42577 Introduction to Business Analytics

2024/2025

Course information
Introduktion til Business Analytics
English
5
MSc
Offered as a single course
General competence course (MSc), Business Analytics
General competence course (MSc), Civil Engineering
General competence course (MSc), Transportation and Logistics
Polytechnical foundation (MSc), Technology Entreneurship
Programme specific course (MSc), Business Analytics
Programme specific course (MSc), Transport and Logistics
Technological specialization course (MSc), Petroleum Engineering
Technological specialization course (MSc), Technology Entrepreneurship
Technological specialization course (MSc), Transportation and Logistics
Elective course (B Eng), IT and Economics
Elective course (B Eng), Manufacturing and Management
Autumn E1A (Mon 8-12)
Campus Lyngby
Lectures and practical laboratories (e.g. with iPython notebook).
13 weeks
No exam
Evaluation of exercises/reports
The evaluation is based on two written tests (50% of total grade) and one group project (group size of 3-6) with individualized report (50% of total grade). The project is divided into two parts. One part is evaluated using peer grading, the second part by evaluated by the lecturers.
7 step scale , external examiner
42184. 42585
Programming in Python. A minimum of 10 ECTS must be taken among the 8 programming courses: 02002/​02003/​02105/​02110/​02158/​02393/​02614/​02635.
02402/02403/02105 , Introductory courses in statistics are very important.
Filipe Rodrigues , Lyngby Campus, Building 358, Ph. (+45) 4525 6530 , rodr@dtu.dk
Ravi Seshadri , Ph. (+45) 4525 6583 , ravse@dtu.dk
Guido Cantelmo , Ph. (+45) 4525 6582 , guica@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 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), 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 from mobility and business contexts.

The course also includes an introduction to data wrangling, problem formulation, and the basic suite of machine learning algorithms.
Learning objectives
A student who has met the objectives of the course will be able to:
  • 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
Content
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)
Course literature
Each lecture has its own recommended texts, that will be available publicly online. For the student interested in a book to follow, we recommend "The elements of statistical learning", Trevor Hastie, Robert Tibshirani and Jerome H. Friedman "Python for Data Analysis", Wes McKinney
Last updated
02. maj, 2024