Program Structure and Curriculum

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Innovative and Varied

The Mannheim Master in Management Analytics strikes a balance between theoretical foundation and practical relevance and perfectly integrates business, methodology and technology, preparing our participants for future challenges in the best possible manner.

An Overview of the Program Structure

The Mannheim Master in Management Analytics is organized in modules that build on each other over a period of 24 months. This allows you to retain full-time employment throughout the program and apply your newly acquired knowledge directly at your workplace.

  • Core Courses

    In the first three modules, core courses lay the professional foundation in the three core areas: business, methodology and technology.

  • Elective Courses

    A large proportion of electives allows you to focus on your professional interests and career goals. Various tracks are available for this purpose: You can either concentrate on statistics or machine learning, or focus on specific operational functions such as marketing, finance and operations.

  • Certificates

    During your studies, you can acquire certificates in the latest programming languages such as Python and R, or tools such as Google Analytics and Amazon Web Services.

  • Business Analytics Master Project

    Together with 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.

  • Soft Skills

    Teamwork in essential program elements, such as the Business Analytics Master Project, and soft skills courses foster the consolidation of the necessary key qualities for management positions.

  • Study Trip

    An optional one-week study trip is an opportunity to broaden your horizons, extend your network and learn about innovative companies in technology centers such as Silicon Wadi in Israel and Silicon Vikings in Scandinavia.

  • Social Sustainability Project

    The Social Sustainability Project provides a meaningful project management challenge while also giving students the opportunity to make a difference in the community.

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 Mannheim Master in Management Analytics.

Methodology

  • Analytical and Critical Thinking

    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 collection, 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. 

    By the end of the course, participants should be able to 

    • to reflect critically on their own analytical thinking and data handling
    • to be able to assess external information and analyses with regard to their credibility and quality
    • communicate more effectively in their own argumentation and presentation of results

    To this end, the course provides the most important concepts and tools regarding formal (why is something wrong and how to recognize it) and psychological aspects of critical thinking (why do we tend to draw wrong conclusions and how to avoid them): 

    • Fundamentals of the psychology of critical thinking
    • Overview of the most common cognitive biases in data analytics
    • Sensitivity for statistical pitfalls in data analysis and the responsible handling of big data and machine learning algorithms
    • Overview of proven strategies to neutralize or minimize the negative effects of cognitive bias
    • Tools to improve your own argumentation and presentation of results
       
  • 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 software used in class to solve the cases is R.

    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 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
  • Introduction to Machine Learning

    Machine learning is becoming increasingly important for strategic decision-making in companies. In the course Introduction to Machine Learning, 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.
  • Predictive Analytics

    Predictive analytics is a field of statistics and data analysis that uses data modeling to predict future outcomes. By identifying trends and patterns in past and current data and understanding data relationships, analysts can create models today that can be used to predict the impact of various strategies and decisions in the future.
    Companies that use predictive analytics to increase sales, identify the best talent, develop new products, and make better decisions gain a competitive advantage with these methods. Predicting key business and economic variables is becoming increasingly important as it enables both more objective decisions and improved profitability. 

    • In this course you will learn methods for predicting business and economic variables based on historical data. This includes traditional statistical methods such as regression, time series, multivariate, and econometric models, as well as machine learning methods such as decision tree prediction.
    • The course focuses on the presentation and explanation of methods in the context of substantial business and economic problems using a variety of forecasting methods.
  • 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
  • Computer Vision and Image Mining

    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 not only learn about the intuitions and mathematics of the methods from a theoretical perspective, but will also see how computer vision can be successfully used in companies based on real applications and examples.
  • Text Analytics & Text Mining

    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 Text Analytics & Text Mining covers the basic concepts of text mining and natural language processing 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.

Business

  • 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.
  • 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.

  • Blockchain & FinTech

    Financial technology in general and blockchain in particular are expected to massively impact our economy and our society. The market capitalization of bitcoin and all existing cryptocurrencies reached 800 billion US dollars in 2018 and the business value-add of blockchain is expected to exceed 3.1 trillion USD by 2030. The World Economic Forum expects 10% of the World's GDP to run on blockchain by 2030. In light of these predictions, managers need to understand how blockchain technology works, which new possibilities it offers, and how to make use of it in their respective environments. This course teaches these elements, such that the students can lead the discussions about the managerial implications of blockchain and financial technology.

    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.

    The course is divided in four parts:
    1)    From Bitcoin to blockchain: the protocol behind the engine
    2)    Managerial applications of blockchain
    3)    Workshop: Blockchain as a solution to unsolved challenges
    4)    Flipped classroom: Building knowledge from your projects

  • Social Media Analytics

    In marketing, social media are not only a means of communicating with customers, but also offer the opportunity to better understand customers. Participants will learn how to gain new marketing insights from social media data using analytical methods. Basic skills of social media listening, social monitoring and the importance of countless social media metrics are taught. In addition, it will be shown in a practice-oriented manner how social media data can be used to gain insights into the market structure and consumer perception of a brand. 

