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Der Mannheim Master in Management Analytics steht ebenso für die richtige Balance zwischen theoretischer Fundierung und Praxisnähe wie auch für eine enge Verzahnung der Bereiche Business, Methoden und Technologie. Auf diese Weise bereiten wir unsere Teilnehmerinnen und Teilnehmer in optimaler Weise auf Ihre zukünftigen Herausforderungen vor.
Alle Angaben vorbehaltlich einer Zustimmung durch die Universitätsgremien. Daher sind Änderungen zu Studienaufbau- und inhalten sowie Zulassungsvoraussetzungen noch möglich.
Wahlkurse werden der Klassengröße und Verfügbarkeit angepasst. Die angegebenen Zertifikate stellen nur eine Auswahl der möglichen Kurse dar. Gegebenenfalls sind diese kostenpflichtig.
Der Mannheim Master in Management Analytics besteht aus aufeinander aufbauenden Modulen, die über einen Zeitraum von 24 Monaten verteilt sind. Diese Struktur erlaubt es Ihnen, während des Programms weiterhin voll berufstätig zu sein und ihr neu erworbenes Wissen direkt am Arbeitsplatz anzuwenden.
In den ersten drei Modulen legen Pflichtkurse die fachliche Basis in den drei Kernbereichen Methoden, Business und Technologie.
Ein hoher Anteil an Wahlkursen ermöglicht Ihnen eine Schwerpunktsetzung gemäß Ihrer beruflichen Interessen und Karriereziele. Hierzu stehen Ihnen verschiedene Tracks zur Verfügung. Sie können sich zum einen entweder in Richtung Statistik oder Machine Learning orientieren, zum anderen Ihren Schwerpunkt auf einzelne betriebliche Funktionen (z.B. Marketing, Finanzen, Operations) legen.
Während des Studiums können Sie Zertifikate in aktuellen Programmiersprachen wie Python und R oder für Tools wie Google Analytics oder Amazon Web Services erwerben.
Gemeinsam mit einem Team aus Mitstudierenden wenden Sie Fachwissen und Methodenkompetenzen, die Sie sich im Laufe des Studiums angeeignet haben, in einem umfassenden Praxisprojekt in einem unserer Partnerunternehmen an.
Teamarbeit bei wesentlichen Studienelementen wie der Masterarbeit und Soft-Skill-Kurse fördern die Stärkung der für Führungspositionen notwendigen Schlüsselqualifikationen.
Optional bietet ein einwöchiger Study Trip die Möglichkeit, den Horizont und das eigene Netzwerk zu erweitern und Einblicke in innovative Unternehmen in Technologiezentren wie dem Silicon Wadi in Israel oder bei den Silicon Vikings in Skandinavien zu gewinnen.
Das Social Sustainability Project gibt den Studierenden die Möglichkeit, als Klasse ein soziales und nachhaltiges Projekt in der Metropolregion Rhein-Neckar zu planen und zu realisieren, Management-Wissen direkt anzuwenden und dabei einen Beitrag für die Gesellschaft zu leisten.
Werden Sie zum Experten für Digitale Transformation. Im Folgenden finden Sie eine detaillierte Beschreibung der einzelnen Module und Inhalte des Mannheim Master in Management Analytics.
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 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):
“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” 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.
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:
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.
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 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.
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.
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.
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.
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 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.
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
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.
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.
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.
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.
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 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
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
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.
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.
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.
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!
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!
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.