Single-Course Engelsk 5 ECTS

Introduction to Business Analytics

Overall Course Objectives

Business Analytics is about exploring and analysing data to gain insight into past business performance in order to guide future business planning.

This course introduces the portfolio of data science tasks and techniques necessary for exploring, manipulating, visualizing and analysing data (descriptive analytics), as well as for building prediction models using machine learning (predictive analytics) that can be used to gain insights and support decisions (prescriptive analytics). It is designed with Business Analytics students in mind (i.e. some Python programming background is required), particularly – but not exclusively – those related to studies on mobility and logistics and business analytics. Therefore, it contains a strong hands-on component, with specific real-world cases coming mainly from mobility and transportation.

The course also includes an introduction to data wrangling, problem formulation, and the basic suite of machine learning algorithms.

See course description in Danish

Design and implement Python-based data analysis workflows, ensuring reproducibility and efficiency
Formulate and operationalize business-related problems as data-driven analytical tasks
Select and apply appropriate data structures, algorithms, and analytical techniques to address complex business problems
Analyse, interpret, and synthesize results from descriptive and predictive models into actionable business insights and recommendations
Develop and evaluate visualizations that reveal complex temporal and spatial patterns for decision-making
Design, train, and validate statistical and machine learning models using appropriate evaluation strategies
Critically assess data science methodologies, model performance, and underlying assumptions, including bias, variance, and overfitting
Explain and critically reflect on key data mining and machine learning concepts in practical and business contexts

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 and management.

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).