42578 Advanced Business Analytics

2024/2025

Course information
Advanced Business Analytics
English
5
MSc
Offered as a single course
General competence course (MSc), Business Analytics
Programme specific course (MSc), Business Analytics
Programme specific course (MSc), Transport and Logistics
Technological specialization course (MSc), Transportation and Logistics
Spring F3B (Fri 13-17)
Campus Lyngby
Lectures, practical laboratories and online learning, i.e. self-learning with online resources (e.g. with iPython notebook).
13 weeks
Evaluation of exercises/reports
The evaluation is based on a group project (with mandatory description of individualized contributions) and a presentation. The grade is based on an overall evaluation of the performance.
7 step scale , internal examiner
42577
Francisco Camara Pereira , Lyngby Campus, Building 358, Ph. (+45) 4525 1496 , camara@dtu.dk
Filipe Rodrigues , Lyngby Campus, Building 358, Ph. (+45) 4525 6530 , rodr@dtu.dk
42 Department of Technology, Management and Economics
At the Studyplanner
This course gives the student an opportunity to prepare a project that may participate in DTU's Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More information http://www.groendyst.dtu.dk/english
General course objectives
Business Analytics (BA) is about exploring and analysing large amounts of data to gain insight into past business performance in order to guide future business planning. This course introduces a portfolio of advanced data-centric methods which cover the three main directions in BA: Descriptive (“what happened?”), predictive (“what will happen?”), and prescriptive (“what should happen?”). The methods will be applied to various business cases with aim to demonstrate how to extract business value from data, provide data-driven decision support along with effective data management principles. In this course, advanced machine learning techniques are used so understanding of data science and machine learning basics is required as well as a good level of programming skills is expected.
Learning objectives
A student who has met the objectives of the course will be able to:
  • Identify business and societal impact opportunities related to effective data utilization
  • Summarize the identifying characteristics of advanced machine learning approaches for descriptive, predictive and prescriptive analytics
  • Select and apply appropriate machine learning (regression, classification, reinforcement learning, clustering) and data management tools (e.g Pandas)
  • Conduct small-scale machine learning experiments and understand related scaling principles
  • Apply one or more explainable AI techniques (e.g SHAP, Lime) in data-driven decision advisory situations
  • Conduct basic analysis of information in a natural language form
  • Understand the technical principles and potential of foundation models (e.g. LLMs)
  • Quantify uncertainties in predictive modelling, through quantile regression and heteroskedasticity model
  • Understand and be able to explain causal vs correlational relationships between variables
  • Be able to provide a clear and informative summary (executive summary) for data-driven analyses and tools, including insights for business and critical questions
Content
The classes are taught in an interactive manner, with theoretical parts, intermingled with practical exercises. The practical exercises are done in Python.

The main topics covered in the course includes web data mining; natural language processing; recommender systems; explainable AI; deep learning; reinforcement learning; spatio-temporal prediction models; ensemble models; survival analysis; prediction uncertainty and causality.
Course literature
Each module corresponds to its own recommended texts, that will be provided in the end of the lecture notes. The mandatory texts shall be publicly available online.
Last updated
02. maj, 2024