Introduction to Business Analytics

• Run Python scripts that load and analyses small/medium-sized datasets
• Convert a raw dataset into an actionable form to solve a concrete problem
• Apply basic data structures and algorithms to manipulate data
• Relate available problems and data in a mobility/business analytics context with techniques to tackle them
• Extract and analyse insights from the application of methods for descriptive and predictive analytics
• Visualize and deconstruct complex temporal and spatial patterns
• Critically evaluate the results of a data sciences analysis and recommend actions from an operational point of view
• Appropriately train and test a statistical model to answer a problem
• Explain important data mining concepts, such as overfitting, bias, regularisation, etc

The classes are taught in an interactive manner, with theoretical parts, intermingled with practice with Jupyter Notebooks.

Main topics are: data visualisation, forecasting and regression models, classification, clustering, dimensionality reduction and time-series. The methods will be exemplified through different cases within e.g. transportation, management and marketing.

Introduction to Python programming and Pandas is provided as supplementary material (requirement of the course/self learning)

02402/02403/02105, Introductory courses in statistics are very important.

Lectures and practical laboratories (e.g. with iPython notebook).