GRA 6445 Introductory Data Science for Marketing

GRA 6445 Introductory Data Science for Marketing

Course code:
GRA 6445
Department:
Economics
Credits:
6
Course coordinator:
Steffen Grønneberg
Course name in Norwegian:
Introductory Data Science for Marketing
Product category:
Master
Portfolio:
MSc - Core course
Semester:
2021 Autumn
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

The course gives the students a detailed overview of statistical inference and basic applications. The basic applications are implemented using Excel and SPSS. The course focuses on fundamental data science issues, on basic probability theory, the logic of hypothesis testing and confidence intervals, and the concept of a statistical model, illustrated via simple and multiple linear regression. A brief introduction to the design of experiments, randomization, and data gathering is given.

Learning outcomes - Knowledge

The students will have a conceptually mature and critical relationship towards statistical inference, knowing some of the conditions where classical statistical inference is justified, as well as what can go wrong if such assumptions are not met. An introduction to the concept of a statistical model and the data reduction this entails is given, and the students will be given a practical and critical introduction to the use of such models. Basic probability theory is developed, and the students will know how to do calculations with basic population quantities, such as expectations of sums of random variables. The students will know the logic of a statistical hypothesis test from a basic decision theoretic perspective, including analysis of power in the simplest possible cases, as well as critical issues such as common abuses of statistical tests. The students will know the important difference between randomized experiments on the one hand, and observational studies on the other, and have an introductory overview of the problems in analysing observational data, including the problems surrounding causality.

Learning outcomes - Skills

Training in Excel and SPSS will be given during the course, and the students will learn basic data manipulation and applied statistical methods using software. Skills in mathematical reasoning and probability calculations will be developed via the introduction of basic probability theory, and the calculations involved in developing basic statistical inference. A more abstract skill that the course develops is the ability to understand the non-trivial logic of statistical inference procedures, such as hypothesis tests, which is foundational for later courses where statistical methodology is applied and developed.

General Competence

The student will receive a critical and mature introduction to basic statistics and data science. Important critical issues surrounding statistical inference will be discussed at a technical level.

Course content
• Univariate descriptive statistics and plots, the normal distribution.
• Bivariate descriptive statistics and plots, correlation and least squares estimation. Tables for categorical data, Simpson’s paradox.
• Causation, randomization, sampling, bias and variability.
• Basic probability rules, random variables, population means and variances, the law of large numbers.
• The sampling distribution of a sample mean, the central limit theorem.
• Confidence intervals and testing under exact normality with a known . Potential problems with tests. Power and inference as a decision.
• Inference for a mean under more realistic conditions.
• The simple linear regression model, and inference.
• The multiple linear regression model, inference and data examples.
• One way ANOVA.
Teaching and learning activities

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Software tools
SPSS

Please note that while attendance is not compulsory in all courses, 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.

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 spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Teaching

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

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Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA64451
ECTS
-
Resit:
Examination when next scheduled course
20No24 Hour(s)Individual Assignment An oral defense of the assignment might be required.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA64452
ECTS
-
Resit:
Examination when next scheduled course
80Yes 3 Hour(s)
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Individual .
Exams:
 Exam category: Submission Form of assessment: Written submission Weight: 20 Invigilation: No Grouping (size): Individual Support materials: Duration: 24 Hour(s) Comment: Assignment An oral defense of the assignment might be required. Exam code: GRA64451 Grading scale: ECTS Resit: Examination when next scheduled course
 Exam category: Submission Form of assessment: Written submission Weight: 80 Invigilation: Yes Grouping (size): Individual Support materials: BI-approved exam calculator Simple calculator Bilingual dictionary Duration: 3 Hour(s) Comment: . Exam code: GRA64452 Grading scale: ECTS Resit: Examination when next scheduled course
Type of Assessment:
Ordinary examination
Total weight:
100
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)