EBA 3400 Programming, Data Extraction and Visualisation
EBA 3400 Programming, Data Extraction and Visualisation
The aim of this course is to equip the students with basic tools in programming, data extraction and visualization of datasets. Using a learning-by-doing approach, we solve basic problems encountered in data science using Python. The course will be using a blended learning approach with a focus on solving practical problems under guidance by teachers. Data examples for business applications will be given.
During the course students shall:
- Learn basic data analytics, and have an overview of the exploratory phase of an empirical investigation.
- Learn how to translate practical problems into Python code, and the possibilities that programming gives the data analyst.
After completed course students will be able to:
- Perform basic data exploration and visualization tasks.
- Automate analyses that would otherwise be impossible to perform manually. Importantly, the course gives basic skills in Python programming.
- Communicate the result of an empirical investigation based on the tools introduced in the course.
The course will strengthen the analytical abilities of the students, and give them tools to test their logic through writing computer programs. The course will further improve the students' abilities in central 21st century skills, such as enabling critical thinking through visualizing statements about large amounts of data, working on technical problems, and in general improve the students' information literacy through gaining a technical understanding in information processing.
- Basics of Python
- Understand variables and data types.
- Use statements for automated tasks (conditionals and loops).
- Write functions.
- Data extraction and exploratory analysis
- Read and write data with Python.
- Access a sub-set of a dataset, changing parts of the data, etc.
- Analyze data quality.
- Compute summary statistics and interpret the results.
- Visualization techniques
- Use univariate plots and multivariate plots to visualize data.
- Interpret the results of the plots.
The course has 42 hours and will consist of lectures and problem solving using Python.
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Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
No specific prerequisites are required.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Individual Duration: 3 Hour(s) Exam code: EBA 34001 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 30 Grouping: Group (1 - 3) Duration: 1 Week(s) Exam code: EBA 34001 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Structured test Invigilation Weight: 30 Grouping: Individual Support materials:
Duration: 45 Minute(s) Exam code: EBA 34001 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 42 Hour(s) | |
Feedback activities and counselling | 36 Hour(s) | |
Student's own work with learning resources | 86 Hour(s) | |
Examination | 36 Hour(s) | One group asssignment and and two individual written exams. Approxemately 36 hours in total. |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.