Survival Analysis Part 2: Predicting Time to Event for Lungs Cancer Patients

Let's understand the intuition behind survival analysis concepts and how to implement in detail on real world applications.

Prabhakaran SPrincipal Data Scientist

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Lessons
15, in 3 chapters
Video
1 h 39 min
Certificate
On completion

Course content

3 chapters · 15 lessons · 1 h 39 min

  1. 01Problem Statement and Exploratory Analysis7 lessons · 49 min
    1. Determine if a treatment (drug) has an impact on survival rates9:55
    2. Download Resources1:00
    3. The Kaplan Meier Estimator2:25
    4. Survival rates for different treatments8:40
    5. Log Rank Test to compare Survival rates10:08
    6. Survival rates for Cell Types1:27
    7. Self Assessment Questions15:04
  2. 02Multi Variate Survival4 lessons · 23 min
    1. Multivariate Survival Cox-Ph Model7:33
    2. Course Review1:00
    3. C-index for evaluating Survival models8:29
    4. Estimating Predictive power of variables5:50
  3. 03Accelerated Failure Time (AFT) with XGBoost4 lessons · 29 min
    1. Accelerated Failure Time (AFT) based modeling15:43
    2. Modeling with logit and XGBoost embeddings3:50
    3. Training XGBoost Embeddings model for Survival Analysis5:06
    4. Further Reading4:36

What you need

  • Basics of Python
  • Foundational knowledge of Data Science
  • High school maths

Who this course is for

  • Data Science Aspirants
  • Data Science Professionals
  • Software/Data engineers interested in quantitative analysis
  • Professionals working with large datasets
  • Data analysts, economists, researchers

Your instructor

Prabhakaran S

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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