Specialized Models: Time Series and Survival Analysis

1. Introduction to Time Series Analysis

2. Stationarity and Time Series Smoothing

3. ARMA and ARIMA Models

4. Deep Learning and Survival Analysis Forecasts

This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics.

By the end of this course you should be able to:- Identify common modeling challenges with time series data. 

- Explain how to decompose Time Series data: trend, seasonality, and residuals. 

- Explain how autoregressive, moving average, and ARIMA models work. 

- Understand how to select and implement various Time Series models. 

- Describe hazard and survival modeling approaches. 

- Identify types of problems suitable for survival analysis.