GRA 6445 Introductory Data Science for Marketing

GRA 6445 Introductory Data Science for Marketing

Course code: 
GRA 6445
Department: 
Economics
Credits: 
6
Course coordinator: 
David Kreiberg
Course name in Norwegian: 
Introductory Data Science for Marketing
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

The course provides students with a detailed overview of methods for statistical modeling and inference. The course focuses on fundamental data science topics such as descriptive analysis, basic probability theory, parameter estimation, procedures for making inference, and regression analysis. Throughout the course, students will make use of statistical software for data handling tasks and model estimation.

Learning outcomes - Knowledge

Students will learn about essential statistical concepts such as:

  • Data types and methods for describing the data.
  • Probability models, random variables and their characteristics, probability distributions, and linear combinations of random variables.
  • Random sampling, sampling distribution, the Central limit theorem, and the data collecting process.
  • Population parameters versus sample statistics, estimation, properties of estimators, and the law of large numbers.
  • Statistical inference, including assumptions and the consequences of violating these assumptions.
  • Data reduction and statistical models.
Learning outcomes - Skills

Students will develop important skills that allow them to:

  • Analyze and describe data using methods for descriptive analysis.
  • Describe stochastic phenomena using probability models and to perform simple probability calculations.
  • Apply methods for parameter estimation.
  • Make inference about the value of a parameter, including interpreting and presenting the results.
  • Perform data handling tasks and statistical modeling using statistical software.
General Competence

The student will receive a critical and mature introduction to basic statistics and data science, which will enable students to apply statistical methods for solving real-world problems.

Course content
  • Univariate and bivariate descriptive statistics and plots.
  • Causation, randomization, sampling, bias and variability.
  • Probability models, random variables, characteristics of random variables, and linear combinations of random variables.
  • The distribution of the sample mean and the central limit theorem.
  • Parameter estimation.
  • Inference under exact normality and inference under more realistic conditions.
  • Linear regression analysis.
  • One-way ANOVA.
Teaching and learning activities

.

Software tools
SPSS
Additional information

Although attendance is not compulsory, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to get a grade in the course.

The examination for this course has been changed from autumn 2023. It is not possible to resit the old version of the examination.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 64453
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
40No72 Hour(s)Group (1 - 3)Assignment. An oral defense of the assignment might be required.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 64454
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
60Yes 3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual .
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:No
Grouping (size):Group (1-3)
Support materials:
Duration:72 Hour(s)
Comment:Assignment. An oral defense of the assignment might be required.
Exam code:GRA 64453
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:60
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 3 Hour(s)
Comment:.
Exam code:GRA 64454
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Lectures
Teaching
6 Hour(s)
Project-work under supervision in a classroom.
Examination
5 Hour(s)
Home exam
Examination
3 Hour(s)
Final exam
Student's own work with learning resources
110 Hour(s)
Sum workload: 
160

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.