Data Science and Business | Business Information Technology

Program Overview

The Data Science and Business specialization within the Business Information Technology Master's program at the University of Twente equips graduates with the knowledge and skills to excel in data-driven decision-making. The program covers principles and practices of data science, data mining, machine learning, and business process management. Graduates are prepared for careers in data science, business analytics, and related fields where data-driven decision-making is crucial.

Program Outline

Degree Overview:

Overview:

The Data Science and Business specialization within the Business Information Technology Master's program at the University of Twente aims to equip graduates with the knowledge and skills to excel in data-driven decision-making at operational, tactical, and strategic levels.

Objectives:

• Provide students with a comprehensive understanding of the principles and practices of data science and their application in business contexts.
• Enhance students' abilities to collect, process, analyze, and interpret large volumes of data using advanced data mining and machine learning techniques.
• Develop students' critical thinking, problem-solving, and communication skills to effectively communicate data-driven insights and recommendations to stakeholders.
• Prepare students for careers in data science, business analytics, and related fields where data-driven decision-making is crucial.

Outline:

Program Content:

• Business Innovation and the interaction between IT and business processes
• Fundamentals of IT security and risk assessment
• Data Analytics
• Machine Learning
• Business Process Management
• IT Architecture and Enterprise Integration
• Strategic Management and Leadership
• Research Methods for Business Information Technology

Course Schedule:

The program consists of coursework, projects, and a Master's thesis. Students typically complete the program in 18 months of full-time study. The course schedule includes:

• Semester 1: Core courses in Business Information Technology fundamentals, Data Analytics, and Machine Learning.
• Semester 2: Elective courses, project work, and a research methods course.
• Semester 3: Master's thesis research and writing.

Individual Modules:

• Managing Big Data:
Storage and processing of large data volumes, including cloud computing and distributed systems.
• Simulation:
Modeling and simulation of business processes to optimize performance and identify bottlenecks.
• Machine Learning:
Neural networks, deep learning, and reinforcement learning for automated data processing and business process optimization.
• Business Process Management:
Analysis, design, and improvement of business processes using data-driven insights.
• IT Architecture and Enterprise Integration:
Enterprise architecture frameworks, integration patterns, and cloud computing for IT infrastructure design
• Strategic Management and Leadership:
Strategic planning, decision-making, and leadership in the context of digital transformation and data-driven innovation.
• Research Methods for Business Information Technology:
Research design, data collection, analysis, and interpretation in the field of Business Information Technology.

Assessment:

• Continuous assessment through assignments, quizzes, and participation in class discussions and projects.
• Written exams for core courses.
• Project reports and presentations.
• Master's thesis evaluation based on originality, research quality, and presentation.

Teaching:

• Lectures, tutorials, and hands-on lab sessions delivered by experienced faculty and industry experts.
• Emphasis on practical application of data science and business principles through case studies, projects, and real-world data analysis.
• Collaborative learning environment with opportunities for group work and peer feedback.
• Access to state-of-the-art research facilities and computing resources.

Careers:

• Data Scientist
• Business Analyst
• Data Analyst
• Management Consultant
• IT Project Manager
• Business Intelligence Analyst
• Data Engineer
• Quantitative Analyst