Regression and Time Series Analysis using Python

Linear regression, nonlinear regression, auto-regressive models, time series analysis, regularisation and more

LEVEL: INTERMEDIATE

DURATION: 2-DAY COURS

DELIVERED: AT YOUR OFFICE

What you will learn

You will learn the skills you need to develop and evaluate regression models that allow you to make quantitative predictions.

Multivariate linear regression

Ridge and LASSO regularisation

Languages and libraries :

Python 3

Numpy and Pandas for data manipulation

Scikit-learn and statsmodel for linear and time series model

Matplotlib for visualisation

Good knowledge of python, basic understanding of machine learning practice (as taught in Introduction to Data Science)

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 more about the course!

Get in Touch

DAY ONE

Linear Regression
- Multivariate regression using SKLearn
- Outliers, leverage, analysis of diagnostics
- Data transformations
Regularisation: Ridge and Lasso
- The Ridge regression
- The LASSO regression
- Applications and comparisons
Generalised Linear Models
- The Logistic regression
- Use of regularisation
- Applications in industry

DAY TWO

Handling Time Series
- Manipulating time series with Pandas
- Handling trends and seasonalities in time series: understanding lagging
- Autocorrelation function and moving average (MA)
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

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.

Director