment workflowВидео

Lesson 4 : CI/CD for R packages

CI/CD as a feedback loop for in-development R packagesВидеоAnatomy of a CI/CD workflow for an R packageВидеоSet up CI/CD for an R package on GitHubЛабораторная

Module review

Module ReviewВидеоModule AssessmentЗадание
06Assessing and managing risk7 материалов

Lesson 1: Why you need to understand the risk in using others code

Introduction to risk in your codebaseВидеоWhy should we consider package quality?Видео

Lesson 2: Building an understanding of risks

Considering the communities behind Open Source projectsВидеоAsessing the implementation of complex statistical methods in a package you useВидеоAssessing a package quizЗадание

Communicating your position on an R package

What tools and approaches can help to assess and understand risk in R packages I use?ВидеоAdvise a new colleague on the health and robustness of a packageВзаимная проверка
07Conclusion1 материалов

Conclusion

ConclusionВидео
Industry DevelopmentВидео
Before releaseВидео
Writing statistical software that can robustly implement complex methodsВидео
Module readingsЧтение