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 testing and visualizing statements about large amounts of data, working in teams on technical problems, and in general improve the students' information literacy through gaining a technical understanding in information processing.
The following three topics will be covered simultaneously using applied programming projects, lectures and web-based learning.
- Basics of Python
- Reading and writing data with Python.
- Accessing sub-sets of a dataset, changing parts of the data, etc.
- Automating tasks in Python (looping, and control structures such as "if, else" etc).
- Basic statistics
- Fundamental theory on data types, data collection, and data quality.
- Communicating statistical results.
- Exploratory analysis
- Computing summary statistics, key numbers, proportions and other descriptive with Python.
- Visualization techniques, including basic statistical plots and their interpretation, scatter-plots, and basic plots for multivariate data.
- The basics of cluster analysis.
The course has 45 hours and will consist of lectures and problem solving using Phyton.
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No spesific prerequisites is required,
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 35 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: Written submission Weight: 35 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: Written submission Invigilation Weight: 30 Grouping: Individual Support materials:
Duration: 2 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 |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Other in classroom | 36 Hour(s) | Problemsolvinng in class using Phyton |
Student's own work with learning resources | 86 Hour(s) | |
Examination | 42 Hour(s) | Two group asssignments and an individual written exam. Approxemately 42 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.