GRA 6445 Introductory Data Science for Marketing
GRA 6445 Introductory Data Science for Marketing
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.
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.
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.
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.
- 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.
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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.
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.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group (1 - 3) Duration: 72 Hour(s) Exam code: GRA 64453 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: . Exam code: GRA 64454 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
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) |
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.
An oral defense of the assignment might be required.