GRA 6020 Applied Data Analytics
GRA 6020 Applied Data Analytics
This course gives an introduction to some of the more important tools and techniques used in data analytics for business. Students will obtain hands-on experience working with real data problems, they will learn how to use descriptive statistics to explore data and justify models, and how to use statistical models to turn data into actionable knowledge.
Basic knowledge of formal statistical methodology will be reviewed and applied. Focus will be on various statistical modeling frameworks typically applied in the social sciences. The students will learn how to perform and interpret statistical tests, make confidence intervals, and will acquire understanding and knowledge of the basic theory and motivation underlying and associated with regression type models.
The course focuses on practical data analytics, thereby empowering the student to do independent data analyses on their own.
Skills in data-selection, data-reorganization, data-transformations and descriptive statistics will be developed in connection with data-visualization, model formulation, model diagnostics and model selection.
Skills in choosing and using exploratory tools for getting an overview of large datasets will be developed.
Finally, skills in turning a practical question into a question that can be addressed via statistical tools, and then using statistical tools to decide on a course of action for the practical question at hand will be developed. The use of statistical software, such as Excel, R, SPSS and Stata, is an essential element in modern statistical analysis. Skills in the practical use of such programs will therefore be developed.
Through experience in model building and computer experiments, the student will reflect on the limitations of statistical techniques, the issue of subjectivity in reaching statistical conclusions, and the level of trust one may place in statistically based decisions.
- Introductory descriptive statistics, data visualization and data re-organization.
- Data exploration.
- Introductory statistical inference.
- An introduction to various statistical modeling frameworks, such as multiple linear regression analysis (including ANOVA), factor analysis and other multivariate modeling techniques of interest.
- Diagnostics and model selection.
One of several of the software tools mentioned are relevant for the course.
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 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.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group (1 - 3) Duration: 14 Day(s) Comment: . Exam code: GRA 60207 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: Written examination under supervision. Exam code: GRA 60208 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
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.