EBA 1180 Mathematics for Data Science
EBA 1180 Mathematics for Data Science
This is a basic course in mathematics. The course is a compulsory part of the Bachelor of Data Science. The course will be taught during the first year over two semesters. It consists of an introductory part in the autumn semester, and an advanced part in the autumn and spring semester.
After completing the course, the student will have;
- Acquired a broad understanding of concepts, methods and theories in mathematics.
- A broad knowledge of algebra, functions in one or several variables, financial mathematics, derivation and integration, and linear algebra.
- Specialized knowledge of how mathematical models and methods can be used in economics and finance.
After completing the course, the student will be able to:
- Analyze quantitative problems using mathematical concepts, and be able to use mathematical methods to solve these problems.
- Assess solution strategies, and be able to carry out the necessary computations correctly and precisely.
- Give mathematical arguments to justify his conclusions, and be able to give written answers that explains the method used and the obtained results to the reader.
- See connections between mathematics and other subjects, especially economics and finance.
- Be able to translate mathematical problems into algorithms and Python code
After completing the course, the student will be able to reflect upon central assumptions for the models and theories used. The student will be able to reflect upon the results obtained, and critically assess if they are reasonable.
- Elementary algebra
- Mathematics of finance and series
- Functions and graphs
- Exponential and logarithmic functions
- Derivation with applications
- Integration with applications
- Linear algebra and matrix algebra
- Functions of several variables
- Optimization in several variables
The course is lectured over one year and consists of an introductory part (36 hours) and an advanced part (48 hours).
For each week, there will be a work program consisting of a problem set and reading assignments. The student must learn the material presented in the reading assignments and work through the problem set. Some of the problems will be reviewed in plenary sessions.
By allocating some time in class to short assignment related to new topics, students will be activated and learning objectives achieved.
Wolfram Alpha is used in lecture to illustrate taught material. Problem sets will include problems the students should solve using python.
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Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Basic knowledge of mathematics equivalent to the admission requirement for the programme.
| Assessments |
|---|
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 40 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: EBA 11805 Grading scale: ECTS Resit: Examination every semester |
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: Second Semester Weight: 60 Grouping: Individual Support materials:
Duration: 5 Hour(s) Comment: Written exam at the end of the spring term. All exams must be passed to obtain a final grade in the course. Exam code: EBA 11806 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 84 Hour(s) | Lectures |
Group work / Assignments | 65 Hour(s) | Problem sessions |
Examination | 8 Hour(s) | Two course papers, multiple choice exam and final exam |
Student's own work with learning resources | 43 Hour(s) | Own work with theory and problems |
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.
All exams must be passed to obtain a final grade in the course.