Syllabus

Title
1032 Quantitative Methods I
Instructors
Sourav Adhikari, M.Sc., Assoz.Prof. PD Dr. Zehra Eksi-Altay, BSc.MSc.
Contact details
Lectures: Zehra Eksi-Altay (zehra.eksi@wu.ac.at), Exercise Sessions: Sourav Adhikari (sourav.adhikari@wu.ac.at)
  • Type
    VUE
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
10/01/21 to 10/03/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/06/21 10:30 AM - 12:00 PM Online-Einheit
Wednesday 10/13/21 11:15 AM - 12:00 PM Online-Einheit
Wednesday 10/13/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 10/20/21 11:15 AM - 12:00 PM TC.2.02
Wednesday 10/20/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 10/27/21 11:15 AM - 12:00 PM Online-Einheit
Wednesday 10/27/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 10/27/21 05:00 PM - 06:30 PM TC.0.10 Audimax
Wednesday 11/03/21 11:15 AM - 12:00 PM TC.2.02
Wednesday 11/03/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 11/10/21 11:15 AM - 12:00 PM Online-Einheit
Wednesday 11/10/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 11/17/21 11:15 AM - 12:00 PM TC.2.02
Wednesday 11/17/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 12/01/21 11:15 AM - 12:00 PM Online-Einheit
Wednesday 12/01/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 12/15/21 03:30 PM - 04:30 PM Online-Einheit
Wednesday 12/22/21 11:30 AM - 01:30 PM Online-Einheit

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

This course is taught as a main lecture accompanied by practicals. For the main lecture, recorded lecture videos will be released weekly at Learn.

Practicals will be held in the alternating hybrid mode with mandatory attendance. In this mode, each student will attend the practicals on campus every second week. Those who are not in the class will follow the live lecture-cast. Overall, under the alternating hybrid mode, there will be 45 minute in-class teaching together with synchronous streaming. Out of the 7 practical sessions, students are allowed to miss at most 2 sessions without notice.

In order to support students for R programing, regular online or on-site tutorials will be offered by the tutors.

Teaching/learning method(s)

This course is taught as a main lecture accompanied by practicals. For the main lecture, recorded lecture videos will be released weekly at Learn. Practicals will be held in the alternating hybrid mode.

Assessment

Course evaluation consists of four parts:
  1. Midterm exam (20 points) (On campus, TC.0.10 Audimax, will take place on 27.10.2021 at 17:00-18:30)  
  2. Final exam (50 points) (On campus, TC.0.10 Audimax, will take place on 22.12.2021 at 11:30-13:30)
  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/2.1) points as the result for this part, where S is the sum of the weekly scores. Dividing by 2.1 means that students can skip up to 14 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: on 04.11.2021 and to be handed in at 23:55 on 12.12.2021. No late submission will be accepted. Interviews: to be announced
    • 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

1 Author: W. John Braun, Duncan J. Murdoch
Title:

A First Course in Statistical Programming with R


Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
2 Author: Knut Sysdaeter, Peter Hammond
Title:

Essential Mathematics for Economic Analysis


Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book

Recommended previous knowledge and skills

Mathematical skills and knowledge at high school level.

Availability of lecturer(s)

Lectures: Paul Eisenberg (paul.eisenberg@wu.ac.at), Exercise Sessions: Darjus Hosszejni (darjus.hosszejni@wu.ac.at)  

Last edited: 2021-10-05



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