Program Structure and Curriculum

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A Unique 12-Month Study Program Spanning Two Locations

Becoming a management analytics student at Reichman University and Mannheim Business School means that you will study both at one of the most prolific entrepreneurial tech hubs and in the heart of the European economy, with the potential to gain access to and future employment at a budding startup, hidden champion, or leading corporation. Data scientists are in great demand at companies of all sizes and in all industries. According to a World Economic Forum study, data analysts and scientists, AI and machine learning specialists, as well as big data and digital transformation specialists are among the top 10 most promising job profiles for the coming years. They address data-driven decision-making problems in various areas of the business such as finance, marketing, operations, human resources, and business intelligence.

  • Courses

    Introductory courses like ‘Data Science for Business’ or ‘Decision-Making Under Uncertainty’ lay the foundation for transforming data into meaningful insights and actions. Advanced courses then help you deepen your knowledge in specific areas such as Machine Learning, UX Design, Financial Analytics, or Marketing Analytics.

  • Business Analytics Master Project

    Working in a team of fellow students, you will apply the specialist knowledge and methodologies acquired throughout your studies in a comprehensive practical project at one of our partner companies.

  • Data Analytics Skills

    During your studies, you will acquire skills in the latest programming languages such as Python and R.

  • Soft Skills

    Essential program elements such as the Business Analytics Master Project require that you work with classmates in heterogeneous teams, just as you would at your workplace. Soft skills courses foster the necessary key qualities for being a digital driver in your company.

  • Practical Input

    In the course of the modules, you will regularly get insights from industry experts and opportunities to visit companies in Israel and Germany to learn about their data-driven projects.

Sample Schedule

The Curriculum in Detail

Become a digital transformation expert. Here you will find a detailed description of the individual modules and course components of the Master in Management Analytics.


  • Data Science for Business I

    “Data Science for Business I” is designed to introduce participants to key concepts, tools, and practices of data science from a managerial perspective. The objective of the course is to provide the students with the analytical tools that will help them to generate insights from data in a robust, correct, and actionable way. While the course has a solid theoretical foundation, it is designed to be a hands-on course in which the students apply the learnings from the first day onwards, solving cases and problems. The course starts with the basics of statistics and ends up introducing machine learning models to incorporate time components in predictive analytics. The programming language used in class to solve the cases is Python.

    In this course participants will have exposure to state-of-the-art methodologies for data science. The main objective of the course is that the students learn how to gather, treat and clean data, as well as analyze it using different methods and generate insights that shall serve them to gain knowledge about their fields. The overall objective of this course is that participants feel comfortable and fluent when working with data, and that they learn how to use different methods to generate insights from the data. Moreover, the course teaches students how to model functions and how to make simulations to generate further knowledge. Ethically adequate treatment and processing of data will also be discussed.

  • Data Science for Business II

    “Data Science for Business II” is designed to deepen the knowledge of the participants in the use of analytical and computational tools to generate knowledge. The course aim to provide students a profound introduction in how to use machine learning, clustering techniques, classifiers, and network science tools, such that the students can generate various managerial insights and knowledge out of data. While the course has a solid theoretical foundation, it is designed to be a hands-on course in which the students apply the learnings from the first day onwards, solving cases and problems. The software used in class to solve the cases is Python or R.

    In this course participants will continue their exposure to state-of-the-art methodologies for data science. The main objective of the course is that the students learn how to generate information, knowledge, and wisdom from data by applying advanced data-science methods. The overall objective of this course is that participants understand and are comfortable using advance analytics tools. The completion of this course shall position the students as experts in the managerial application of data analytics tools and set them above the average data-driven business manager.

  • Decision Making under Uncertainty

    In the course Decision Making under Uncertainty, participants learn the basics and concepts as well as tools and practices of decision making under uncertainty and (im)perfect information. They learn how to implement practice-oriented decision problems using commonly used software packages. The participants of the course learn how to structure and improve decision processes to make better decisions and provide a recommendation to the company: 

    •    Structuring decision problems under uncertainty using influence diagrams
    •    Analysis and implementation of decision problems under uncertainty using decision trees
    •    Sensitivity analysis
    •    Analysis of the value of perfect as well as imperfect information
    •    Analysis of complex decision problems using Monte Carlo simulation
    •    Challenges of decision making under uncertainty within an organizational context

  • Data Intuition

    The ubiquity of information and large amounts of data nowadays requires the increased ability to analyze and interpret them in a reflective and critical manner. Moreover, any form of data analysis, for example the use of machine learning algorithms, is inherently dependent on the impartiality of the data scientist. 

