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Survival Analysis Courses

Survival analysis courses can help you learn techniques for estimating life expectancy, analyzing time-to-event data, and understanding hazard functions. You can build skills in censoring techniques, Kaplan-Meier estimators, and Cox proportional hazards modeling. Many courses introduce tools like R and Python for statistical computing, along with software for visualizing survival curves and interpreting results in various contexts, such as healthcare and engineering.


Popular Survival Analysis Courses and Certifications


  • I

    Imperial College London

    Survival Analysis in R for Public Health

    Skills you'll gain: Biostatistics, Statistical Analysis, R (Software), R Programming, Statistical Methods, Data Wrangling, Regression Analysis, Exploratory Data Analysis, Time Series Analysis and Forecasting, Logistic Regression, Probability & Statistics, Descriptive Statistics, Model Evaluation, Public Health

    4.5
    Rating, 4.5 out of 5 stars
    ·
    331 reviews

    Intermediate · Course · 1 - 4 Weeks

  • I

    IBM

    Specialized Models: Time Series and Survival Analysis

    Skills you'll gain: Time Series Analysis and Forecasting, Deep Learning, Statistical Analysis, Predictive Modeling, Statistical Methods, Forecasting, Data Preprocessing, Jupyter, Applied Machine Learning, Data Transformation, Model Evaluation, Pandas (Python Package), Unsupervised Learning, Dimensionality Reduction

    4.5
    Rating, 4.5 out of 5 stars
    ·
    139 reviews

    Intermediate · Course · 1 - 4 Weeks

  • I

    Imperial College London

    Statistical Analysis with R for Public Health

    Skills you'll gain: Logistic Regression, Analytical Skills, Correlation Analysis, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Statistics, Statistical Modeling, Biostatistics, Model Evaluation, Exploratory Data Analysis, Statistical Analysis, R (Software), Statistics, Statistical Methods, Data Wrangling, Public Health

    4.7
    Rating, 4.7 out of 5 stars
    ·
    2.1K reviews

    Beginner · Specialization · 3 - 6 Months

  • S

    SkillUp

    Introduction to Healthcare Data Analytics

    Skills you'll gain: Data Storytelling, Data Visualization, Statistical Hypothesis Testing, Data Visualization Software, Interactive Data Visualization, Data Presentation, Health Informatics, Data Analysis, Healthcare Ethics, Technical Communication, Supervised Learning, Looker (Software), Statistical Analysis, Statistics, Predictive Analytics, Health Care, Healthcare Industry Knowledge, Data Literacy, Health Technology, Regression Analysis

    Intermediate · Specialization · 1 - 3 Months

  • W

    Wesleyan University

    Regression Modeling in Practice

    Skills you'll gain: Regression Analysis, Logistic Regression, Statistical Methods, Statistical Analysis, Statistical Modeling, Data Analysis, Correlation Analysis, Analysis, SAS (Software), Scatter Plots, Predictive Modeling, Model Evaluation, Data Manipulation, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    274 reviews

    Mixed · Course · 1 - 4 Weeks

  • T

    The State University of New York

    Practical Time Series Analysis

    Skills you'll gain: Time Series Analysis and Forecasting, Forecasting, R Programming, Statistical Analysis, Model Evaluation, Data Analysis, Data Visualization, Mathematical Modeling, Statistical Modeling, Correlation Analysis, Probability & Statistics, Descriptive Statistics, Statistical Inference, Software Installation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    1.7K reviews

    Intermediate · Course · 1 - 3 Months

What brings you to Coursera today?

  • C

    Coursera

    Analyze Healthcare Data with Confidence

    Skills you'll gain: Statistical Inference, Statistical Analysis, Data Literacy, Probability & Statistics, Estimation, Correlation Analysis

    Beginner · Course · 1 - 4 Weeks

  • M

    Microsoft

    Preparing Data for Analysis with Microsoft Excel

    Skills you'll gain: Excel Formulas, Microsoft Excel, Power BI, Spreadsheet Software, Timelines, Data Processing, Data Analysis, Data Manipulation, Data Presentation, Data Cleansing

    4.7
    Rating, 4.7 out of 5 stars
    ·
    5.5K reviews

    Beginner · Course · 1 - 4 Weeks

  • J

    Johns Hopkins University

    Multiple Regression Analysis in Public Health

    Skills you'll gain: Biostatistics, Regression Analysis, Logistic Regression, Statistical Methods, Public Health, Probability & Statistics, Statistical Analysis, Statistical Inference, Advanced Analytics, Statistical Modeling, Predictive Modeling, Model Evaluation

