Specialized Models: Time Series and Survival Analysis
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Approx. 11 hours to complete
Course Summary
This course covers the basics of time series analysis and survival analysis. You will learn how to identify patterns and trends in time series data, and how to use survival analysis to model the probability of an event occurring over time.Key Learning Points
- Understand the basics of time series analysis and survival analysis
- Learn how to identify patterns and trends in time series data
- Use survival analysis to model the probability of an event occurring over time
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453 - $97,054
- India: ₹394,739 - ₹1,218,501
- Spain: €21,000 - €40,000
- Data Scientist
- USA: $85,000 - $134,000
- India: ₹600,000 - ₹1,800,000
- Spain: €30,000 - €60,000
- Actuary
- USA: $64,000 - $114,000
- India: ₹703,000 - ₹2,500,000
- Spain: €36,000 - €70,000
Related Topics for further study
Learning Outcomes
- Understand the basics of time series analysis and survival analysis
- Identify patterns and trends in time series data
- Use survival analysis to model the probability of an event occurring over time
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with programming concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Assignments and quizzes
Similar Courses
- Applied Time Series Analysis
- Survival Analysis
- Introduction to Probability and Data with R
Related Education Paths
Notable People in This Field
- Nate Silver
- Hadley Wickham
- Andrew Ng
Related Books
Description
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning.
Outline
- Introduction to Time Series Analysis
- Course Introduction
- Introduction to Forecasting and Time Series Analysis
- Pandas Time Series Notebook - Part 1
- Pandas Time Series Notebook - Part 2
- Pandas Time Series Notebook - Part 3
- Pandas Time Series Notebook - Part 4
- Time Series Decomposition
- Decomposition Models
- Decomposition Notebook - Part 1
- Decomposition Notebook - Part 2
- Time Series Demo (Activity)
- Time Series Decomposition Demo (Activity)
- Summary/Review
- Check for Understanding
- Check for Understanding
- End of Module Quiz
- Stationarity and Time Series Smoothing
- Stationarity and Autocorrelation
- Stationarity Notebook - Part 1
- Stationarity Notebook - Part 2
- Stationarity Notebook - Part 3
- Nonstationarity Examples
- Identifying Nonstationarity
- Common Transformations
- Time Series Smoothing
- Smoothing Moving Averages
- Smoothing Exponential Intro
- Advanced Smoothing
- Smoothing Notebook - Part 1
- Smoothing Notebook - Part 2
- Stationarity Demo (Activity)
- Time Series Smoothing Demo (Activity)
- Summary/Review
- Check for Understanding
- Check for Understanding
- End of Module Quiz
- ARMA and ARIMA Models
- Autoregressive Models and Moving Average Models
- Useful Plots
- ARMA Models Notebook - Part 1
- ARMA Models Notebook - Part 2
- ARIMA and SARIMA Models
- SARIMA Prophet Notebook - Part 1
- SARIMA Prophet Notebook - Part 2
- SARIMA Prophet Notebook - Part 3
- SARIMA Prophet Notebook - Part 4
- ARMA Models Demo (Activity)
- SARIMA Prophet Demo (Activity)
- Summary/Review
- Check for Understanding
- Check for Understanding
- End of Module Quiz
- Deep Learning and Survival Analysis Forecasts
- Deep Learning - Part 1
- Deep Learning - Part 2
- Deep Learning - Part 3
- Deep Learning Notebook - Part 1
- Deep Learning Notebook - Part 2
- Survival Analysis and Censoring - Part 1
- Survival Analysis and Censoring - Part 2
- Survival Analysis Notebook
- Deep Learning for Forecasting Demo (Activity)
- Survival Analysis Demo (Activity)
- Summary/Review
- Check for Understanding
- Check for Understanding
- End of Module Quiz
Summary of User Reviews
This course on time series and survival analysis has received positive reviews from many users. The course is well-structured and easy to follow, making it accessible to learners of all levels. One key aspect that many users appreciated was the practical application of the concepts learned in the course.Pros from User Reviews
- Well-structured and easy to follow
- Practical application of concepts
- Engaging and knowledgeable instructor
- Interactive exercises and quizzes
- Useful real-world examples
Cons from User Reviews
- Some users found the course to be too basic
- Lack of in-depth coverage on certain topics
- Limited discussion on the mathematical aspects of the concepts
- Not enough focus on time series analysis
- No hands-on projects or assignments
Mark J Grover
4.5 Raiting
Excel 2016 PowerPivot, PowerQuery, PowerView & BI
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Enter the sizzling hot world of BI, Power Pivot, DAX, and big data. Excel 2016 Power Tools are a total game changer!...
Excel 2013 PowerPivot & Advanced Business Intelligence Tools
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Enter the very hot world of BI, Power Pivot, DAX, and big data. This investment of your time is a no brainer....
Excel 2010 PowerPivot & Advanced Business Intelligence Tools
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Enter the very hot world of BI, Power Pivot, DAX, and big data. This investment of your time is a no brainer....
Children's Human Rights - An Interdisciplinary Introduction
- 4.7
-
![]()
![]()
![]()
![]()
![]()
Successful international strategies and programs promoting children’s rights will be highlighted, as well as the role of key actors involved in international organizations working in this field....