    This course is designed to sensitize the participants to how cognitive biases - independent of the intentions of the data scientist - can impair critical thinking when dealing with data. The course draws on the psychology of critical thinking and explains the most common forms of cognitive biases. The course shows the implications for the collection, analysis and interpretation of data by analyzing a variety of examples from everyday management, the media or scientific publications. Finally, the course explains which "debiasing" techniques have proven to be effective in improving the quality of thinking and decision-making and how effective and undistorted reasoning can be used when dealing with data. The aim of the course is to provide participants with essential thought-provoking impulses for a critical and responsible approach to the collection, analysis and interpretation of data.

  • Data Visualization & Storytelling

    In this course, you will learn how to generate relevant insights from data and communicate these effectively. You will be able to develop powerful stories from data and master suitable types of data visualizations. The course spans the full scope from psychological foundations to software implementation. Best practice guest lectures will illustrate current approaches. 

    Topics include: 

    • Psychological foundations and principles of data visualization
      • From visual perception to understanding
      • The role of context
      • Managing cognitive load of audience
      • Directing audience attention
    • Types of data visualization
      • Cross-sectional and longitudinal data
      • Small and big data
      • Structured and unstructured data
      • Univariate, bivariate and multivariate visualization
      • Visualization of econometric and artificial intelligence models
    • Developing stories from data 
      • Talks 
      • Reports 
      • Dashboards


  • Strategy Innovation and Entrepreneurship

    The course explores how startups and larger firms can use their data to create sustainable competitive advantages. It starts with outlining on the strategy of the single business firm and explores how it can innovate to create competitive advantages around data, and identifies the conditions that would make such competitive advantages sustainable. The course further deals with the shaping and the execution of strategic moves and the strategic management of businesses in changing, competitive and uncertain digital environments. It treats the issues of business strategies from the firm's top management perspective with an aim of building up data- and analytics-centered sustainable competitive advantages. Then the course moves on to deal with growth strategies, focusing on how firms can use their data and analytics centered competitive advantages in a given business area to diversify to new businesses and new geographies.

  • Financial Analytics I

    Data science, and in particular statistical and mathematical methods, is now part of most financial activities and is changing the financial services industry inexorably. In this course, participants will learn the methodological tools to use Big Data as a lender for faster and better credit decisions or as a trader for data analysis to maximize portfolio returns.

    • The following methods are taught in a practice-oriented manner, using company examples: Statistical inference, financial time series modelling, event study analysis and machine learning for Big Data prediction. 
    • Real data is analysed to create models for financial and macro forecasting, quantitative trading and dynamic risk management.
  • Financial Analytics II

    Technology is playing an increasingly dominant role in the financial service industry. Financial technology (Fintech)  is changing how existing players operate, and it is creating new ways to deliver core services like saving, investing, borrowing, and insuring. The course provides an overview of the most significant technological advances that are radically changing the industry, focusing on AI and Blockchain. The Sudents will learn to analyze how these technologies create value in the financial industry by lowering frictions — from unit processing cost, through asymmetric information and network effects.

    This course integrates a strategic discussion of the competitive landscape and the market opportunities for new entrants, with an in-depth understanding of the technologies and their applications on the areas in which these technologies are driving changes (Lending, Clearing, and Trading).
    The objective of this course is to offer students the ability to understand, propose, critically analyze, and influence innovations in the field of financial technology (Fintech), as well as to understand the basics about blockchain technology and blockchain-based systems. The ultimate goal of the course is to train the students to be able to take the correct managerial decisions when analyzing and/or developing Fintech and blockchain systems.

  • Accounting

    Accounting is the “language of business” by measuring, processing, and communicating financial and non-financial information about a firm’s economic activities. Many, if not most, quantitative analyses and applications anchor on accounting data that is stored in the firm’s general ledger and information system such as SAP Financials and Accounting. Therefore, at least some basic understanding of how accounting data is prepared and which underlying concepts stand behind the numbers and terminologies used in many managerial decision tools seems essential for data analysts working in organizations. 

