LOG904-181 Data-Based Decision - Data science course in Python (Autumn 2023)

ECTS Credits:
2.5
Responsible faculty:
Avdeling for logistikk
Campus:
Molde
Course Leader:
Arild Hoff
Teaching language:
English
Study level:
{code=HN, name=Second degree level}
Duration:
½ year

Table of contents

Contents

This course has the objective of presenting and discussing the main challenges inherent to data-driven decision making and to the execution of Business Intelligence and Data Science projects. The seminar aims to provide students with knowledge of the main research techniques required to data-driven decision making and will use Python and for data mining and examination of data. The seminar presents an integrated and introductory view of the different areas addressed in the master program of Integrated Business Intelligence Systems.

Please note: This seminar replaces

which is cancelled autumn 2022.

The course is connected to the following study programs

Recommended knowledge

No specific background needed. We will start from zero.

The student's learning outcomes after passing the exam

  • Knowledge: Students will gain knowledge of data analytics and data visualization towards decision support.
  • Skills: Use of Python for data analytics and PowerBI for data visualization
  • General competence: Ability to prepare data for decision support

Forms of teaching and learning

Lectures (three hours per day), exercise, and group work.

Exam

Form of assessment: Written school exam on computers, on Friday

  • Proportion: 40 %
  • Duration: 1,5 hour
  • Grouping: Individual
  • Grading scale: Letter (A - F)
  • Support material:

Form of assessment: Group assignment, deadline on Saturday

  • Proportion: 60 %
  • Grouping: Group work
  • Grading scale: Letter (A - F)

Pensum

  • Introduction to data science – main concepts - Introduction to Python. Data cleaning Process in Python (hands-on)
  • Feature Engineer and Data Balance – Data visualization (hands-on) - Power BI for data visualization and modulation
  • Algorithms and evaluation – Regression problems, Naïve Bayes, Random Forest, Algorithms K-means, KNN, SVM
  • Data problem solve (hands-on) – A case to go through - Introduction to neural networks
Last updated from FS (Felles studentsystem) Oct. 14, 2026 4:40:55 AM