EBA 3400 Programming, data extraction and visualisation

EBA 3400 Programming, data extraction and visualisation

Course code: 
EBA 3400
Department: 
Economics
Credits: 
7.5
Course coordinator: 
Christian Brinch
Product category: 
Bachelor
Portfolio: 
Bachelor of Business Analytics - Programme Courses
Semester: 
2019 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

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.

Learning outcomes - Knowledge

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. 
Learning outcomes - Skills

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. 
General Competence

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.

Course content

The following three topics will be covered simultaneously using applied programming projects, lectures and web-based learning.

  1. Basics of Python
  2. 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).
  3. Basic statistics
    • Fundamental theory on data types, data collection, and data quality.
    • Communicating statistical results.
  4. 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.
Teaching and learning activities

The course has 45 hours and will consist of lectures and problem solving using Phyton.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

.

Required prerequisite knowledge

No spesific prerequisites is required, 

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 34001
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
35No 1 Week(s)Group (1 - 3)
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 34001
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
35No1 Week(s)Group (1 - 3)
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
EBA 34001
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
30Yes 2 Hour(s)
  • Bilingual dictionary
Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:35
Invigilation:No
Grouping (size):Group (1-3)
Support materials:
Duration: 1 Week(s)
Comment:
Exam code:EBA 34001
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:35
Invigilation:No
Grouping (size):Group (1-3)
Support materials:
Duration:1 Week(s)
Comment:
Exam code:EBA 34001
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:30
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • Bilingual dictionary
Duration: 2 Hour(s)
Comment:
Exam code:EBA 34001
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Total weight: 
100
Workload activityDurationType of durationComment student effort
Teaching36Hour(s)
Other in classroom36Hour(s)Problemsolvinng in class using Phyton
Self study86Hour(s)
Examination42Hour(s)Two group asssignments and an individual written exam. Approxemately 42 hours in total.
Expected student effort:
Workload activity:Teaching
Duration:36 Hour(s)
Comment:
Workload activity:Other in classroom
Duration:36 Hour(s)
Comment:Problemsolvinng in class using Phyton
Workload activity:Self study
Duration:86 Hour(s)
Comment:
Workload activity:Examination
Duration:42 Hour(s)
Comment:Two group asssignments and an individual written exam. Approxemately 42 hours in total.
Sum workload: 
200

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.