Data Science: The Way from Big Data to Smart Data

Data have become one of the most important resources in the age of digitalisation. They can be used, for example, to predict and prevent disasters, achieve medical progress more quickly and gain competitive advantages. This practical course trains you to become a Data Scientist. Participants receive supports that enables them to independently work on, evaluate and interpret complex tasks using methods including explorative data analysis, regression analysis, classification, discrimination and clustering, neural networks and main component analysis using the statistics programme package R.

With the help of specific application examples, you can apply what you have learned directly in practice. The data for the practical examples are derived from the fields of environmental and life sciences, medicine, transport, finance and the food industry.

• You learn the basics of the statistics programme package R (open source) and use it to conduct data analyses.
• You obtain insight into the diverse subject of statistical modelling.
• You learn how to work with systematic and logical precision.
• You gain problem-solving experience in the field of data science.

The intensive course includes 8 modules:

• Module 1: The Data Scientist – Key Figure in Understanding Business and Data
• Module 2: Introduction to the Programme System R: Problem Analyses and Obtaining Data
• Module 3: Data Mining: Preparing the Information and Exploring the Raw Data
• Module 4: Statistical Modelling Methods in R
• Module 5: Logistical Regression (Binary Response Variables)
• Module 6: Classification Trees and Regression Trees
• Module 7: Artificial Neural Networks

The course has been designed for people who have to deal with many types of data in different business areas and want to acquire current knowledge in the field of statistical data analysis, modelling and interpretation. Such as

• Health professionals,
• Logisticians,
• People working in tourism,
• Civil engineering employees.

Applicants should have received an academic education at the university level and have basic knowledge in statistics.