We may earn an affiliate commission when you visit our partners.
Course image
Ana Laura Quintero and Niko Alain Cruz Sancén

En el campo de las ciencias de la salud se genera una gran cantidad de datos que son la base para tomar decisiones al administrar instituciones y sistemas de salud, al momento de dar atención a un paciente, así como en el estudio de la calidad del servicio.

Lo anterior origina la necesidad de que casi cualquier colaborador del sector salud, ya sean profesionistas, administradores e investigadores aprendan a clasificar, describir e interpretar datos y evidencias científicas, aplicando las pruebas estadísticas adecuadas.

Read more

En el campo de las ciencias de la salud se genera una gran cantidad de datos que son la base para tomar decisiones al administrar instituciones y sistemas de salud, al momento de dar atención a un paciente, así como en el estudio de la calidad del servicio.

Lo anterior origina la necesidad de que casi cualquier colaborador del sector salud, ya sean profesionistas, administradores e investigadores aprendan a clasificar, describir e interpretar datos y evidencias científicas, aplicando las pruebas estadísticas adecuadas.

Por dicha razón, en este curso te brindaremos las herramientas para que seas capaz de reconocer, analizar, interpretar, visualizar y utilizar de forma efectiva los datos generados en las ciencias de la salud, así como describir una base de datos empleando la estadística descriptiva (medidas de tendencia central, de dispersión). ¡Inscríbete!

What you'll learn

  • Clasificar apropiadamente los datos de salud.
  • Identificar pruebas de estadística descriptiva para datos cuantitativos y cualitativos.
  • Generar una base de datos de salud en Excel.
  • Ejecutar efectivamente las pruebas de estadística descriptiva [medidas de tendencia central, dispersión y posición].
  • Representar los datos descriptivos de una investigación.
  • Reconocer la descripción de resultados en la evidencia científica.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Well-suited for nearly any collaborator within healthcare systems, who must analyze data and scientific evidence
This course will develop your ability to visualize and interpret data effectively
In this course, you will learn to apply appropriate statistical descriptive tests for both quantitative and qualitative data

Save this course

Save Introducción a Ciencia de Datos en Salud to your list so you can find it easily later:
Save

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 Introducción a Ciencia de Datos en Salud with these activities:
Read 'Introduction to Statistical Thinking' by Benjamin Yakir
Gain a comprehensive understanding of statistical concepts and their applications in various fields, providing a solid foundation for the course.
Show steps
  • Read through the book, taking notes on key concepts and examples.
  • Complete the exercises and practice problems provided in the book.
Brush up on basic statistics
Reinforce your understanding of basic statistical concepts and techniques, making it easier to grasp more advanced topics covered in the course.
Browse courses on Inferential Statistics
Show steps
  • Review notes or textbooks on descriptive statistics and inferential statistics.
  • Practice solving problems related to central tendency, dispersion, and hypothesis testing.
Participate in peer discussion groups
Engage with peers to share knowledge, clarify concepts, and gain diverse perspectives on statistical concepts.
Show steps
  • Join online forums or discussion groups related to the course topics.
  • Actively participate in discussions, asking and answering questions.
  • Collaborate with peers on projects or assignments.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on data analysis techniques
Gain hands-on experience in applying statistical techniques to real-world datasets, deepening your understanding of their practical applications.
Browse courses on Data Analysis
Show steps
  • Identify online tutorials or courses on data analysis techniques relevant to the course.
  • Follow the tutorials step-by-step, working through examples and exercises.
  • Apply the techniques learned to analyze a small dataset of your own.
Solve practice problems on statistical tests
Enhance your problem-solving skills and strengthen your understanding of the different statistical tests covered in the course.
Browse courses on Hypothesis Testing
Show steps
  • Find practice problems or online quizzes on statistical tests.
  • Attempt the problems and check your answers against provided solutions.
  • Identify areas where you need further practice and focus on those concepts.
Contribute to an open-source statistical software project
Gain hands-on experience in the development and application of statistical software, enhancing your technical skills and contributing to the open-source community.
Browse courses on Open Source
Show steps
  • Identify an open-source statistical software project that aligns with your interests.
  • 熟悉项目文档和代码库。
  • Identify a bug or feature that you can contribute to.
  • Implement the fix or feature and submit a pull request.
Create a data visualization dashboard
Develop your data visualization skills by creating an interactive dashboard that communicates statistical insights effectively.
Browse courses on Data Visualization
Show steps
  • Choose a relevant dataset and identify the key insights you want to convey.
  • Select appropriate visualization techniques and tools to create the dashboard.
  • Design and implement the dashboard, ensuring it is user-friendly and informative.
Develop a statistical model for a real-world problem
Apply your statistical knowledge to solve a real-world problem, putting your skills into practice and developing a deeper understanding of the subject matter.
Browse courses on Statistical Modeling
Show steps
  • Identify a problem that can be addressed using statistical modeling.
  • Collect and clean the necessary data.
  • Develop and train a statistical model using appropriate techniques.
  • Evaluate the performance of the model and make adjustments as needed.
  • Present your findings and insights.

