GRA 6036 Data Analytics with Programming
GRA 6036 Data Analytics with Programming
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. Simulation techniques will be used to assess statistical tools.
Central theory surrounding regression models will be developed. The students will learn applied data analytics and programming using the R software system.
The student will be trained in the extremely flexible R system to do applied data analysis. This includes control-structures, such as if-statements and loops, data importation and reorganization, the use of visualization techniques, programming as well as using descriptive statistics, calling upon and implementing some statistical procedures, as well as writing 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.
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. Further, simulation techniques will be introduced in order to assess the validity of a statistical technique.
- Introduction to R. Introductory descriptive statistics, data visualization and data re-organization. Introductory statistical inference.
- Data exploration and visualization in R.
- An introduction to data-modelling: Simple regression models and an introduction to simulation.
- Multiple linear regression: Dummy-variables, interaction terms, data-transformations and interpretation.
- Regression diagnostics and model selection.
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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 receive a final 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.
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.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group (1 - 3) Duration: 2 Week(s) Exam code: GRA60365 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: Final written examination under supervision Exam code: GRA60366 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 | Lectures and blended learning with projects for students. |
Teaching | 10 Hour(s) | Project-work under supervision in smaller classrooms. |
Examination | 15 Hour(s) | Work related to the home exam. |
Examination | 3 Hour(s) | Final exam |
Student's own work with learning resources | 96 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.