# Introduction to Statistics and Data Science

Exam number: 6854

Semester: from 1st semester

Duration of the module: One semester

Form of the module (i.e. obligatory, elective etc.): Elective

Frequency of module offer: Each winter semester

Prerequisites: Knowledge in mathematics and statistics

Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.

Person responsible for module: Prof. Dr. Wolfgang Schmid

Name of the professor: Prof. Dr. Wolfgang Schmid

Language of teaching: English

ECTS-Credits (based on the workload): 6

Workload and its composition (self-study, contact time):
Contact time (Lecture, tutorial etc.): 45 h; self-study: 135 h

Contact hours (per week in semester): 4

Methods and duration of examination:
Successful preparation of a term paper as well as presentation of the results of work

Emphasis of the grade for the final grade: Please check regulations of the study programme

Aim of the module (expected learning outcomes and competencies to be acquired):
Drawing conclusions from data is vital in research, administration and business. This class provides an overview of descriptive, inductive and explorative statistical methods which guide through the process of quantitative data analysis and helps to describe, interpret and analyse data. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analysing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The subject is closely tied to the practice of statistics. The course is organized around various kinds of problems that entail the use of statistical methods and include many real examples to motivate and introduce the theory.

Contents of the module:
1. Elements of Data Analysis
2. Introduction to Statistical Inference
3. Tests for Univariate Data
4. Tests for Bivariate Data
5. Multiple Linear Regression
6. Analysis of Variance
7. Introduction to Time Series

Teaching and learning methods:
Seminar, presentations

Literature (compulsory reading, recommended literature):
Heumann, C., & Schomaker, M. (2016). Introduction to statistics and data analysis. Springer International Publishing Switzerland.
Ruppert, D. (2004). Statistics and finance: An introduction (Vol. 27). New York: Springer.
Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John wiley & sons.
Gelman, A. (2005). Analysis of variance—why it is more important than ever. The annals of statistics, 33(1), 1-53.
Faraway, J. J. (2002). Practical regression and ANOVA using R (Vol. 168). Bath: University of Bath.
Cohen, Y., & Cohen, J. Y. (2008). Statistics and Data with R: An applied approach through examples. John Wiley & Sons.
Rice, J. A. (2006). Mathematical statistics and data analysis. Cengage Learning.

Further information:
Registration in Moodle Viadrina required.