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© FH Kufstein Tirol

Data analysis and machine learning

Master's degree program

Interdisciplinary and application-oriented. Our part-time Master's degre program in Data Science & Intelligent Analytics equips you with the skills to leverage data streams effectively. Become an expert in making data-driven decisions!

Overview

  • Qualification Level:

    Stufe 2, Master
  • Price:

    Euro 363,36* (excl. Student Union-fees) each semester
  • Academic Degree:

    Master of Science in Engineering (MSc)
  • Academic Program:

    Part-time
  • Language:

    71 % German, 29 % English
  • Remote Options:

    E-Learning min. 40 % online
  • Exchange Semester:

    Supervised study trip in the 2nd semester**
  • Admission Requirements:

    General admission requirements

Note:

Employment with a company is not required for the part-time study program.
Class times: primarily from Friday afternoon to Saturday evening.
* More information for international students can be found in the Links & Downloads section.
** Travel costs must be paid by the student or covered by a grant.

Program Description

Eingang der FH Kufstein Tirol vom Gebäude B, mit dem Namen der Fachhochschule als Schriftzug darüber stehend. | © FH Kufstein Tirol
© FH Kufstein Tirol
Drei Personen sitzen an einem Tisch, und ein Mann dieser Gruppe zeigt mit seinem Stift in die Richtung der Kamera, wo sich ein Whiteboard befindet. | © FH Kufstein Tirol
© FH Kufstein Tirol
Ein Bücherregal, auf dem drei Reihen von Büchern sind. | © FH Kufstein Tirol
© FH Kufstein Tirol

Dive into the world of data science and intelligent analytics! Learn to analyze complex data sets and extract valuable insights for your organization. Be at the forefront of shaping the future of data utilization in businesses!

Our Master's degree program in Data Science & Intelligent Analytics integrates computer science, statistics, mathematics, and related application disciplines. Graduates gain practical skills in data analysis, technology, business applications, and innovative solution development. We ensure exposure to complex challenges through hands-on data science labs and project-based learning. The program covers the entire value chain, from raw data to cross-functional roles to business success.

Study Focus

  • 30 %

    Datenanalyse und Machine Learning

  • 30 %

    Software development with Python

  • 20 %

    Data storage, integration & use

  • 10 %

    Innovation and data management

  • 10 %

    Business ethics, compliance and law

What You Will Learn

  • Software development in Python

  • Machine learning in Scikit-Learn, Tensorflow, PyTorch and others.

  • Data Engineering with MySQL, MongoDB, Cassandra, Neo4J u.a.

  • Management of machine learning projects

  • Transfer of learning outcomes into practical application

Popular Occupational Fields

  • Big Data Application Developer
  • Data Engineer
  • Big Data & BI Consultant
  • Data Scientist
  • Manager for Data Science Teams
  • Big Data Analyst
  • Specialist for Business Intelligence & Analytics

Career Opportunities

  • 430+ vacancies

    in IT and data analysis in Tyrol

  • EURO 50,200 average salary

    for employees in the IT/data analytics sector in Austria in 2022 (according to WeAreDevelopers)

  • EUR 30.8 billion budget

    for investments in IT and data processing in Austria (according to Statista) in 2021; still growing

  • + 27.48 % increase

    of revenue in the Austrian IT market by 2028 (according to Statista).

  • +3.7 % growth

    in the European IT sector (according to: Gartner)

The path to the Master's degree

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© FH Kufstein Tirol

The degree program spans four semesters. The first two semesters establish foundational knowledge in software development, machine learning, and data engineering. In the third semester, students expand and deepen these skills through practical applications. The fourth semester is dedicated to writing the Master’s thesis and completing the degree program..

Special features:
  • Practical knowledge transfer from day one

  • Study trip in the second semester

  • Practical project with an application-oriented task

Recognition of Prior Learning

Students have the option to receive credit for skills and competencies they have already acquired before the start of each semester.

