MSc Data Analytics for Business

Program Overview

The MSc in Data Analytics for Business equips professionals with the technical and managerial skills to bridge the gap between data specialists and business sectors. Graduates gain expertise in data analytics, digital transformation, and essential management tools, preparing them for a wide range of career opportunities in the data-driven business landscape.

Program Outline

Degree Overview:

The MSc in Data Analytics for Business is a program designed to bridge the gap between data specialists and business sectors. Graduates acquire expertise in data analytics and the digital transformation of companies, positioning them as valuable resources in modern business environments.

Objectives:

• Become a professional: The program empowers you to bridge the technical and business sides of data analytics, becoming a professional who understands both the intricacies of technology and the dynamics of the business world.
• Develop managerial skills: Learn essential managerial topics like digital corporate strategy, ethical considerations, legal aspects, project management, and design thinking - all from a hands-on perspective.

Outline:

The program encompasses four key areas:

• Technical skills: Acquire mastery in programming languages such as Python and SQL, alongside learning core skills in data mining, data organization, and visualization.
• Analytical skills: Master fundamental data analytics algorithms and learn their application across various business domains.
• Business knowledge: Gain in-depth understanding of strategic management, digital transformation, and the intricacies of diverse business functions.
• Exams: Assessing theoretical knowledge across various modules.
• Assignments: Evaluating application of learned skills on practical problems.
• Projects: Assessing independent project development skills and teamwork abilities.
• Presentations: Evaluating communication skills and project presentation clarity.

Teaching:

The program leverages diverse teaching methods to enhance the learning experience:

• Lectures: Facilitated by expert faculty, delivering core theoretical content.
• Workshops: Providing hands-on practice sessions applying learned skills via practical cases.
• Guest speaker sessions: Industry professionals share real-world insights and perspectives from their specific domain expertise.
• Hackathons: Collaborative activities encouraging innovative problem-solving and practical skill development under time pressure.
• Company visits: Immerse yourself in real-world scenarios through visits to leading companies like IBM, Bloomberg, and Microsoft.

Careers:

As a program graduate, you will be equipped for a broad range of career opportunities. Here are some prominent examples:

• Data Engineer: Build and oversee the infrastructure required to analyze data effectively
• AI and Machine Learning engineer: Design and engineer AI-powered solutions and applications
• Business Analyst: Analyze and interpret relevant data to inform strategic decision-making
• Data Scientist: Extract and leverage insights from complex data in various business functions
• Big Data Engineer: Develop and manage large-scale data systems and pipelines

Other:

• Admission Requirements: Applicants typically require a 4-year degree with demonstrable mathematics and statistics skills.
• Application Process: Visit the dedicated admission page for information on specific application deadlines and required documents.