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: 
2024 Autumn
Active status: 
Active
Level of study: 
Bachelor
Teaching language: 
English
Course type: 
One semester
Introduction

This course covers basic Python programming and data manipulation and visualization packages, aiming to provide students with fundamental skills for solving practical problems in data science. Using a hands-on learning approach, students will develop their programming skills through a variety of in-class and out-of-class exercises.

Learning outcomes - Knowledge

Throughout the course, students will:

  • Learn how to translate real-world problems into Python code and explore the possibilities of using programming to complete data science tasks.
  • Learn basic data analytics and develop an understanding of the exploratory phase in empirical investigations.
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

This course will train logical thinking by converting ideas into computer programs, thereby improving students' analytical skills. It will develop critical thinking skills by analyzing and extracting information from a wide range of datasets. Overall, the course aims to improve students' information literacy by providing them with a technical understanding of 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 34 hours of synchronous classroom instruction and 11 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: 
Submission other than PDF
Exam/hand-in semester: 
First Semester
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: 
Submission other than PDF
Exam/hand-in semester: 
First Semester
Weight: 
70
Grouping: 
Individual
Duration: 
3 Hour(s)
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
34 Hour(s)
Individual problem solving
11 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.