To apply for credit, they must submit a request directly to the Director of Studies.

Director of Studies

lorem
© FH Kufstein Tirol

Prof. (FH) Lukas Demetz, PhD

Head of IT Services & Software Development, Interim Director of Studies Bachelor Web Business & Technology/Coding & Digital Design, Master Data Science & Intelligent Analytics, Master Web Communication & Information Systems/Web Engineering & IT Solutions

Curriculum

Data Science Application

Machine Learning & Deep Learning
  • Semester 2
  • 10 ECTS

The following content is discussed in the course: - Classical neural networks as a supplement to classical algorithms of data science (e.g. Random Forests, SCM, etc.) - Fallen, artificial neural networks (CNN) - Recursive, artificial neural networks (RNN, LSTM) - Continuing, artificial neural networks (GAN, FARM, BERT, CGAN, etc.) The network types discussed are subject to constant change. For this reason, only a few network types are men-tioned here as examples. Current network types are also discussed and applied in the course.

Details
Internet of Things (elective)
  • Semester 3
  • 4 ECTS

Introduction - IoT architecture (e.g. reference models) - Requirements for IOT systems - IOT data transmission protocols - Use of IOT in an industrial context (examples) - Basics of sensor technology - Basics of embedded systems Implementation - Procedure for implementing IOT - Prototypical implementation of IOT - Selection of sensors - Data collection, visualization and evaluation - Challenges in implementation

Details
Agile Product Development (elective)
  • Semester 3
  • 4 ECTS

- Overview of agile process methods - Roles in the agile process - Flow of an agile project (Sprins, Dailies, Demos, Retros) - Coaching of an agile project (e.g. question techniques) - Experiences with agile projects from software development - Challenges in developing smart products - Methods of product development (e.g. FMEA, TRIZ) - Advantages of hybrid process methods - Role of management in the agile process

Details
Process automation (elective)
  • Semester 3
  • 4 ECTS

- Basic terms: Business process, workflow, BPMS, WFMS, RPA, etc. - Selection criteria for workflow engines for process automation - Architecture and integration of workflows for process automation - Overview of interprocess communication - Transactional properties of processes, simulation and code generation - Basics of Microsoft Dynamics 365: Modules and navigation, basic entities and standard workflows - Organizational and technical implementation with configuration and declarative programming

Details
Quantitative Process & Quality Management (Six Sigma) (elective)
  • Semester 3
  • 4 ECTS

The following content is discussed in the course: - Basics of descriptive statistics - Measurement system analysis - Sample determination - Statistical process monitoring - Process monitoring charts - Process capability analysis - Components of Variants Analysis (COV) - Repetition Basics of inferential statistics - Failure cause determination via hypothesis testing (T-test, Chi-Sq, ANOVA) - Multiple regression analysis

Details
Business Platforms & Cloud Computing (elective)
  • Semester 3
  • 4 ECTS

Students are given an overview of common business platforms and cloud computing. In addition, the advantages and disadvantages of the respective platforms are discussed. Students are therefore able to select suitable platforms for a given problem. Students gain practical experience with selected platforms using case studies. In addition, methods for defining interfaces are discussed with the students.

Details
Human-Computer Interaction (elective
  • Semester 3
  • 4 ECTS

The lecture teaches basic concepts from the field of human-computer interaction (usability, user experience, user interface design) and information visualization. This includes the following focal points: User interface architectures; design criteria, guidelines and standards for the creation and modelling of user interfaces of interactive systems; ap-proaches and methods (quantitative and qualitative) for the evaluation of user interfaces of interactive systems; web style guides and evaluation criteria for websites (e.g. with regard to accessibility); basics of information presentation and data visualization; interactive information visualization; the theoretical lecture contents are prepared in the exercise using practical examples and implemented in a small project (usability evaluation) in a team.

