Syllabus

Title
0187 Quantitative Methods I
Instructors
Jana Hlavinova, Ph.D.
Contact details
Type
VUE
Weekly hours
2
Language of instruction
Englisch
Registration
09/30/22 to 10/02/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/12/22 09:00 AM - 11:00 AM TC.2.01
Wednesday 10/12/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 10/19/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 10/19/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 11/02/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 11/02/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 11/09/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 11/09/22 01:30 PM - 03:30 PM TC.0.02
Wednesday 11/16/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 11/16/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 11/30/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 11/30/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 12/07/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 12/07/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 12/14/22 09:30 AM - 11:00 AM TC.2.01
Wednesday 12/14/22 04:30 PM - 05:30 PM TC.5.14
Wednesday 01/11/23 09:30 AM - 11:00 AM TC.2.01
Wednesday 01/18/23 09:30 AM - 12:00 PM TC.0.10 Audimax
Contents

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 (VÜ). There will be 9 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 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 at Learn.

Assessment
Course evaluation consists of four parts:
  1. Midterm exam (20 points) (On campus, will take place on November 9, 2022 at 13:30)  
  2. Final exam (50 points) (On campus, will take place on January 18, 2023 at 9:30)
  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 Learn@WU as a Learning Activity
    • 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 Learn@WU 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
    • Release date: end of November. Deadline: 11.01.2023.
    • 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)

 

Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Recommended previous knowledge and skills

Mathematical skills and knowledge at high school level.

Last edited: 2022-10-05



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