Big Data Masterclass

Data science emerged as a multi-disciplinary field aimed at creating value from data. This course starts with an overview of data science and related fields and then defines data types and sources. Special focus is put on the assessment of data quality and electronic data processing.

Use of data-driven methods has become vital for businesses, and this course outlines how data-driven approaches can be integrated within a business context and how operational decisions can be made using data-driven methods.

Finally, this course highlights the importance of statistics and machine learning in the field of data science and gives an overview of relevant methods and approaches.

Kursinhalte

• Introduction to Data Science
  • Definition of the term „data science“
  • Data science and related fields
  • Data science activities
• Data
  • Data types and data sources
  • The 5Vs of data
  • Data curation and data quality
  • Data engineering
• Data Science in Business
  • Identification of use cases
  • Performance evaluation
  • Data-driven operational decisions
  • Cognitive biases
• Statistics
  • Importance of statistics for data science
  • Important statistical concepts
• Machine Learning
  • Role of machine learning in data science
  • Overview of machine learning approaches

Fakten zum Modul: Niveau: Bachelor · Unterrichtssprache: English · 5 ECTS-Punkte · Modulprüfung: Workbook

The goal of data science can be summarized as the extraction of insights (hence, value) from data. It is self-evident that this objective cannot be successfully achieved based on unreliable and untrustworthy data. This course aims at establishing the notion of data quality and the pertinent methods for data quality management. Furthermore, techniques for acquiring data as well as formatting and tidying data in order to make it suitable for subsequent analytical treatment are covered.

Kursinhalte

• Data Quality
  • Introduction to data quality
  • Data quality dimensions and issue types
• Data Quality Management
  • Data governance and stewardship
  • Activities and processes
• Data Acquisition
  • Web scraping
  • Data APIs
• Working with Common Data Formats
  • Text-based formats (CSV, XML, JSON)
  • Binary formats (HDF 5, Parquet, Arrow)
• Tidy Data
  • Structuring
  • Cleansing
  • Enrichment

Fakten zum Modul: Niveau: Bachelor · Unterrichtssprache: English · 5 ECTS-Punkte · Modulprüfung: Hausarbeit

A core part of data science is creating value from data. This means not only the creation of sophisticated predictive models but also the development of these models according to modern software development principles.

This course gives a detailed overview of the relevant methods and paradigms which data scientists need to know in order to develop enterprise-grade models.

This course discusses traditional and agile project management techniques, highlighting both the Kanban and Scrum approaches. It explores relevant software development paradigms such as test-driven development, pair programming, mob programming, and extreme programming.

Special focus is given to the topic of testing and the consideration of how to bring a model into a production environment.

Kursinhalte

• Traditional Project Management
  • Requirements engineering
  • Waterfall model
  • Rational unified process
• Agile Project Management
  • Criticism of the waterfall model
  • Introduction to SCRUM
  • Introduction to Kanban
• Testing
  • Why testing?
  • Unit trests
  • Integration tests
  • Performance monitoring
• Software Development Paradigms
  • Test-driven development (TDD)
  • Pair programming
  • Mob programming
  • Extreme programming
• From Model to Production
  • Continuous delivery
  • Continuous integration
  • Building a scalable environment

Fakten zum Modul: Niveau: Bachelor · Unterrichtssprache: English · 5 ECTS-Punkte · Modulprüfung: Examen, 90 Minuten

This project course will give students hands-on experience in the challenging task of bringing a predictive model into a production environment. Students will need to consider practical aspects such as data storage and processing, as well as constraints such as service availability and the maximum amount of time a model is allowed to run due to external project requirements.

Through this course, students will obtain holistic overview of the integration of predictive models into enterprise-grade applications or services.

Kursinhalte

This project course focuses on practical aspects of ensuring that a predictive model can run in a production environment. The students start with a chosen use case and model and then evaluate the requirements which need to be fulfilled so that the model can be used as part of an enterprise application or app. Students need to evaluate requirements in terms of data storage, processing and throughput, and availability of the service, as well as the persistency, serving, and versioning of the model itself. Monitoring the execution of model predictions and raising alerts in cases of operational issues is a core part of building a reliable model pipeline. All relevant artifacts and considerations are documented by the students in a project report.

Fakten zum Modul: Niveau: Bachelor · Unterrichtssprache: English · 5 ECTS-Punkte · Modulprüfung: Projektbericht