    • The aim of the course is to convey current methods and tools of digital marketing and social media analytics. 
    • Course contents are: Methods for measuring and controlling the virality of news and products, viral product design and the integration of the multi-channel experience.
    • The participants learn the methods of randomized experimentation, A/B testing and causal inference for the marketing strategy in a practice-oriented way.
  • Pricing & Revenue Management

    In today's e-business environment, there is an increasing number of unlocked opportunities to increase profits through Pricing & Revenue Management (P&RM). P&RM is a short-term planning instrument in order to effectively match supply and demand and thereby maximize profitability – by selling the right product to the right customer at the right time through the right channel for the right price. P&RM takes into account that on the supply side, resources to produce these products are usually constrained and often perishable, and therefore, the effectiveness of market-related (e.g., pricing) decisions is highly interrelated with resource allocation decisions.

    Today, P&RM is a large revenue generator for several major industries relying on sophisticated decision support systems; Robert Crandall, former Chairman and CEO of American Airlines, has called Revenue Management "the single most important technical development in transportation management since we entered deregulation." While airlines have the longest history of development in P&RM, applications have rapidly diffused beyond airlines to industries such as retailing, hospitality, railways, car rental, telecommunications and financial services, internet service provision, electric utilities, broadcasting and even manufacturing.

    For outside observers, P&RM may seem often like an art. But finally, the most important pillar of P&RM is analytics – including systematic data analysis, forecasting, and powerful optimization that allows taking all market- and supply-related profit drivers simultaneously into account.  

    • This course provides practical insights into P&RM as well as the key concepts, the underlying basic models and state-of-the-art methods and tools of P&RM. In particular you will learn how to use dynamic pricing and capacity allocation strategies to better match supply and demand.
    • Further topics may include product line design, bundling, stochastic dynamic programming, pricing in networks, etc.
    • Through lecture-style class sessions, exercises, and cases, participants will gain insights into the theory and practice of P&RM and enhance their analytical skills.
    • At the end of the course, you will be able to better understand P&RM decisions - one of the most important but least understood management decisions.
  • 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.
  • 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.
  • Strategy & Innovation

    The world's most valuable companies are successful because they make innovation a fundamental part of their business strategy. Strategy and innovation are thus inseparably linked. While the strategy sets the direction for the future, the innovative power within the company ensures that new products and services are developed, new processes are designed and new business models are created.

    • The following current methods and tools for the development of strategic innovations are learned in this course: strategic planning concepts in marketing management, innovation and diffusion processes including theories of diffusion of innovations, innovation models, imitation models and the Bass model. 
    • In addition, the course also covers decision-making behaviour and innovation processes, including adoption versus diffusion, consumer preferences and new product diffusion.
  • Strategic Management

    The course starts with outlining on the strategy of the single business unit, namely the single business firm and a single business in the context of the diversified firm. It deals with the shaping and the execution of strategic moves and the strategic management of businesses in changing, competitive and uncertain environments. It treats the issues of business strategies from the firm's top management perspective with an aim of building up a sustainable competitive advantage. Then the course moves on to deal with growth strategies, focusing on the strategic management of complex firms, those that follow business diversification ("multi-business"), those that operate in a number of countries and geographic areas ("international") and those that combine the two directions. Looking at both, the multi-business and the multi-locations firms, the course identifies the generic strategy-structure-management-control clusters, evaluates their performance, and assesses their potential to create sustained competitive advantage.

    The course aims to enable the participant to

    • acquire the language, concepts, models, mapping and analytical tools that are used in dealing with strategic issues.
    • comprehend the components, complexity and problems of strategically managing a strategic business unit as well as a global diversified corporation. 
    • develop basic skills and knowledge to plan single-business strategies and assess its effectiveness. 
    • develop a strategic perspective of the individual business unit and global diversified corporation. 
       
  • 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.

  • Financial Analytics

    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. 

Technology

  • Operations Research

    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 todays businesses.

  • Tutorial for Programming in R

    This introductory course in R is designed to welcome you to the world of data science using R. Knowing this programming language is a valuable competence in any data-related field of work, which is why these three tutorials will provide you with first insights into the true basics of R, as well as fundamental data manipulation and visualization options using base R. The course is designed to guide you through the necessary steps to becoming proficient in R. Watch the videos discussing and illustrating the content of each tutorial, while following along using the R-Code. Hope you enjoy the course!

  • Tutorial for Programming in Python

    This course introduces you to the foundations of the Python programming language, with a focus on topics that are prerequisites to apply Python in a Data Science context. Due to its ease of use and its wealth of Data Science libraries, Python is a valuable asset in all kinds of data-related tasks. The course is designed to guide you through the necessary steps to learn Python from scratch. The videos introduce new content using illustrative examples of Python code, which are executed interactively in the presentation. Coding exercises are provided to deepen the knowledge of the discussed subjects. Hope you enjoy the course!

  • 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 systrems 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.

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.

Elective courses are subject to class size and availability. The specified certificate courses are just a few of the ones available in this program. Additional costs may apply.

Contact Person

Manon Jana Pfeifer
Admissions Mannheim Master in Management Analytics

MANNHEIM BUSINESS SCHOOL (MBS)

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.

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