    In this course, you will study the basic concepts underlying accounting data that is retrieved from the firm’s information systems and communicated externally in the financial reports, as well as discuss traditional methods of analyzing financial statement data. The course covers:

    • The fundamentals of managerial and cost accounting data collected for internal decisions such as product or process costing, cost estimation or budgeting. 
    • The basic concepts behind financial accounting data that firms are required to disclose externally to the public and that stakeholders use in their decisions.
    • The classic methods of financial statement analysis and financial modelling based on forecasted pro-forma financial statements in business planning.

    The overall objective of the course is to deliver a better understanding of the “data-points” that you (a) internally collect and analyze from the firm’s accounting-based information systems, or (b) externally collect and analyze from other firm’s financial reports and filings. The course is advisable for everyone that aims at working with financial data in the future.

  • Marketing Analytics

    Companies are currently spending millions of dollars on data-gathering initiatives, but few are successfully capitalizing on all this data to generate revenue and increase profit. Converting data into increased business performance requires the ability to extract insights from data through analytics. Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI) of marketing efforts. With a profound understanding of marketing analytics, marketers will be more efficient and will improve the performance of their marketing actions and minimize wasted marketing dollars.

    In this course participants will learn state-of-the-art models and analytical approaches for a better understanding of consumers, customers, markets and competitors as well as increasing efficiency of marketing actions and enhancing competitive advantage. By the end of the course, participants will be able to make sense of the information and knowledge available and create marketing strategy and programs based on both analytical arguments and quantitative metrics. Via cases and real-life applications, participants will

    • derive insights from data for important marketing decisions such as segmentation, targeting, brand perception and positioning, marketing resource allocation, demand forecasting, advertising and pricing;
    • master the selection and use of various models and analytical approaches, and
    • develop the confidence and skills to successfully justify their strategic and tactical marketing decisions using advanced analytics and state-of-the-art marketing metrics.

    The overall objective of the course is to train analytical thinking skills by analyzing and framing marketing problems and using problem-solving techniques to create marketing intelligence, all while considering ethical issues. Overall, participants will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable marketing insights.

  • Supply Chain Analytics

    Supply chain management is among the areas in which analytical methods have a particularly high impact. It is concerned with all activities aimed at satisfying customer demand. As such, it is paramount to the creation of business value. Notable complexities arise from manifold interdependencies between supply chain processes, resulting in intricate trade-offs, and from the interplay between different supply chain members, each having their own objectives. Supply chain analytics provides powerful levers to counter these challenges, resulting in better decisions and, ultimately, maximized performance.

    • In this course, you will learn about the linkages between supply chain management and firm performance and get to know major levers for managing supply chain processes both upstream and downstream.
    • You will recognize key supply chain challenges and understand how supply chain analytics can help tackle them.
    • You will become familiar with core methods of supply chain analytics and modeling and learn how to apply them.
  • HR/People Analytics

    People are often referred to as the most important asset of a company, data now as the oil of the 21st century. It is therefore not surprising that people analytics is seen as a crucial factor in human resource management practice. Instead of listening to intuition and gut feeling, analytical approaches and insights can now drive decision-making in companies. People analytics facilitates this data-driven decision-making to improve decision quality in various areas of HR such as employee engagement, recruitment, performance management, and leadership.

    • The aim of the course is to provide current methods, analytical approaches and metrics to address current topics in HR.
    • Participants will learn how to analyze HR-related data and apply an evidence-based approach to management.


  • Data Management

    In order to derive value from data and to bring its full potential into business processes, a profound understanding of data management methods as well as organizational and technical measures is required.

    • In this course you will learn the basics of data storage and database management systems and acquire basic skills in data cleansing and data quality to facilitate data use and ensure data quality in companies and in data science projects. 
    • The basics of metadata and metadata standards, methods for solving basic data integration problems as well as data models and software architectures for the integration of different data types are covered.
    • Ethically adequate treatment and processing of data will also be discussed.
    • In addition, the following topics will be taught in a practice-oriented way, using company examples: Relational databases, document-based databases, NoSQL, data manipulation, access to data sources, web APIs, web crawling and parsing of text data.
  • Machine Learning for Business

    Machine learning is becoming increasingly important for strategic decision-making in companies. In the course Machine Learning for Business, students learn the basic concepts of data-driven knowledge induction. They are introduced to machine learning tools and best practices, as well as to machine learning problems and tasks that are commonly found in companies. Students will learn how to translate data-driven business problems into machine learning scenarios and select the best machine learning models for those scenarios. 

