Regression and Time Series Analysis

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

  • Level: intermediate
  • Duration: 2-day course
  • Delivered: in-house

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
  • Logistic regression and generalised linear models
  • Time series analysis: AR, MA, ARMA and ARIMA models

Languages and libraries

  • Python 3
  • Numpy and Pandas for data manipulation
  • scikit-learn and statsmodel for linear and time series models
  • matplotlib for visualisation


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


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

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