GRA 6445 Introductory Data Science for Marketing
GRA 6445 Introductory Data Science for Marketing
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.
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.
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 nontrivial logic of statistical inference procedures, such as hypothesis tests, which is foundational for later courses where statistical methodology is applied and developed.
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.
 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.

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.
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.
Covid19
Due to the Covid19 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.

Exam category  Weight  Invigilation  Duration  Support materials  Grouping  Comment exam 

Exam category: Submission Form of assessment: Written submission Exam code: GRA64451 Grading scale: ECTS Grading rules:  Resit: Examination when next scheduled course  20  No  24 Hour(s)  Individual  Assignment An oral defense of the assignment might be required.  
Exam category: Submission Form of assessment: Written submission Exam code: GRA64452 Grading scale: ECTS Grading rules:  Resit: Examination when next scheduled course  80  Yes  3 Hour(s) 
 Individual  . 
Activity  Duration  Comment 

Teaching  36 Hour(s)  Lectures 
Teaching  6 Hour(s)  Projectwork 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) 
A course of 1 ECTS credit corresponds to a workload of 2630 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.