DS810: Data Driven Decision Making

Study Board of Science

Teaching language: Danish or English depending on the teacher
EKA: N340054102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340054101
ECTS value: 10

Date of Approval: 08-08-2019


Duration: 1 semester

Version: Approved - active

Entry requirements

The course cannot be taken by students enrolled in the master programme in Computer Science

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 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’ behaviour. 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 mining big 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, analysing and processing big data. The students will have the skills to apply these methods to particular, empirical problems. And the course will give the students the competence to predict and evaluate expedient practices in related to a wide range of big 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.
  • Police and fire departments collaborate with emergency managers to develop more accurate models of automotive and pedestrian traffic by using GPS data from cars, buses, taxis, and mobile phones.
  • 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.
  • Web scraping analytic tools applied to for example twitter can be used to measure real-time international conflict sentiment levels and to measure political tendencies and their movements prior to important elections etc.
  • 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 data mining software to solve the problems based on Big Data.
  • Perform clearly articulated and informed decision making based on Big Data Analytics.
  • Account for and discuss all three phases of working with big data and specific methods for generating, processing/analysing and making informed decisions on the basis of Big Data. The student should be able to generate clear and operable management/policy implications on the basis of these three phases.
  • Identify and assess big data resources relevant in social sciences.
  • These abilities will be documented through the work with a particular case study, which will include:
  • Selecting and applying specific methods relevant for a particular case study.
  • Accessing relevant big data sources and analyze them using specialized software.
  • Clearly outlining the academic and managerial implications of working with the specific methods to an academic and a practitioner audience.

Content

Throughout the course, the students combine theoretical knowledge with an extensive project work, where they get hands-on experience in accessing and working with big data. The course has three main areas:
  • Datafication and data collection: Methods to generate and structure data in an expedient and operable format
  • Data analysis and data visualization: Methods to process, analyze and visualize the data
  • Decision making: Methods to making the right decisions on the basis of the data analysis

Literature

  • Matloff, N. (2011). The Art of R Programming. Book (optional)
  • James, G., Witten, D., Hastie, T., Tibshirani, R (2013). An Introduction to Statistical Learning with Applications in R. Springer (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

N340054102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

10

Additional information

Oral exam without preparation

Indicative number of lessons

45 hours per semester

Teaching Method

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.

Activities during the studyphase:
  • 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
Christian Møller Dahl cmd@sam.sdu.dk Econometrics and Data Science

Additional teachers

Name E-mail Department City
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

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study