Masters in Analytics (120 ECTS)

In partnership with Great Learning, the MSc Big Data and Business Analytics course curriculum, designed by industry professionals and academic experts at FOM International University, equips learners with skills in assessing and improving Big Data systems, applying advanced analytics to real-world problems, and developing innovative business models for actionable insights.

Semester 1

Learners integrate essential Big Data technologies and practices, advanced decision-making techniques, ethical leadership skills, AI-driven data analysis, and foundational German language proficiency.

Big Data Architecture and Infrastructure

This module explains data center infrastructure, storage systems, and network technologies essential for Big Data. It covers business continuity, information security, and storage management, equipping learners for the evolving IT landscape.

Design Focussed Management

This module discusses, analyzes, and makes informed management decisions by integrating economic and behavioral factors, enhancing decision-making skills and collaborative abilities in various business scenarios.

Leadership and Sustainability

This module focuses on leadership theories and styles, emphasizing diversity, sustainability, and ethical decision-making. It prepares learners for roles requiring solid interpersonal and ethical skills.

Big Data Analytics

This module enables learners to assess Big Data applications and utilize AI, enhancing their data analysis, predictive analytics, and visualization skills for data-driven environments.

Deutsch

This module equips learners with essential German language skills, enabling them to understand everyday expressions and communicate basic needs.

Semester 2

Learners inculcate skills for analyzing structured and unstructured data, project management techniques for Big Data, and strategic planning in Business Analytics. These modules focus on data protection law and ethics and IT security management, ensuring a comprehensive understanding of the technical and ethical aspects of Big Data.

Applied Programming

This module enables learners to acquire skills to analyze structured, semi-structured, and unstructured data using relevant programming tools essential for Big Data processing

Analysis of Semi and unstructured data

This module provides essential skills for analyzing unstructured data, including text structuring, classification, and clustering.

Project management of Big Data projects

This module prepares learners to manage big data projects effectively, addressing unique challenges and complexities in this field.

Business Analytics

This module teaches planning, implementation, assessment, and statistical analysis of Big Data applications, fostering data-driven innovation.

Ethics and law

This module equips learners with knowledge of data protection law and ethics, focusing on compliance and ethical data analysis.

Information Security

This module addresses IT security management within a business context, covering risk management and practical security strategies.

Semester 3

Learners practice evaluating economic potential through data visualization, foundational research in economics, advanced analytics and data mining, strategic business model development, and a practical project focusing on real-world Big Data challenges to enhance research and problem-solving skills.

Big Data Consulting Project

This module instructs learners to evaluate economic potential and communicate findings through data visualization for the company’s competitive advantage.

Research Methods

This module provides theoretical foundations for economics research, focusing on data analysis and business intelligence skills

Big Data Analysis Project

This module teaches advanced analytics and data mining, developing the ability to analyze large amounts of data and complex problems.

Strategic Business Model Development

This module prepares learners for management roles, focusing on business model development and ethical considerations to make informed business decisions.

Applied Project I

This project focuses on empirical or practical issues in Big Data and Analytics, requiring a detailed report for applying theoretical knowledge and technical skills gained through the Business Analytics program to real-world challenges, enhancing research and problem-solving abilities.

Semester 4

Learners engage in activities to develop skills for independently completing specialized tasks and oral presentations of a Master's thesis. They present on a predetermined topic, applying their knowledge of Big Data and Business Analysis in a practical setting.

Thesis/Thesis Defense

This activity imparts skills for autonomously completing subject-specific tasks and for oral presentation of a Master’s thesis.

Applied Project II

This project involves a presentation on a topic announced at the beginning of the semester, allowing learners to apply their knowledge earned through the Big Data and Business Analysis master program in a practical context.

Non Credit Curriculum

Learners expand their skills beyond Big Data.  They will gain the expertise to excel in various business functions, from finance and HR analytics to digital marketing and supply chain management, and make data-driven decisions for strategic advantage.

*Note: Curriculum is subject to revision based on industry requirements. 

Intro to Machine Learning and Deep Learning:

Learners understand the fundamentals of Machine Learning, including the core concepts, algorithms, and techniques that empower machines to learn from data and make intelligent predictions.

 

• Introduction to ML: This module explores supervised and unsupervised learning algorithms, data preprocessing techniques, and model evaluation methods.
• Neural Networks: This module helps understand neural network architecture, activation functions, and training processes.
• Introduction to NLP: This module explores how machines can understand, interpret, and generate human language.
• Introduction to Computer Vision: This module discovers how machines can "see" and analyze visual data, paving the way for applications like image recognition and object detection.
   

Introduction to Generative AI:

Learners understand the foundational knowledge about Generative AI. They explore its capabilities firsthand and gain insights into the future advancements facilitated by tools like Gemini Pro Vision.

 

• Introduction to Generative AI: This module explores the fundamental concepts of GenAI and its potential to generate realistic images, compelling text formats, and more.
• ChatGPT—Generative AI and Applications: This module discusses a powerful language model that showcases GenAI's capabilities and real-world applications. It includes a live demonstration highlighting its remarkable prowess.
• Intro to Gemini Pro Vision: This module introduces innovative tools and technologies pushing the boundaries of computer vision and its potential synergy with GenAI.

Prompt Engineering

Learners acquire the knowledge and skills to craft effective prompts, navigate advanced techniques, and evaluate the results. They equip with skills to harness the true power of Generative AI for various applications.

• Introduction to Prompt Engineering: This module guides how large language models process information and generate outputs.
• Python Basics for Prompt Engineering: This module establishes a foundation in Python, a versatile programming language frequently used in Prompt Engineering.
• Prompt Engineering at Scale: This module explores advanced techniques for tackling complex tasks efficiently using Prompt Engineering methodologies.
• Prompt Engineering—Text Summarization: This module teaches how to craft effective prompts that create concise and informative summaries from vast amounts of text data.
• Generative AI Workloads: This module explores the diverse applications of Generative AI and its potential in classification tasks for various data types, showcasing its versatility across different domains.
• Evaluating Generative AI Outputs: This module helps learn how to effectively assess the quality and accuracy of outputs generated by large language models (Text-to-Text LLM Outputs).
   

Note - The curriculum is indicative and subject to change.