Uniwersytet Marii Curie-Skłodowskiej w Lublinie - Centralny System Uwierzytelniania
Strona główna

Quantitative Fundamentals

Informacje ogólne

Kod przedmiotu: E-DS-2S-O.1
Kod Erasmus / ISCED: (brak danych) / (0540) Matematyka i statystyka Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu: Quantitative Fundamentals
Jednostka: Wydział Ekonomiczny
Grupy:
Strona przedmiotu: http://kampus.umcs.pl
Punkty ECTS i inne: 6.00 Podstawowe informacje o zasadach przyporządkowania punktów ECTS:
  • roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS;
  • tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h;
  • 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się;
  • tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS;
  • nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta.
Język prowadzenia: angielski
Wymagania wstępne:

(tylko po angielsku) Requirements in the area of:

• knowledge: shows acquaintance of problems and methods of algebra, mathematical analysis, descriptive statistics, mathematical statistics and basics of macroeconomics, microeconomics and finance

• skills: can perform basic mathematical operations, calculate chosen statistical measures

• competences (attitude): can individually use bibliography as well as prepare information on a selected topic


Godzinowe ekwiwalenty punktów ECTS:

(tylko po angielsku) a) contact hours (with the participation of an academic teacher):


- lecture 15 h./1 ECTS

- seminar 30h/ 2 ECTS


b) non-contact hours (student's own work):


- studying literature 50h/ 1 ECTS


- preparation for the exam 50h/ 2 ECTS


The total number of hours for the course: 145h


The total number of ECTS points for the subject: 6 ECTS

Sposób weryfikacji efektów kształcenia:

(tylko po angielsku) 1. General requirements: Students are requested to complete required readings and prepare for seminars before attending.

2. Lecture attendance: Students have to arrive on time to class, stay the entirety of the class and keep absences to a minimum. I expect to be informed beforehand if you need to miss a class. To encourage this policy, a student who is not present in class more than one time will not be grade for course based on “collection of the points” but based on final exam.

3. Counseling: Individual or small group volunteer access to the counseling (during office hours). It is the responsibility of the student to seek help and ask questions when concepts presented in lecture or the textbook are not clear. However, if the student encounters the decline in scores, a counseling meeting may be initiated by the lecturer.

4. Examining during seminars: A series of short exercises are required to make up the total course grade – only for the students who attended the classes (one absence is acceptable). These exercises would be available for students during the whole course: lecture and seminar. Student collects the points which will be given for solving exercises, and at the end of course an appropriate grade would be given. Grades for course are setup according to the following scale:

Points Grade

Below 50 2.0 / F (Fail)

50 - 60 3.0 / E (Sufficient)

61 - 70 3.5 / D (Satisfactory)

71 - 80 4.0 / C (Good)

81 - 90 4.5 / B (Very good)

91 - 100 5/ A (Excellent)


Students who fail to collect a sufficient number of points or for those who has more than one absence, can attempt one time to catch up missed exercises (counseling meetings).


5. The final exam may include material from all reading assignments, all lectures, and all assignments. Grades for exam are setup according to the following scale:

% Grade

Below 50 2.0 / F (Fail)

50 - 60 3.0 / E (Sufficient)

61 - 70 3.5 / D (Satisfactory)

71 - 80 4.0 / C (Good)

81 - 90 4.5 / B (Very good)

91 - 100 5.0 / A (Excellent)


Student who gets 2.0 (Fail) as finale course grade can attempt two times to pass the extra final exam, but there will be no makeup of that exam if student receive grade 3.0 (Sufficient) or higher. Cheating is not acceptable in any form. Any evidence of cheating in exams will lead to annulling the grade and disciplinary procedure.

If student is not present for an exam, the missed grade will be dropped from the averaging process. If student miss in excess of one exam, a grade of 2.0 will be recorded for the second missed exam and averaged into the final grade.


5. Course changes: This course syllabus provides a general plan for the course. The instructor reserves the right to make changes to the syllabus; including: assignments (projects), timetable, and examinations, etc., in order to accommodate the needs of the class as a whole and fulfill the goals and objectives of the course. If changes are necessitated during the term of the course, the instructor will immediately notify students of such changes by e-mail communication and/or announcement in class.


Pełny opis: (tylko po angielsku)

The program provides a comprehensive set of tools to support advanced studies in the area of quantitative finance, management and logistics or to prepare for employment in the financial or investment industries. You will be introduced with quantitative data fundamentals and also learn where to start when it comes to analysing quantitative data.

Learning objectives:

1. Build a starter statistical toolbox with appreciation for both the utility and limitations of these techniques.

2. Use software and simulation to do statistics (R).

3. Become an informed consumer of statistical information.

4. Prepare for further coursework or on-the-job study

Literatura: (tylko po angielsku)

Any good book in statistics should be useful. Our main reference will be:

Bruce A., Bruce P., Gedeck P., Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, O'Reily, 2020

Brandimarte, P. (2012). Quantitative methods: An introduction for business management. John Wiley & Sons.

Quirk, T. (2020). Excel 2019 for Business Statistics. Springer International Publishing.

Black, K. (2019). Business statistics: for contemporary decision making. John Wiley & Sons.

Efekty uczenia się: (tylko po angielsku)

Knowledge: knows standard statistical methods and tools for collecting analysis and presentation of economic and social data

Skills: can forecast economic phenomena with the use of basic statistical and econometric methods and tools, can apply appropriate methods and tools for the analysis of economic phenomena

Attitudes: Interpret the results of statistical analysis in a variety of contexts to make deductions and draw conclusions relevant to those contexts. Justify the use of specific methods of analysis in a given situation. Communicate conclusions in a clear and logical manner.

Zajęcia w cyklu "Semestr zimowy 2022/2023" (zakończony)

Okres: 2022-10-01 - 2023-02-01
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Wykład, 15 godzin więcej informacji
Koordynatorzy: Anna Tatarczak
Prowadzący grup: Arleta Kędra, Anna Tatarczak
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Egzamin
Laboratorium - Zaliczenie na ocenę
Wykład - Egzamin

Zajęcia w cyklu "Semestr zimowy 2023/2024" (zakończony)

Okres: 2023-10-01 - 2024-02-04
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Konwersatorium, 30 godzin więcej informacji
Wykład, 15 godzin więcej informacji
Koordynatorzy: Beata Żukowska
Prowadzący grup: Beata Żukowska
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Egzamin
Konwersatorium - Zaliczenie na ocenę
Wykład - Egzamin
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Marii Curie-Skłodowskiej w Lublinie.
kontakt deklaracja dostępności USOSweb 7.0.3.0-cf0b884f2 (2024-04-02)