Data Science / Statistics / R Foundation / Statistics / Descriptive Statistics

Statistics - Descriptive Statistics


ID 2858618
Classroom 3 days
Webinar 5 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training


Descriptive statistics is the discipline of quantitatively describing the main features of a collection of data, or the quantitative description itself. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable's distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf display. When a sample consists of more than one variable, descriptive statistics may be used to describe the relationship between pairs of variables. In this case, descriptive statistics include quantitative measures of dependence. This training covers all the fundamentals of descriptive statistics which can be used in marketing, controlling and engineering. You will learn theory and the mathematical foundations in lectures with examples and you will train your new knowledge in practical hands-on labs and exercices.

Training Dates

  • 2020-Oct-19 - Oct-23
  • 2020-Dec-28 - Jan-01
  • 2021-Mar-08 - Mar-12
  • 2021-May-17 - May-21

850 EUR +VAT

Location | Enrollment


Introduction to Statistics

Descriptive and Inductive Statistics - Uni-/Bi- and Multi-variate Statistics - Summary tables: Grouped data, Frequency distributions, Contingency tables - Statistical graphics: Bar chart, Biplot, Box plot, Histogram

Univariate Analysis: Measures of Central Tendency

Mean (Arithmetic, Geometric, Harmonic) - Median - Mode

Univariate Analysis: Measures of Dispersion

Range - Variance and Standard deviation - Coefficient of variation - Percentiles - Interquartile range - Shape: Variance, Skewness, Kurtosis, Moments

Univariate Analysis: Measures of Shape

Skewness - Kurtosis - Moments

Bivariate Analysis: Dependence

Continuous data: Pearson product-moment correlation, Partial correlation, Scatter plot - Ordinal data: Rank correlation (Spearman's rho, Kendall's tau) - Categorical data: Contingency tables, Cramer´s V, Phi coefficient, Chi coefficient

Bivariate Analysis: Regression

Linear regression: Simple linear regression, Ordinary least squares - Regression analysis: Errors and residuals, Regression model validation, Estimations - Overview of non-linear regression models


Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.