LOG904-181 Data-Based Decision - Data science course in Python (Autumn 2022)
- 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:
- 1 week
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
- Master in Logistics
- Experience-based Master in Logistics
- Master in Sustainable Energy Logistics
- Exchange programme - Master's level
Recommended knowledge
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
Exam
Form of assessment: Written school exam on computers, on Friday
- Proportion: 30 %
- Duration: 1 hour
- Grouping: Individual
- Grading scale: Letter (A - F)
- Support material:
Form of assessment: Written school exam on computers (Hands-on computer problem solving), on Friday
- Proportion: 70 %
- Duration: 2 hours
- Grouping: Group work
- Grading scale: Letter (A - F)
- Support material: using computer with Python and PowerBI
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