Facts about the course
- ECTS Credits:
- 2.5
- Responsible department:
- Faculty of Logistics
- Course Leader:
- Joao Carlos Amaro Ferreira
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- 1 week
LOG904-181 Data-Based Decision - Data science course in Python (Autumn 2022)
About the course
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 of Science in Logistics
- Experience-based Master in Logistics
- Master of Science in Sustainable Energy Logistics
- Exchange programme - Master's level
- Master of Science in Sustainable Transport and Urban Mobility
Recommended requirements
No specific background needed. We will start from zero.
The student's learning outcomes after completing the course
-
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.
Examination
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
Syllabus
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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
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Algorithms and evaluation – Regression problems, Naïve Bayes, Random Forest, Algorithms K-means, KNN, SVM
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Data problem solve (hands-on) – A case to go through - Introduction to neural networks