DSK810: Data Driven Decision Making

Internal Course Code

DSK810

Entry requirements

The course cannot be taken by students enrolled in the master programme in Computer Science
The course cannot be chosen if you have passed, registered, or have followed DS810, or if DS810 is a constituent part of your Curriculum.

Academic preconditions

Students taking the course are expected to:

follow a course on basic introductory statistics in parallel or to have prior knowledge of basic introductory statistics. Prior knowledge to particular software packages is not a prerequisite for being enrolled.

Course introduction

Big data and machine learning based analytics has emerged as the driving force behind critical business decisions and generally its role is growing within the characterization and understanding of individuals and of firms’ behavior. Advances in our ability to collect, store, and process different kinds of data from traditionally unconnected and unstructured sources enables us to answer massively complex, data-driven questions in ways that have never been possible before.

The main purpose of this course is to prepare students to make sense of real-world phenomena and everyday activities by synthesizing and analyzing data by uncovering relevant patterns, relationships, and trends with the intention of making better informed decisions.

The course will provide the student with knowledge about the central methods related to generating, analyzing and processing data. The students will have the skills to apply these methods to particular, empirical problems. The course will give the students the competence to predict and evaluate expedient practices in related to a wide range of data related problems as for instance:

Businesses can predict future sales results by combining their customers’ preference profiles with website click-stream data, social network interactions, and location data.
Financial institutions now leverage transactional data combined with artificial intelligence models to detect fraudulent activities in real time, significantly reducing the impact of financial fraud on consumers and the banking sector.
Manufacturing firms are implementing predictive maintenance strategies using machine learning and IoT data, minimizing downtime and extending the lifespan of machinery and equipment.
Emergency room physicians are able to reduce time to initial treatment and, as a result, patient mortality, by fusing aggregate patient histories with the results of up to the minute lab tests.
Energy companies are employing predictive analytics to forecast demand and manage renewable energy sources more efficiently, contributing to a more sustainable and reliable energy supply.
With the development of electronic health records, remote treatment, and the ability to share data online, we have an array of new healthcare solutions available. The use of mobile technologies to collect and distribute information might help significantly with the prevention and treatment of disease.

Expected learning outcome

After taking the course, the student should be able to apply quantitative modelling and data analysis techniques to the solution of real world problems in the social sciences, communicate findings, and effectively present results using data visualization techniques.

The student, should after the course be able to:

Competently use machine learning to solve the problems based on data.
Perform clearly articulated and informed decision making based on data driven analytics.
Account for and discuss all phases of working with data and specific methods for generating, processing/analyzing and making informed decisions on the basis of data. The student should be able to generate clear and operable management/policy implications on the basis of these three phases.
Identify and assess data resources relevant in social sciences.
These abilities will be documented through the work with a particular case study, which will include: a) Selecting and applying specific methods relevant for a particular case study; b) Accessing relevant data sources and analyze them; c) Clearly outlining the academic and managerial implications of working with the specific methods to an academic and a practitioner audience.

Content

TThroughout the course, the students combine theoretical knowledge with an extensive project work, where they get hands-on experience in accessing and working with data. The course has two main areas:

Data analysis: Methods to process, analyze and visualize the data
Decision making: Methods to making informed decisions on the basis of the data analysis

Literature

James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. (2021, 2nd edition). Introduction. In: An Introduction to Statistical Learning. Springer Texts in Statistics. Springer, Cham. (can be downloaded free of charge from the SDU Library)
See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn

Tests

Oral exam

EKA

N340150202

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card - Name

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

Additional information

Oral exam without preparation

Indicative number of lessons

45 hours per semester

Teaching Method

Planned lessons:

Total number of planned lessons: 45

Hereof:

Common lessons in classroom/auditorium: 45

Both frontal lectures and training hours are focused on the application of techniques for concurrent programming to concrete problems. The difference is in the teaching method. In frontal lectures, learning will be driven by discussions directed by the teacher, whereas in training hours the students will have to try applying concepts by themselves first. It is expected that activities in class will be split approximately evenly between frontal lectures and training hours.

Other planned teaching activities:

Reading of course material
Reflection on methods and theoretical concepts
Gain familiarity with the programming lanquage R
Using R to obtain and prepare data for analysis
Using R to analyze data

Teacher responsible

Name	E-mail	Department
Arne Feddersen	af@sam.sdu.dk	Institut for Erhverv og Bæredygtighed

Additional teachers

Name	E-mail	Department
Christian Emil Westermann	cew@sam.sdu.dk	Econometrics and Data Science
Christian Møller Dahl	cmd@sam.sdu.dk	Econometrics and Data Science
Surabhi Verma	suv@sam.sdu.dk	Center for Integrerende Innovationsledelse (C*I2M)

Timetable

Kolding

Show full time table

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Registration

NAT

Offered in

Kolding

Recommended course of study

Profile	Education	Semester	Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study.

If a transitional arrangement has been made for a course, it will be stated in the list.

See transitional arrangements for all courses at the Faculty of Science.