EBA 3400 Programming, Data Extraction and Visualisation

EBA 3400 Programming, Data Extraction and Visualisation

Course code: 
EBA 3400
Department: 
Data Science and Analytics
Credits: 
7.5
Course coordinator: 
Wei-Ting Yang
Course name in Norwegian: 
Programming, Data Extraction and Visualisation
Product category: 
Bachelor
Portfolio: 
Bachelor of Data Science for Business - Programme Courses
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Bachelor
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' analytical skills, such as enabling critical thinking through the analysis and visualization of large amounts of data, working on technical problems, and in general improve the students' information literacy through gaining a technical understanding in information processing.

Course content
  1. Python basics
    • Programming environment.
    • Variables and data types.
    • Input and output.
    • Control flow (conditionals and loops).
    • Functions.
  2. Data extraction and visualization
    • Reading and writing data.
    • Data summarization and quality assessment.
    • Subset selection and data manipulation.
    • Data aggregation and statistics computation.
    • Data visualization and interpretation.
Teaching and learning activities

The course consists of 30 hours of synchronous classroom instruction and 15 hours of asynchronous learning. Asynchronous learning activities include exercises, quizzes, and readings.

The Python programming language will be taught in the course.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information
  • Please note that while attendance is not compulsory, it is the student’s own responsibility to obtain any information provided in class.
  • Students wishing to improve their grades may retake the exam at the next scheduled exam.

 

Qualifications

Higher Education Entrance Qualification

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

No specific prerequisites are required.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Handin - all file types
Weight: 
30
Grouping: 
Group (1 - 3)
Duration: 
3 Day(s)
Exam code: 
EBA 34002
Grading scale: 
ECTS
Resit: 
Examination every semester
Exam category: 
Submission
Form of assessment: 
Handin - all file types
Weight: 
70
Grouping: 
Individual
Duration: 
3 Hour(s)
Comment: 
.
Exam code: 
EBA 34003
Grading scale: 
ECTS
Resit: 
Examination every semester
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
30 Hour(s)
Individual problem solving
15 Hour(s)
Asynchronous activities: exercises, quizzes, and readings.
Feedback activities and counselling
15 Hour(s)
Student's own work with learning resources
100 Hour(s)
Examination
40 Hour(s)
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.