0830 Quantitative Methods I
Nurtai Meimanjan, M.Sc., Assoz.Prof. PD Dr. Zehra Eksi-Altay, BSc.MSc.
Weekly hours
Language of instruction
09/29/23 to 10/01/23
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Wednesday 10/11/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 10/11/23 03:30 PM - 04:30 PM TC.5.14
Wednesday 10/18/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 10/18/23 03:30 PM - 04:30 PM TC.5.14
Wednesday 10/25/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 10/25/23 03:30 PM - 04:30 PM TC.5.14
Friday 11/03/23 04:30 PM - 06:30 PM TC.0.10 Audimax
Wednesday 11/08/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 11/15/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 11/15/23 03:30 PM - 04:30 PM TC.5.14
Wednesday 11/22/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 11/22/23 03:30 PM - 04:30 PM TC.5.14
Wednesday 12/06/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 12/06/23 03:30 PM - 04:30 PM TC.5.14
Wednesday 12/13/23 09:00 AM - 11:00 AM D5.0.001
Wednesday 12/13/23 03:30 PM - 04:30 PM TC.5.14
Wednesday 01/17/24 08:30 AM - 11:00 AM TC.0.10 Audimax

Course contents:

  • introduction to the open source programming environment R, R as a calculator, named vectors in R
  • functions of one variable, defining and evaluating functions in R
  • brief introduction to functions of several variables
  • graphs of functions, graphing functions in R
  • special functions and their properties: linear, quadratic, polynomial, power, exponential, logarithmic
  • concatenation and composition of functions, inverse of functions
  • analytical and numerical rootfinding
  • elementary financial mathematics (discounting and compounding, simple annuities): computation and visualization using R
  • elementary matrix algebra and its usage in R
  • systems of linear equations and their representation using matrix algebra
  • analytical and numerical differentiation
  • single and multivariable optimization
Learning outcomes

After completing the course, students should be familiar with basic concepts, methods and tools in mathematics and computing that are necessary for the quantitative analysis of problems in modern business and economics. Moreover, students will have acquired basic programming skills in the open-source computer language R, enabling them to independently conduct simple mathematical analyses.

Attendance requirements

100% physical, emotional, and intellectual participation is strongly recommended. Attendance on practical sessions is mandatory. This means, at most two sessions  can be missed.  Attendance in the main lectures will not be formally checked.

Teaching/learning method(s)

The course will be taught as a lecture accompanied by practicals in small groups (VUE). There will be 8 on-campus lectures with 120 participants. Concerning the practicals, there will be 7 on-campus sessions, starting with the first week (the day of the first main lecture) where students will use their own computers. The main focus of the practical sessions will be to cover the relevant R material and gain computational skills. Additionally,  in order to support students for R programming, regular tutorials will be offered by the tutors.

Students are expected to be active in the class. We also encourage the use of Forum.

Course evaluation consists of four parts:
  1. Midterm exam (20 points) (On campus, will take place on November 3, 2023 at 16:30)  
  2. Final exam (50 points) (On campus, will take place on January 17, 2024 at 08:30)
  3. 7 Homework assignments (10 points in total)
    • Homework assignments will be assessed as individual work
    • For each exercise session we will publish a set of 5 homework problems at Canvas
    • The 35 exercises are categorized into 1-star and 2-star questions
    • Students should solve and submit as many problems as possible every week at Canvas by 11:55 pm on Tuesday. Late submissions will not be accepted; please count with the possibility of technical problems and do not leave your submission to the last minute
    • Each week, each student receives a weekly score between 0 and 5 (inclusive). At the end of the course, we take min(10, S/2.5) points as the result for this part, where S is the sum of the weekly scores. Dividing by 2.5 means that students can skip up to 10 exercises throughout the semester and still get full points. Taking the minimum with 10 is there because the highest achievable score in this part is 10 points
  4. Case study (20 points)
    • 15 points group work to be handed in in written form + 5 points individual interview
    • Any collaborations between different groups will be punished with severe point reductions

The following grading scale applies:

  • 89.01-100.00 - Excellent (1)
  • 78.01-89.00 - Good (2)
  • 67.01-78.00 - Satisfactory (3)
  • 56.01-67.00 -  Sufficient (4)
  • 0.00-56.00 -  Insufficient (5)



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Recommended previous knowledge and skills

Mathematical skills and knowledge at high school level.

Last edited: 2023-06-07