# 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 vectorial data operations, 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.
- A brief introduction to regression diagnostics and model selection.

It is the student’s own responsibility to obtain any information provided in class.

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 |
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Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group (1 - 3) Duration: 2 Week(s) Exam code: GRA 60365 Grading scale: ECTS Resit: Examination when next scheduled course |

Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials: - BI-approved exam calculator
- Simple calculator
- Bilingual dictionary
Duration: 3 Hour(s) Comment: Final written examination under supervision Exam code: GRA 60366 Grading scale: ECTS Resit: Examination when next scheduled course |

All exams must be passed to get a grade in this course.

Activity | Duration | Comment |
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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.