    4.8
    Rating, 4.8 out of 5 stars
    ·
    325 reviews

    Beginner · Course · 1 - 4 Weeks

  • U

    University of Michigan

    Logistic Regression and Prediction for Health Data

    Skills you'll gain: Logistic Regression, Model Evaluation, Statistical Inference, Predictive Analytics, R Programming, Statistical Modeling, Statistical Methods, Biostatistics, Regression Analysis, Statistical Analysis, Statistics, Statistical Hypothesis Testing, Data Analysis

    Intermediate · Course · 1 - 4 Weeks

  • A

    Arizona State University

    Probability, Statistical Inference and Regression Analysis

    Skills you'll gain: Probability & Statistics, Analytical Skills, Exploratory Data Analysis, Estimation, Logistic Regression

    Intermediate · Course · 1 - 3 Months

  • C

    Coursera

    Classify, Document & Validate Healthcare Data

    Skills you'll gain: Health Information Management, Electronic Medical Record, Medical Privacy, Clinical Data Management, Medical Records, Health Insurance Portability And Accountability Act (HIPAA) Compliance, Health Informatics, Data Validation, Data Dictionary, Data Quality, Clinical Documentation, Health Care, Metadata Management, Data Governance, Data Security, Taxonomy, Data Integrity, Regulatory Compliance

    Beginner · Course · 1 - 4 Weeks

1234…768

In summary, here are 10 of our most popular survival analysis courses

  • Survival Analysis in R for Public Health: Imperial College London
  • Specialized Models: Time Series and Survival Analysis: IBM
  • Statistical Analysis with R for Public Health: Imperial College London
  • Introduction to Healthcare Data Analytics: SkillUp
  • Regression Modeling in Practice: Wesleyan University
  • Practical Time Series Analysis: The State University of New York
  • Analyze Healthcare Data with Confidence: Coursera
  • Preparing Data for Analysis with Microsoft Excel: Microsoft
  • Multiple Regression Analysis in Public Health : Johns Hopkins University
  • Logistic Regression and Prediction for Health Data: University of Michigan

Frequently Asked Questions about Survival Analysis

Survival analysis is a statistical approach used to analyze the time until an event occurs, such as death, failure, or any other significant event. It is crucial in various fields, including healthcare, engineering, and social sciences, as it helps researchers and practitioners understand the factors influencing the timing of events. By estimating survival functions and hazard rates, survival analysis provides insights that can inform decision-making and improve outcomes.‎

Careers in survival analysis span multiple industries. Common job titles include biostatistician, data analyst, clinical researcher, and epidemiologist. Professionals in these roles often work with healthcare organizations, pharmaceutical companies, and research institutions, applying survival analysis techniques to study patient outcomes, treatment effectiveness, and risk factors. The demand for skilled analysts in this area continues to grow, making it a promising career path.‎

To excel in survival analysis, you should develop a strong foundation in statistics, particularly in concepts like probability distributions, regression models, and hypothesis testing. Proficiency in statistical software, such as R or Python, is essential for data manipulation and analysis. Additionally, understanding the context of your data‚Äîwhether in healthcare, engineering, or social sciences‚Äîwill enhance your ability to apply survival analysis effectively.‎

Some of the best online courses for survival analysis include Survival Analysis in R for Public Health and Specialized Models: Time Series and Survival Analysis. These courses provide practical skills and theoretical knowledge, allowing learners to apply survival analysis techniques in real-world scenarios.‎

Yes. You can start learning survival analysis on Coursera for free in two ways:

  1. Preview the first module of many survival analysis courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in survival analysis, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn survival analysis, start by enrolling in online courses that cover the fundamentals and applications of the topic. Engage with practical exercises and real-world datasets to reinforce your understanding. Additionally, consider joining online forums or study groups to discuss concepts and share insights with peers. Regular practice and application of techniques will enhance your skills over time.‎

Typical topics covered in survival analysis courses include survival functions, hazard functions, Kaplan-Meier estimators, Cox proportional hazards models, and competing risks. Courses may also explore the application of these concepts in various fields, such as healthcare and engineering, providing a comprehensive understanding of how to analyze time-to-event data.‎

For training and upskilling employees in survival analysis, consider courses like Survival Analysis in R for Public Health and Specialized Models: Time Series and Survival Analysis. These courses are designed to equip professionals with the necessary skills to apply survival analysis techniques effectively in their work environments.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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