Career center

Learners who complete Introducción a Ciencia de Datos en Salud will develop knowledge and skills that may be useful to these careers:
Statistician
A Statistician collects, analyzes, interprets, and presents data. They use statistical methods to develop models, test hypotheses, and make predictions. This course would be beneficial for aspiring Statisticians, as it provides a strong foundation in statistical analysis and interpretation. The course covers topics such as data classification, statistical tests, and data visualization, which are all essential skills for a Statistician.
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course would provide a foundation in data analysis and interpretation, which are essential skills for a Data Scientist. The course covers topics such as data classification, statistical analysis, data visualization, and hypothesis testing, all of which are relevant to the field of data science.
Epidemiologist
An Epidemiologist investigates the distribution and patterns of health events and diseases in a population. They use their findings to develop and implement public health programs. This course would be helpful for aspiring Epidemiologists, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of epidemiology.
Medical Researcher
A Medical Researcher designs and conducts studies to investigate the causes and treatments of diseases. They use data to analyze results and draw conclusions. This course would be helpful for aspiring Medical Researchers, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of medical research.
Health Informatics Specialist
A Health Informatics Specialist uses data to improve the quality and efficiency of healthcare delivery. This course would provide a foundation in data analysis and interpretation, which are essential skills for a Health Informatics Specialist. The course covers topics such as data classification, statistical analysis, and data visualization, all of which are relevant to the field of health informatics.
Clinical Data Manager
A Clinical Data Manager is responsible for the collection, management, and analysis of clinical data. This course would provide a foundation in data analysis and interpretation, which are essential skills for a Clinical Data Manager. The course covers topics such as data classification, statistical analysis, and data visualization, all of which are relevant to the field of clinical data management.
Health Policy Analyst
A Health Policy Analyst analyzes and interprets data to develop and evaluate health policies. They use their findings to make recommendations to policymakers. This course would be beneficial for aspiring Health Policy Analysts, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of health policy analysis.
Quality Improvement Specialist
A Quality Improvement Specialist develops and implements programs to improve the quality of healthcare services. They use data to track progress and identify areas for improvement. This course would be beneficial for aspiring Quality Improvement Specialists, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of quality improvement.
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. They use their findings to make recommendations and improve business outcomes. This course would be useful for aspiring Data Analysts, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of data analytics.
Nurse
A Nurse provides care to patients in a variety of settings. They use data to monitor patient outcomes and identify potential health problems. This course would be useful for aspiring Nurses, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of nursing.
Physician Assistant
A Physician Assistant provides medical care under the supervision of a physician. They use data to make decisions about patient care and monitor patient outcomes. This course would be useful for aspiring Physician Assistants, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of medicine.
Public Health Educator
A Public Health Educator develops and implements programs to promote health and prevent disease. They use data to evaluate the effectiveness of their programs. This course would be useful for aspiring Public Health Educators, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of public health education.
Risk Manager
A Risk Manager identifies and assesses risks to an organization. They use data to develop and implement risk management plans. This course would be useful for aspiring Risk Managers, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of risk management.
Healthcare Administrator
A Healthcare Administrator plans, directs, and coordinates the provision of healthcare services. They use data to make decisions about resource allocation, staffing, and quality of care. This course would be useful for aspiring Healthcare Administrators, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of healthcare administration.
Pharmacist
A Pharmacist dispenses medications and provides advice on their use. They also use data to monitor patient outcomes and identify potential drug interactions. This course would be useful for aspiring Pharmacists, as it provides a foundation in data analysis and interpretation. The course covers topics such as data classification, statistical analysis, and data visualization, which are all relevant to the field of pharmacy.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introducción a Ciencia de Datos en Salud.
Provides a comprehensive overview of statistical methods used in health care research. It covers a wide range of topics, from basic concepts to more advanced techniques. It valuable resource for researchers who need to design and conduct studies, and for clinicians who need to understand the results of research.
Provides a clear and concise introduction to the basic principles of statistics and their application to a healthcare setting. It is well-written and easy to follow, making it a valuable resource for healthcare professionals who need to understand and use statistics in their work.
Provides a clear and concise introduction to the principles of epidemiology. It covers topics such as measures and rates, case-control studies, and cohort studies.
Provides a practical guide to data analysis for health sciences students and researchers. It covers a wide range of topics, from data management to statistical analysis. It valuable resource for anyone who needs to learn how to analyze and interpret health data.
Provides a comprehensive overview of health care ethics. It valuable resource for students and professionals who want to learn more about this field.
Provides an introduction to health care policy and politics in the United States. It covers topics such as the history of health care policy, the role of government in health care, and the challenges facing the health care system.
Provides a comprehensive overview of public health surveillance. It valuable resource for students and professionals who want to learn more about this field.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Introducción a Ciencia de Datos en Salud.
Introducción a Ciencia de Datos en Salud
Most relevant
Estadísticas para la Ciencia de Datos con Python
Most relevant
Análisis exploratorio de datos con Python y R
Most relevant
Estadística Aplicada a los Negocios
Most relevant
Estadística y probabilidad: principios de Inferencia
Most relevant
Análisis estadístico con Excel
Most relevant
Introducción a la ciencia de datos aplicada
Most relevant
Introducción a Ciencias de Datos y Estadística Básica...
Most relevant
Business Analytics
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser