Regression and Time Series Analysis using Python
Linear regression, nonlinear regression, auto-regressive models, time series analysis, regularisation and more
You will learn the skills you need to develop and evaluate regression models that allow you to make quantitative predictions.
Languages and libraries :
PREREQUISITES
Good knowledge of python, basic understanding of machine learning practice (as taught in Introduction to Data Science)
AUDIENCE
Those who wish to take their data science skills further and learn state-of-the-art techniques in this constantly evolving field.
Get in touch with us to learn about the course
DAY ONE
REGRESSION ANALYSIS
Session 1
Linear regression
- Multivariate regression using SKLearn
- Outliers, leverage, analysis of diagnostics
- Data transformations
Session 2
Regularisation: Ridge and LASSO
- The Ridge regression
- The LASSO regression
- Applications and comparisons
Session 3
Generalised linear models
- The Logistic regression
- Use of regularisation
- Applications in industry
DAY TWO
TIME SERIES ANALYSIS
Session 1
Handling time series
- Manipulating time series with Pandas
- Handling trends and seasonalities in time series: understanding lagging
- Autocorrelation function and moving average (MA)
Session 2
Time series models
- Building an autoregression (AR)
- Adding structure in a time series model: towards ARMA and ARIMA models
- Modeling real-world time series: how to avoid pitfalls
CONTACT US
We will email you within the next 24 hours to arrange a quick call to help with any questions about the programme and recommend pre-course materials.
We look forward to speaking with you.
Dr. Raoul-Gabriel Urma
Director