GRA 4110 Applied Data Analytics
GRA 4110 Applied Data Analytics
This course gives an applied introduction to the most important techniques in business-related data analytics. Students are given hands-on experience with programming, working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. Programming, mathematical theory and applications will be interwoven in application focused projects.
Central theory surrounding regression models will be developed. The students will learn applied data analytics and programming using the R software system. Skills in working with high precision and technical material will be developed.
The student will be trained in the extremely flexible R system to do applied data analysis. This includes vectorial data operations, loops, data importation and reorganization, the use of visualization techniques, and some general programming, as well as using descriptive statistics, calling upon and implementing some statistical procedures, as well as writing simple simulation experiments.
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 will be developed. 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.
Skills in technical and mathematical understanding will also be developed, mainly through work on mathematical problems related to the applied projects, focusing on the interplay between arithmetical rules, data access, and implementation of computer algorithms.
Through experience in model building, computer experiments, and an understanding of the underlying statistical arguments used in practical analyses, 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. Further, simulation and related techniques will be introduced in order to assess the validity and quality of a statistical technique.
- Introduction to sums and summation notation, and other foundational issues.
- Introduction to R. Introductory descriptive statistics, data visualization and data re-organization. Data exploration and visualization in R
- A review of statistical inference and large sample inference using simulation-experiments.
- An introduction to data-modelling: Simple regression models and an introduction to simulation.
- Multiple linear regression: Dummy-variables, interaction terms, data-transformations and interpretation.
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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.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
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| Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group/Individual (1 - 3) Duration: 1 Week(s) Exam code: GRA 41101 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) Exam code: GRA 41102 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, including time for project work (blended learning). |
Teaching | 12 Hour(s) | Project work under supervision, in class rooms |
Examination | 15 Hour(s) | Work with home exam |
Examination | 3 Hour(s) | Final exam |
Student's own work with learning resources | 94 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.