Data science and machine learning, 2022, Lecturer, Master level, English, Autumn 2022, 12 weeks-48 teaching hours, Lecturing and group study, oral exam, 50 students



Recommended prerequisites
Basic knowledge in operations analysis, mathematics, and statistics is required. Experience with R or a programming language, i.e., Python is a plus, but the course is designed that students get support in learning the software in use.

Overall learning objectives
•Describe and explain the subject of data science (“What it is and what it is not”)
•Explain and use the terminology in the correct way
•Describe the data science process and the interaction of its components
•Carry out a data science project
•Apply statistical learning algorithms
•Create effective visualization of given data
•Explain and communicate data analytics projects results

Learning objectives - Knowledge
•Knowledge of theory, methodology, and practice within data science specific areas such as mathematics and statistics
•Knowledge of data handling including data storage, data processing, and large-scale data analysis
•Knowledge of data analysis tools

Learning objectives - Skills
•Be able to understand each step of a data science project
•Be able to use systems for data management to clean, transform, and query data
•Be able to select and apply appropriate tools for data analysis
•Be able to organize, summarize, and visualize data and project outcomes for relevant stakeholders

Learning objectives - Competences
•Be able to understand and evaluate theoretical issues of problems to select and apply appropriate tools to perform data analysis including appropriate data handling
•Be able to discuss and evaluate data science projects and application areas including ethical issues
•Be able to work as a team member in data science project

Digitalization is on a top position of companies’ agenda. We are living in the digitalization age where sensors, machines, and other entities linked to the internet produce massive amounts of data each day. But this is not the only source of data. For instance, social media or the Internet in general provide access to a variety of data, e.g., consumer preferences. Anyhow, companies are increasingly facing problems on how to use data. The new(ly) (re-) emerging fields such as (big) data analytics, artificial intelligence, machine learning etc. are tools for deriving knowledge of data that can give a competitive advantage to firms. As a result, skills to handle large amounts of various data types and to analyze these to retrieve knowledge of the past, present, and future, are paramount.
Data science is the study of and the learning from data. It focusses on how to manipulate data effectively and efficiently. This requires skill in mathematics, statistics, databases, and machine learning along with a good understanding for the underlying problem (formulation) to provide good decision support.
This course introduces students to the field of data science and equip them with practical skills of doing data handling and analysis including some of the basic principles and tools they can use to deal with different parts of data science. This encompasses knowledge on exploratory data analysis, descriptive & predictive modelling, and evaluation. The course will give an introduction into this broad field but will select topics where practical skills are acquired that can be immediately applied. This makes it neither a “breadth” nor a “depth” course, i.e., it will not try to be comprehensive across techniques or dig deep into some specific technique.

Course Content:
1.Introduction to Data Science
2.Life Cycle and Workflow Management of a Data Science Project
3.Data Manipulation and Visualization
4.Working with Large Datasets
5.Machine Learning – Supervised and Unsupervised
ECTS-point5,0 ECTS