4 EC
Semester 1, periode 1
5141QSIC4Y
| Eigenaar | Bachelor Science, Technology & Innovation |
| Coördinator | dr. F.A. Nobrega Santos |
| Onderdeel van | Bachelor Science, Technology & Innovation, jaar 1 |
This course introduces essential quantitative mathematical methods and statistical tools necessary for the Science, Technology, and Innovation (STI) bachelor's program. Students will learn to analyze, interpret, and present data effectively using Python, with a focus on theory and real-world applications aligned with the four fundamental themes (4FTs) in their studies.
The course is structured into weekly modules, starting with foundational statistics, including measures of central tendency, variability, and data visualization techniques. As the course progresses, topics such as calculus, differential equations, and linear algebra will be introduced, providing students with the mathematical tools to tackle complex STI problems.
A key feature of this course is its emphasis on merging theory with practical application. Students will participate in group projects, applying statistical methods to real-world datasets obtained from platforms like Kaggle. Through these projects, students will develop proficiency in extracting, analyzing, and visualizing data, culminating in the ability to produce comprehensive statistical reports and mathematical models.
By the end of the course, students will have gained the quantitative skills necessary to excel in the STI bachelor's program. This includes critically evaluating research, contributing to modeling and data-driven projects, and applying statistical reasoning in their future studies and professional pursuits.
Given the interdisciplinary nature of this course, we will utilize a blend of textbooks, articles, and online resources. The core materials will be accessible through Canvas, supplemented by interactive learning tools provided by Sowiso.
All course-related documents, including the detailed syllabus and practicum materials, will be accessible through Canvas, our central learning platform, on a class-by-class basis.
All course-related documents, including the detailed syllabus and practicum materials, will be accessible through Canvas, our central learning platform, on a class-by-class basis.
Python
Lectures will provide ang on overview of key topics, focusin theoretical understanding and practical applications. The aim is to give students a solid foundation and demonstrate how concepts can be applied in real-world scenarios.
TA Lectures: TA sessions will focus on group projects, allowing students to collaborate and apply what they've learned in a practical context. Additionally, these sessions will offer individual tutoring at the end of the class, providing personalised guidance to address specific questions or challenges.
Individual Exercises and Assignments: Students will complete individual exercises and assignments through the SOWISO platform. These tasks will reinforce the material covered in lectures and TA sessions, providing opportunities for self-paced learning and practice.
Activiteit | Uren | |
Hoorcollege | 26 | |
Tentamen | 6 | |
Vragenuur | 2 | |
Werkcollege | 26 | |
Zelfstudie | 52 | |
Totaal | 112 | (4 EC x 28 uur) |
Aanvullende eisen voor dit vak:
It is recommended for students to bring their laptops to the TA section.
| Onderdeel en weging | Details |
|
Eindcijfer | |
|
0.25 (100%) Tentamen 1 |
Evaluation Breakdown:
Announcement on Canvas: A specific announcement will be posted on Canvas detailing the date, time, and format (e.g., in-person, online review) of the inspection session.
Assignment Information
Individual and Group Work: Assignments will be a mix of individual and group work. Specific instructions will be provided for each assignment on Canvas, clearly indicating whether it should be completed individually or as part of a group.
Feedback: Feedback will be provided for all assignments. For individual assignments, personalized feedback will be given through Canvas, highlighting strengths and areas for improvement. For group assignments, collective feedback will be shared during the TA sessions, where students will have the opportunity to ask questions and discuss the feedback in detail.
Grading: Assignments will be graded, and the grades will contribute to the final course grade. The weight of each assignment will be specified in the course syllabus and on the assignment page in Canvas.
Dit vak hanteert de algemene 'Fraude- en plagiaatregeling' van de UvA. Hier wordt nauwkeurig op gecontroleerd. Bij verdenking van fraude of plagiaat wordt de examencommissie van de opleiding ingeschakeld. Zie de Fraude- en plagiaatregeling van de UvA: http://student.uva.nl
| Weeknummer | Onderwerpen | Studiestof |
|---|---|---|
| 1 | Statistics and Python Reinforcement (Part 1) | Basic statistical tests and parameters: median, mean, standard deviation (STD); Displaying data: whisker plots, error bars. |
| 2 | Statistics and Python Reinforcement (Part 2) | Normal distribution: t-test, z-test. |
| 3 | One Variable Calculus (Part 1) | Differentiation |
| 4 | One Variable Calculus (Part 2) | Integration |
| 5 | One Variable Calculus (Part 3) | Application of differentiation and integration in the context of the 4 Fundamental Themes (4FTs). |
| 6 | Differential Equations | Simple Ordinary Differential Equations (ODEs). |
| 7 | Linear Algebra | Small systems of equations (2x2 to 4x4) and solving via matrices; Vectors in 2 and 3 dimensions; Inner product and cross product; Equations of lines and planes. |
| 8 | Extra Topics | Complex numbers; Basic computation with complex numbers; Complex roots of polynomials; Propositional logic; Displaying data, graphs, and other quantitative objects. |