    • The course covers the basics of classical unsupervised learning (such as matrix factorization, data clustering, and outlier detection) and traditional supervised machine learning models (e.g., logistic regression, random forests and support vector machines). 
    • The course also covers the more recent deep learning methods based on (deep) neural architectures: autoencoders, feed-forward networks, convolutional networks, and recurrent networks.
    • The course also covers aspects of implementation of a machine learning project in a business environment and touches on responsibility and ethical aspects of using machine learning within the company.
  • Practical UX Design

    The course aimed to provide a basic understanding and practical hands-on tools for utilizing the user experience design to achieve better digital products. We plan to understand the state of mind needed to create and measure winning user experience. We will learn how to define interface goals correctly and how to evaluate a good design, we will get to know the human factors causing our users to see the screen in a different way that we do, we will learn how to place elements correctly on a desktop screen and how to communicate with users, and we will explore the guidelines of planning our mobile UX strategy and how to rethink our interfaces when they come to smartphones. The participants will evaluate an existing interface, redesign it, create a prototype and perform usability tests to assess it, and we will wrap it up with practical tips on managing UX innovation.

  • Organizational Behavior

    Data Science fundamentally changes the organization of a company and managers need to consider how they govern analytics within the organization. The entry barrier of data science initiatives is still very high for many companies and it is not yet to be a silver bullet for organizational performance. On the one hand, there is a lack of know-how and well-established routines, and on the other hand, organizational inertia, difficulties in recruiting the right people and other inhibiting aspects are often observed.
    In this course you will learn how to overcome organizational barriers and acquire the knowledge and skills necessary to define and/or manage data science divisions within your organization. You will be able to carefully consider data governance structures and demonstrate how data-driven decisions can be integrated into a business organization.

    • In this course you will learn the basic principles of organizational behavior in context of data science as well as psychological theories that explain human behavior in the workplace.
    • The course aims to balance theory and practical application by focusing on theories that can be applied to real-world organizational problems in context of analytics. 
    • Further topics are: decision making, data governance, teamwork and successful team leadership.
  • Decision Technology

    In recent years, computers and the availability of software have changed the way companies analyze, evaluate, and optimize decisions. This course provides the foundation for the use of decision technologies to solve complex management problems in various business areas. You will learn a combination of Management Science/Operations Research Techniques (MS/OR) and Operations Management Techniques (OM), with a focus on prescriptive analytics. You will get familiar with the concept of Modelling and Optimization to structure optimization problems, to solve them using software, and to generate insights how to manage variability in today’s businesses.

  • Text and Image Analysis

    Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Two business related domains where Deep Learning models have proven to be very successful are (1) Text Analytics and Text Mining (2) Computer Vision and Image Mining. This course covers business related applications in these two areas.  

    Computer Vision allows companies to extract information from visual data, such as photos or video sequences. In some areas, machines have already surpassed human performance in terms of object recognition. Applications range from error detection in high-speed assembly lines to autonomous robots and the identification of products or people in social media.

    • The goal of the course is to learn basic methods and tools of computer vision. This course comprises methods for feature recognition, image classification or convolutional neural networks and motion estimation.
    • You will learn about the intuitions and mathematics of the methods from a theoretical perspective and will also see how computer vision can be successfully used in companies based on real applications and examples.

    In the digital age, techniques to automatically process textual content have become ubiquitous. The term text analytics refers to methods for analyzing unstructured textual data such as customer opinions, social media text, wikis or forums, contracts and legal documents, books or even company archives. The course covers the basic concepts of text mining and Natural Language Processing (NLP) as well as tools and best practices for automatically obtaining actionable information from large amounts of unstructured text. 

    • The course covers the basics of natural language processing – text preprocessing, syntactic analysis (capturing structure) and semantic analysis (capturing meaning) of text
    • The course also focuses on standard text analysis approaches for tasks and scenarios commonly found in companies and generally in the business setting: information extraction, document classification, business process mining, and sentiment analysis
    • The course will introduce the necessary theoretical concepts, but it will primarily focus on practical applications and introduce a range of tools and software libraries for analyzing text and inducing actionable knowledge from various sources of unstructured textual data.

All information is subject to approval by the university committees. Therefore, changes to study design and content as well as admission requirements are still possible.

Contact Person

Stefanie Klug
Admissions Manager Master in Management Analytics (Full-Time)


Located in the heart of the German and European economy, Mannheim Business School (MBS), the umbrella organization for management education at the University of Mannheim, is considered to be one of the leading institutions of its kind in Germany and is continuously ranked as Germany’s #1.