Data Science: introduction

About this course

The course follows the main stages of a data science workflow, from understanding a problem and preparing data through exploratory analysis, visualisation, statistical analysis and interpretation. Participants work with real-world datasets and use Python libraries including pandas, NumPy, matplotlib, seaborn and scikit-learn, alongside the AutoML tool H2O. Teaching combines short lectures, live coding demonstrations, discussions and practical exercises. Topics include feature engineering, regression, classification, model evaluation, data storytelling and ethical considerations. Optional exercises and mini projects are available for further study outside class.

What you'll learn

• Prepare and clean datasets for analysis
• Conduct exploratory data analysis and create visualisations
• Apply fundamental statistical methods
• Build and evaluate introductory regression and classification models
• Use H2O and other Python tools for predictive modelling
• Communicate analytical findings and discuss ethical issues in data use

Is this course right for you?

The course is intended for beginners who have some familiarity with Python and data handling, as well as highly motivated learners developing these foundations quickly. Previous programming or data manipulation knowledge is advantageous, while early sessions cover basic programming and data analysis.

• Problem-solving and data-driven analysis are central to the course
• Most learning activities take place during class
• Optional exercises and mini projects are available for additional study outside class

Before you go

• The software used is free to download, with guidance on where to find it.
• The provider does not offer advice on software installation issues on students' home hardware.