Syllabus

Title
1144 Quantitative Methods I
Instructors
Assoz.Prof. PD Dr. Zehra Eksi-Altay, BSc.MSc.
Contact details
Lectures and Exercise Session 1: Zehra Eksi-Altay (zehra.eksi@wu.ac.at), Exercise Sessions 2-4: Camilla Damian (camilla.damian@wu.ac.at)
Type
VUE
Weekly hours
2
Language of instruction
Englisch
Registration
10/02/20 to 10/04/20
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/07/20 10:00 AM - 12:00 PM Online-Einheit
Wednesday 10/14/20 11:00 AM - 11:45 AM TC.0.02 Red Bull
Wednesday 10/14/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 10/21/20 11:00 AM - 11:45 AM Online-Einheit
Wednesday 10/21/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 10/28/20 11:00 AM - 11:45 AM TC.0.02 Red Bull
Wednesday 10/28/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 10/28/20 04:00 PM - 05:30 PM Online-Einheit
Wednesday 11/04/20 11:00 AM - 11:45 AM Online-Einheit
Wednesday 11/04/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 11/11/20 11:00 AM - 11:45 AM Online-Einheit
Wednesday 11/11/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 11/18/20 11:00 AM - 11:45 AM Online-Einheit
Wednesday 11/18/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 12/02/20 11:00 AM - 11:45 AM Online-Einheit
Wednesday 12/02/20 01:00 PM - 02:00 PM Online-Einheit
Wednesday 12/09/20 11:00 AM - 11:45 AM Online-Einheit
Wednesday 12/16/20 01:30 PM - 03:30 PM Online-Einheit
Procedure for the course when limited activity on campus

This course is taught as a main lecture accompanied by practicals.  In case of limited activity on campus the main lecture will be held in the distance mode. Accordingly, recorded lecture videos will be released  weekly at Learn during the semester.

Practicals will be held in the synchronous hybrid mode.  The idea of the practicals is to provide students with the opportunity of asking questions and to engage them in class discussions. Due to the limited capacity of rooms, we will apply a rotation rule such that each student can attend the practicals on campus every second week. Those who are not in the class will follow the live (and/or recorded) lecture-cast .   Overall, under the synchronous hybrid mode, there will be 45 minute in-class teaching together with synchronous streaming.  Additionally, we plan to offer online Q&A sessions (through MS Teams) for students who attend the course remotely. 

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

Students are expected to be active in the class (or on online Q&A sessions). Moreover, students are expected to actively contribute to solving the weekly exercises as well as the case studies in their groups of 4 students. We also encourage the use of Forum at Learn.

Class participation is obligatory both online and on campus and can only be suspended or converted to pure online attendance for a valid reason.

The assessment is based on an online midterm exam (worth 20 points) and an online final exam (worth 50 points) which need to be solved individually, and 7 set of weekly exercises  (worth 10 points in total) and a case study (worth 20 points) , which need to be solved in groups of four.  The grading scale is also the same as under plan A.

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 in both the lectures as well as the practical sessions. However, attendance in the lectures will not be formally checked. Note that there will be no chance to make up for any points which were lost due to missing practical sessions.

Teaching/learning method(s)

The course will be taught as a lecture accompanied by practicals in small groups (VÜ). There will be 10 lectures with 120 participants, lasting 90 minutes each. Concerning the practicals, there will be one introductory session (90 minutes, 4 x 30 participants) and 6 further exercise sessions (60 minutes, 4 x 30 participants), where students will use their own computers. Additionally, there will be tutorials held by senior students.

Assessment

Course evaluation consists of four parts:

  1. Midterm exam (20 points) (Online, will take place on 28.10.2020 at 16:00-17:30)  
  2. Final exam (50 points) (Online, will take place on 16.12.2020 at 13:30-15:30)
  3. 7 Homework assignments (10 points in total) 
  • Homework assignments will be assessed as group work,
  • for each exercise session we will distribute a set of 8 homework problems,
  • the 56 exercises are categorized into 1-star (5 ex./week), 2-star (2 ex./week), and 3-star (1 ex./week) questions,
  • the groups should solve as many problems as possible,  indicate which problems they have solved (via the designated learning activity) and      upload their solutions every week on Learn@WU by 11:55 pm on Tuesday,
  • the lecturers grade the weekly performance with a number between 0 (worst) and 1(best) which counts for the whole group, this is      called the weekly multiplier. The multiplier is multiplied by the number of problems submitted that week that produces the weekly score. This score is a number between 0 and 8.  At the end of the course, we take min(12, S/3.5) points as the result for this part, where S is the sum of the weekly scores. Dividing by 3.5 means that groups can skip up to 21 exercises throughout the semester „for free“ (for example, by only submitting the 1-star problems and only receiving weekly multipliers of 1; there are many other ways, too). Taking the minimum with 12 means that there is room for 2 bonus points in the part of the evaluation. Bonus points can be achieved by skipping less than 21 exercises and receiving good multipliers.

      4. Case study (20 points)

  • 15 points group work to be handed in in written form + 5 points individual interview
  • Release date: 19.11.2020,   Due date:13.12.2020,  Interviews:16-17-18th December
  • 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 and Exercise Session 1: Zehra Eksi-Altay (zehra.eksi@wu.ac.at), Exercise Sessions 2-4: Camilla Damian (camilla.damian@wu.ac.at)

Last edited: 2020-09-29



Back