Module also offered within study programmes:
General information:
Name:
Quantitative analysis for managerial decisions (prof. PHILIPPE DE BROUWER)
Course of study:
2019/2020
Code:
AMAT-2-306-MF-s
Faculty of:
Applied Mathematics
Study level:
Second-cycle studies
Specialty:
Financial Mathematics
Field of study:
Mathematics
Semester:
3
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr inż. Dzieża Jerzy (dzieza@agh.edu.pl)
Module summary

Quantitative analysis for managerial decisions

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Skills: he can
M_U001 •make a project: identify the issue, get data, select a method, do the maths and finally present the result. MAT2A_U22, MAT2A_K07, MAT2A_K02, MAT2A_U13, MAT2A_K06, MAT2A_U15, MAT2A_K05 Presentation,
Project
M_U002 •apply this knowledge to •mine data•make informed decisions based on data and facts•identify opportunities of improvement MAT2A_U20, MAT2A_U18, MAT2A_U16 Test,
Oral answer
M_U003 •understand the importance data in decision-making•understand when what method can be used and what their limitations are•understand the limitations of methods and models MAT2A_W11, MAT2A_U20, MAT2A_U12, MAT2A_W12, MAT2A_W09, MAT2A_U21, MAT2A_U11, MAT2A_W10, MAT2A_U16 Test,
Oral answer
Knowledge: he knows and understands
M_W001 •know the basics of statistics and data manipulations•know at least one analytical tool (R)•know the limitations and possibilities of models, tools and visualizations MAT2A_W12, MAT2A_W10, MAT2A_W08 Test,
Oral answer
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
30 0 0 0 0 30 0 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Skills
M_U001 •make a project: identify the issue, get data, select a method, do the maths and finally present the result. - - - - + - - - - - -
M_U002 •apply this knowledge to •mine data•make informed decisions based on data and facts•identify opportunities of improvement - - - - + - - - - - -
M_U003 •understand the importance data in decision-making•understand when what method can be used and what their limitations are•understand the limitations of methods and models - - - - + - - - - - -
Knowledge
M_W001 •know the basics of statistics and data manipulations•know at least one analytical tool (R)•know the limitations and possibilities of models, tools and visualizations - - - - + - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 50 h
Module ECTS credits 2 ECTS
Udział w zajęciach dydaktycznych/praktyka 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 10 h
Realization of independently performed tasks 10 h
Module content
Conversation seminar (30h):

PROF. PHILIPPE DE BROUWER
PHILIPPE@DE-BROUWER.COM

Description

We are at the eve of a new industrial revolution that will be based on information and new technology. Key in this revolution will be the use of data, analytics, machine learning and mathematical modeling. This course aims to provide insight in these developments but it also aims to provide students tools that will help them to be more successful in a corporate environment.

Specific objectives are:

• know the basics of statistics and data manipulations
• know at least one analytical tool®
• know the limitations and possibilities of models, tools and visualizations
• understand the importance data in decision-making
• understand when what method can be used and what their limitations are
• understand the limitations of methods and models
• apply this knowledge to
• mine data
• make informed decisions based on data and facts
• identify opportunities of improvement
• make a project: identify the issue, get data, select a method, do the maths and finally present the result.

Design

PART I: Theory
1. Introduction: economic cycles and the technology of the future
2. Introduction to modelling with R
a) the basics of programming in R
b) levels of measurement
c) selected notions of statistics
a) levels of measurement
b) measures of central tendency
c) measures of variation and spread
d) measures of covariation
e) regression models
f) model performance
g) distributions
h) anova
i) time series analysis
j) decision trees
k) random forest
l) chi square tests
m) bootstrapping

3. Valuation of financial assets and companies
4. Multi Criteria Decision Analysis

PART II: Practice

1. Guided modeling exercises (preparation for the final presentation)
2. Guest speakers from different companies that bring each a specific topic, models and eventually data so students can try the methods learned)
3. project (“ selected assignment”)
1. preparation of a “ board paper” (showing that the student is able to work with data and communicate about it)
2. presentation of the results

Additional information
Teaching methods and techniques:
  • Conversation seminar: Nie określono
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

-

Participation rules in classes:
  • Conversation seminar:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
Method of calculating the final grade:

Grading
10% presence in classroom
40% collaboration in classroom
50% selected assignment

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

The student should report to the teacher in order to determine the individual way of catching up.

Prerequisites and additional requirements:

-

Recommended literature and teaching resources:

Materials
1. the book “Introduction to statistics and data mining with R”
2. the book “Valuation of Financial Assets and companies”
3. the book “Multiple Criteria Decision Analysis”
4. code snippets
5. links to papers and articles

Scientific publications of module course instructors related to the topic of the module:

-

Additional information:

The Selected Assignment (“project”)
A project can be an individual work or a group work (a “group” is one to 4 persons), that uses at least one of the methods learned. The project aims to practice the methods learned, formulate hypotheses, test them and present the results in a convincing way.
During the last lesson each project can be presented in a “15 minutes elevator pitch”. Elevator pitch presentations are assessed as follows
40% for the idea and its viability
30% for the logical structure of the presentation
30% for the presentation itself (quality of slides if used + oratorical qualities)

(start in winter semester 2018/19)