Details
Application-oriented analysis platforms
  • Semester 3
  • 4 ECTS

The following content is discussed in the course: - Presentation of different user-oriented analysis platforms (e.g. KNIME, RapidMiner, Grafana) - Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure) - Application of the platforms presented using the example of analysis data sets - Discussion of the different approaches

Details
Artificial Intelligence (E)
  • Semester 1
  • 4 ECTS

The following content is discussed in the course: - Reasoning approaches (Roal trees, rule-based expert systems) - Search approaches (depth-first, hill climbing, beam, optimal, branch and bound, A*, games, minimax, and alpha-beta) - Constraint approaches (search, domain reduction, visual object recognition) - Learning approaches (neural nets, back propagation, genetic algorithms, sparse spaces, phonology, near misses, felicity conditions, support vector machines, boosting) - Representation approaches (classes, trajectories, transitions) - Possible applications of artificial intelligence in different contexts - Weak versus strong, artificial intelligence This course is offered together with the Web Communication and Information Systems Master program as an elective course.

Details
Big Data Processing (E)
  • Semester 1
  • 4 ECTS

Students are introduced to the basic features of Big Data. Special attention is paid to the handling of this data and the knowledge acquired is consolidated with examples. Suitable frameworks for solving Big Data problems are presented and worked on in interactive workshops with case studies. Examples of this are as follows: - Apache Hadoop - Apache Spark - Apache Flink - Apache Storm - Apache Samza - Apache Kafka These frameworks will be explained and used with case studies. For this purpose, the centrally-provided Data Labs can be accessed.

Details
Data Science for Business & Commerce
  • Semester 3
  • 4 ECTS

The following content is discussed in the course: - CRM on the strategic level - CRM in process management - CRM on the operative level (CRM software systems) - Operative CRM - Analytical CRM - Communicative CRM This course is offered as an elective course together with the Master's Course in Web Communication and Information Systems.

Details
Data Science for Engineering & Natural Sciences
  • Semester 3
  • 4 ECTS

The following exemplary contents are discussed in the course: - Biology (e.g. genome research, medical diagnostic procedures, etc.) - Physics (e.g. object recognition through image data processing, etc.) - Chemistry (e.g. processing of data-intensive experiments, etc.) - Data-driven maintenance (e.g. predictive maintenance, Digital Twin) - Data-optimized product design (e.g. design of product properties by KNN) - Evaluation of sensor data (e.g. obstacle detection, obstacle avoidance, prediction, etc.) - Cloud-based IoT systems (data storage and collection) - sensor evaluation via Raspberry Pi, Arduino, radio systems

Details
Data Visualization & Visual Analytics (elective)
  • Semester 3
  • 4 ECTS

The following content is discussed in the course: - Evaluation tools with visual orientation, e.g. Bl tools such as MS PowerBl, Tableau, QlikView - Display libraries, e.g. matplotlib.pyplot, gglot2 - Rules of visual communication, e.g. Hichert SUCCESSSS

Details

Data Science Basics

Data Engineering
  • Semester 1
  • 4 ECTS

The following content is discussed in the course: - Properties of high-performance data systems (scalability, maintainability, reliability) - Established concepts of data storage (Relational Model) - Historical concepts of data storage (Hierarchical Model, Network Model) - Modern concepts of data storage (Wide-Column Model, Graph Model, Key-Value Model, Document Model, Column-Oriented Model) - Database systems, matching the models discussed - Scaling of data systems (replication and partitioning) - Writing and reading in data systems (index structures, write strategies)

Details
Dat Engineering Lab
  • Semester 1
  • 5 ECTS

The following content is discussed in the course: - Design and implementation of problem-centred NoSQL databases (e.g. key-value stores, document stores, column-oriented data stores, etc.) - Design and implementation of storage solutions for large quantities of data (big data)

Details
Software development 1
  • Semester 1
  • 6 ECTS

The following content is discussed in the course: - The process of software engineering and project management for data-intensive applications - Programming paradigms for use in data science - Effective and efficient data structures for data-intensive applications - Tools and software ecosystems for the development and testing of data-intensive software systems

Details
Software development 1 Lab
  • Semester 1
  • 2.5 ECTS

In the lab, the contents of the ILV “Software Development 1” are advanced with the aid of practical exercises. The knowledge gained will be discussed in the group and thus allow a deep insight into the material and consolidation of the knowledge, which was theoretically dealt with in the ILV.

