07/27/2022

Bridging Courses at the Faculty of Communication and Environment

Bridging courses are intensive courses you can take before starting your studies. They are voluntary and do not carry any credit-point value towards your degree. Bridging courses are designed for students who may not meet the assumed knowledge requirements and want to bridge the gap between school and university studies.

Please find the currently scheduled bridging courses for the period September 5 - 9, 2022 below. Please note that the programme may still change at short notice. If you are interested in one or more of the courses on offer, please register by web form. The deadline for registration is August 28, 2022 at 11.59 pm.

You will receive all access data and further information between August 29 and September 2, 2022 by e-mail after registration.

Bridging courses between September 5-9, 2022

Time and Date Topic Language Lecturer

Mo-Fr

2pm - 6pm

online (Webex)

Mathematik German Arslan Austin
Webex Link

Mo-Fr

9am - 1pm

online (Webex)

Mathematics English Arslan Austin
Webex Link

Mo-Fr

9am - 1pm

online (Webex)

Statistik Deutsch Sabine Lauderbach, M.A.
Webex Link

Mo-Fr

2pm - 6pm

online (Webex)

Statistics English Sabine Lauderbach, M.A.
Webex Link

Mo-Fr

1.30pm - 3.30pm
and
4pm - 6pm

online

Physics English Kevin Shehu

 

Course contents

Mathematik (German)

  • Elementare Rechenmethoden (inkl. Summen- und Produktzeichen, Potenzen und Logarithmen)
  • Lineare Gleichungssysteme
  • Folgen und Reihen, Grenzwertbegriff
  • Grundlagen der Differentialrechnung (Funktionen und ihre Ableitungen)
  • Einführung in die Lineare Algebra (Vektoren, Matrizen)
  • Grundlagen der Wahrscheinlichkeitsrechnung (Kombinatorik, Häufigkeiten)
  • Einführung in die deskriptive Statistik

Je nach Kurszusammensetzung können sich Schwerpunkte auch verschieben.

Mathematics (English)

  • sequences and series, limit value concept
  • basics of differential calculus (functions, and their derivatives)
  • integrals
  • introduction to linear algebra (vectors, matrices, systems of linear equations)
  • basics of probability calculation
  • introduction to descriptive statistics

Statistik (German)

Tag 1: Deskriptives, Lagemaße und Streuung: Mittelwert, Standardabweichung, Ausreißer, Boxplot und Punktdiagramm, Normalverteilung, Schiefe, Kurtosis.

Tag 2: LaPlace-/Nicht La-Place Experimente, Wahrscheinlichkeiten, Münzwürfe, P-Wert, Urnenmodelle, Population und Stichprobe, Bedingte Wahrscheinlichkeit, Bayes Theorem.

Tag 3: Konfidenzintervall, Signifikanzniveau, H0/H1 und das Fehlalarm Paradoxon, Statistische Tests parametrisch und nicht parametrisch, Funktionsweise Chi-Square und Student T-Test (H0/H1 Test).

Tag 4: Self-Study Chi-Square Goodness of Fit / Casestudy: Problem des Handlungsreisenden, Lernvideos.

Tag 5: Parameterschätzung, Alpha und Beta Fehler, Anpassungsgüte, Optimierungsverfahren, Wahrscheinlichkeitsfunktionen, Arbeit mit Statistik Programmen (Jasp/SPSS/PSPP, R).

Statistics (English)

Day 1: Descriptives, mean, standard deviation, outliers, box plot and scatter plot, normal distribution, skewness and kurtosis.

Day 2: LaPlace/Non La Place experiments, probabilities, coin tosses, P-Value, urn problem, population and sample, conditional probability, Bayes' Theorem.

Day 3: Confidence interval, measuring significance, H0/H1 and false positive paradox, statistical tests parametric and non-parametric, chi-square and Student's T-Test.

Day 4: Self-Study Chi-Square Goodness of Fit / case study: the travelling salesman problem, learn videos.

Day 5: Parameter estimation, alpha and beta errors, goodness of fit, optimization methods, probability functions, working with statistical programs (Jasp/SPSS/PSPP, R).

Physics (English)

  • Physical quantities
  • The power of  10
  • Dimensions and units, calculating with units
  • Significant figures
  • Derivatives and Antiderivatives
  • 1D Motion
  • Vectors
  • 3D Motion
  • Dealing with systems of equations
  • Trigonometry
  • Complex numbers