Details
Statistical learning 1
  • Semester 1
  • 6 ECTS

The following content is discussed in the course: - Statistical measures (point and interval estimators) - Statistical test procedures - Grouping algorithms (classification trees, agglomerative hierarchical clustering, etc.) - Regression algorithms (regression trees, random forests, etc.) - Associative algorithms - Procedures for preprocessing data (e.g. principal component analysis)

Details
Statistical learning 1 Lab
  • Semester 1
  • 2.5 ECTS

In the lab, the contents of the ILV "Statistical Learning 1" are advanced with the aid of practical exercises. The knowledge gained will be discussed in the group and thus allow a deep insight into the material and consolidation of the knowledge, which was theoretically dealt with in the ILV.

Details
Software development 2
  • Semester 2
  • 6 ECTS

The following content is discussed in the course: - Architecture models for data-driven software development and systems - Integration models and paradigms for implementing complex, process-oriented software ecosystems for analytical and data-driven systems - Application of proven design patterns for data-driven applications - Design and implementation of efficient and scalable software systems for data-driven applications - Testing of software applications (e.g. unit tests, integration tests, etc.)

Details
Software development 2 Lab
  • Semester 2
  • 2.5 ECTS

In the lab, the contents of the ILV “Software Development 2” are advanced with the aid of practical exercises. The knowledge gained will be discussed in the group and thus allow a deep insight into the material and consolidation of the knowledge, which was theoretically dealt with in the ILV.

Details
Statistical learning 2
  • Semester 2
  • 6 ECTS

The following content is discussed in the course: - Advanced modelling techniques - Ensemble methods - Optimization of models

Details
Statistical learning 2 Lab
  • Semester 2
  • 2.5 ECTS

In the lab, the contents of the ILV "Statistical Learning 2" are advanced with the aid of practical exercises. The knowledge gained will be discussed in the group and thus allow a deep insight into the material and consolidation of the knowledge, which was theoretically dealt with in the ILV.

Details

Engineering

Trends in ERP (elective)
  • Semester 4
  • 3 ECTS

- Current developments in the field of business application systems with special reference to ERP systems and business process management - Models, examples, best practice cases

Details
Trends in Smart Products (elective)
  • Semester 4
  • 3 ECTS

The contents of this course are not set, but will be adapted to the current prevailing trends. Content examples may include: - Current best practice approaches and concepts in application areas (e.g. Smart Home, Smart City, Smart Production, Connected Vehicles etc.) - Current best practice approaches with regard to development processes and tools - Current research and development activities or research and development results

Details
Trends in Web Technologies (elective)
  • Semester 4
  • 3 ECTS

The contents of this course are not set, but will be adapted to the current prevailing trends. Content examples may include: - New technologies in the field of web architectures - Trends in the field of programming languages on the web - New design concepts in the field of web applications - New questions in the field of research in web technologies and applications - New questions in the field of web development practice

Details
Trends in Data Science (elective)
  • Semester 4
  • 3 ECTS

The contents of this course are not set, but will be adapted to the current prevailing trends. Content examples may include: - New technologies in the field of Big Data Processing - Trends in programming languages in data analysis - New concepts of data processing (e.g. Data Lake) - New questions in the field of data science research - New questions in data science practice

Details

international competence

Study Trip
  • Semester 2
  • 3 ECTS

The following content is discussed in the course: - Intercultural competence - Discussion with representatives from the field

Details

Management

Team Leadership & Project Management
  • Semester 1
  • 2 ECTS

The following content is discussed in the course: - Project management techniques (e.g. SCRUM) - Project management tools in the field of data science (e.g. GitLab) - Techniques for documenting requirements (e.g. Sophist)

Details
Systemic Innovation
  • Semester 1
  • 2 ECTS

The following content is discussed in the course: - Developing a holistic understanding of the subject areas (systemic management) -Methods for generating innovative ideas (e.g. Systematic Inventive Thinking, Design Thinking) - Project structures and management methods for the practical implementation of innovations (e.g. change manage-ment, conflict management) - IT-supported project documentation

Details
Business Ethics, Compliance and Law
  • Semester 2
  • 3 ECTS

The following content is discussed in the course: - Data protection (e.g. DSGVO) - Privacy (e-Privacy Regulation) - Handling of data from an ethical/moral point of view - Compliance

Details

practical transfer

Practical Project
  • Semester 3
  • 4 ECTS

In this course, students work on a real, data-centred project along the entire data value chain (from data collection, integration and storage to analysis and utilization of the data). This allows them to try out the skills they have built up in the first two semesters in a real setting and gain new insights.

Details
Academic Methods
  • Semester 3
  • 2 ECTS

Students are introduced to the theory of science and academic methods. The goals of academic methods are dis-cussed and applied to the students' own problems. During the course, the students will therefore develop a first draft exposé for a Master thesis.

Details
Master thesis
  • Semester 4
  • 22 ECTS

Students independently draft a project idea for their own Master thesis, describe it in the form of an exposé and submit it to the program management for approval. Students then work on the topic and write a Master thesis, which is submitted for review.

Details
Master Thesis Seminar
  • Semester 4
  • 2 ECTS

The course accompanies the students while they draft and write their master thesis. The colloquium will therefore present and discuss the question/hypothesis and structure of the Master thesis. In addition, the scientific methodology of the Master thesis is discussed and questioned and advice is given on the formal design of the Master thesis.

Details
Study regulations to download

Frequently Asked Questions

Do I need any previous knowledge to start studying?

Yes, prior knowledge of mathematics and statistics (8 ECTS) and computer science (6 ECTS) is required for admission to the degree program. Applicants who lack this knowledge can make up for it with our free video-based preparatory course.

How high is the proportion of technology in the course?

Our aim is to provide our students with the tools to work on data analysis projects themselves. For this reason, we try to integrate a high proportion of hands-on units into our courses. The technical part is 50% and is supplemented by application-oriented complementary subjects (e.g. Systemic Innovation or Business Ethics Compliance & Law).

How is the study program organized?

As a part-time degree program, the courses always take place on Friday afternoons and Saturdays. We also teach alternately in face-to-face and online formats. As a rule, we alternate between these two formats on a weekly basis.

Which target groups does the course appeal to?

Our students come from a wide variety of professional and academic backgrounds. We bring together business economists, physicians, biologists, pharmacists, people with an industrial background and many more. We see it as our task to impart skills in the field of data analysis and technology support that are required in various areas of application.

Do I have to work while studying?

No, there is no obligation to work or to be active in the industry. The course offers you the opportunity for further development, even if you are working in another profession or are not currently working.

Unlike a dual study program, there is no binding contract with a company. Many students also use the course during parental leave or for professional reorientation.

The quality of the degree programme and the modern facilities offer optimal conditions for a great course of study. The supervision of the lecturers makes the content understandable for everyone.
© FH Kufstein Tirol
© FH Kufstein Tirol

Sabine Ascher

Student

The degree programme teaches comprehensive methods and technologies of data science, which are tested in practice on extensive data sets. Complex problems can thus be solved and developed intelligently.
© FH Kufstein Tirol
© FH Kufstein Tirol

Karsten Böhm

Lecturer

I find the areas of data engineering and software development particularly interesting, as this knowledge makes my day-to-day work much easier.
© FH Kufstein Tirol
© FH Kufstein Tirol

Victoria Petermaier

Graduate