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The Mannheim Master in Management Analytics & AI 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.
The Mannheim Master in Management Analytics & AI 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. (Click to enlarge the chart)
The Business Fundamentals courses are an integral part of the program. They will effectively guarantee that:
They moreover provide basic knowledge for later more specialized courses.
In the first part of the program, courses lay the professional foundation in the three core areas: Management Analytics, Analytics Technologies and Analytics Methods. After that, courses cover a wide range of topics to help you develop data-driven business models, accelerate change, and improve strategies and decisions. Courses include for example Data Science for Business, Artificial Intelligence and Machine Learning Fundamentals, Strategic Management, Organizational Change, Marketing Analytics, Managing (Big) Data, or Data Ethics.
During your studies, you will acquire skills in the latest programming languages, like Python or R.
Together with a team of fellow students, you will apply the specialist knowledge and methodologies acquired throughout your studies in a comprehensive practical project, either finding a solution to a business challenge at one of our partner companies or creating a business plan for a new product or company. Examples of recent projects:
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.
A multi-day field trip with your peers is an opportunity to broaden your horizons, extend your network and learn from visiting an Analytics hub within Germany, accommodation included.
The Social Class Project provides a meaningful project management challenge while also giving students the opportunity to make a difference in the community.
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 you 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, you 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, you 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, you will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable marketing insights.
From research to modeling to forecasting, every aspect of finance is more driven by data and analytics than ever before. Data science, and in particular statistical and mathematical methods, is now part of most financial activities and is changing the financial services industry inexorably. This course will help you gain the foundational knowledge of data analytics in finance and apply them to create a framework for financial strategies that meet the needs of your company. From fine-tuning customer sales to assessing corporate credit risk, you'll learn to apply the analytics principles that enable informed decision making in this growing industry. In this course, you 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.
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.
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 the basic principles of organizational behavior and change management in context of data science. In particular, we will discuss how to increase data literacy, 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 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 data governance, teamwork and successful team leadership.
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. But companies are innovative not only when they introduce new products or services, but also when they develop new ways of creating and proposing value. In this context, holistic innovation management empowers companies to create a progressive and sustainable future that promotes the digital transformation of business models.
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. The main focus of this course lies on the organization, administration and governance of large volumes of both structured and unstructured data.
Digital innovation continues to transform our lives. These rapid changes pose significant strategic, technological, economic and communicative/integrative challenges for companies across all sectors. Organizations that are effective in using data will win in the economies of the mid-21st century. These must-have core competencies include data analysis, machine learning, data visualizations, data mining, predictive analytics and deep learning. Organizations that won't or can't digitally transform will go the way of Blockbuster or Border's Bookstore. The organization that better harnesses the power of data to create a superior customer experience will thrive in the new business realities. Therefore, in this course, we address questions such as: How do you build the data transformation roadmap to give your organization a competitive advantage? Or how will you digitally transform your organization to give the best customer experience?
You will leave the program with a new understanding of what big data and analytics can do for your business and how you can best utilize and allocate resources in these areas. In this course, you will learn the practical tools and methods used to build a digital organization. The first course provides you the theoretical background of the dynamic capabilities, fitness landscapes, and strategic foresight framework to transform an organization.
Data science is powerful, but with this power comes a host of obligations and responsibilities that professionals in this field need to be aware of, and to behave and decide in an ethical manner. As “big data” gets bigger and bigger, and applications of data science permeate a wider and wider range of different aspects of our lives, new and important ethical issues are arising all the time. This course is about ethics related to data and data science and provides you with the opportunity to clarify ethical issues and ambiguities when dealing with data. Building on ethics shared values that help distinguish right from wrong. Through this course, you will learn who owns data, how we value different aspects of privacy, how to obtain informed consent, and what it means to develop sustainable and fair algorithms to be used for decision making.
The aim of the course is to provide participants with frameworks to discuss and analyze ethical issues, algorithmic challenges, and managerial decisions that arise when addressing business problems via the lens of data science. The objectives of the course are therefore as follows:
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.
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.
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 you 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. It also 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 you 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, you will be able
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 you to key concepts, tools and practices of data science from a managerial perspective. Data is no longer used in companies only for operational decisions, but also for strategic decisions in all areas. Furthermore, data is the catalyst for innovation and productivity. However, the value of data assets comes from how it is used within an organization, which determines how important it is, and ultimately what monetary value can be determined. In this course you will learn how to capture the value of data science in business according to four pillars:
The methodological objectives of the course are to provide you with the analytical tools that will help you 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 you 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. Moreover, the course teaches you how to model functions and how to make simulations to generate further knowledge.
Perceiving information and extracting insights from data is one of the major challenges in management analytics. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched.“Data Science for Business II” is designed to empower you in the use of analytical and computational tools of predictive and prescriptive analyticsto generate knowledge. This course exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of structured and unstructured data aiming at supporting the operator on the shopfloor.
In particular the course aims to provide you with a profound introduction in how to use machine learning, clustering techniques, classifiers, and network science tools, such that you 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 you apply the learnings from the first day onwards, solving cases and problems.
The overall objective of this course is that you understand and are comfortable using advance analytics tools. The completion of this course shall position you 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”, you learn the basics and concepts as well as tools and practices of decision making under uncertainty and (im)perfect information. You learn how to implement practice-oriented decision problems using commonly used software packages and how to structure and improve decision processes to make better decisions and provide a recommendation to the company:
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.
Artificial intelligence and machine learning is becoming increasingly important for strategic decision-making in companies. In the course “Artificial Intelligence and Machine Learning Fundamentals” you learn the basic concepts of data-driven knowledge induction. You are introduced to machine learning tools and best practices, as well as to machine learning problems and tasks that are commonly found in companies. You will learn how to translate data-driven business problems into machine learning scenarios and select the best machine learning models for those scenarios.
In the digital age, techniques to automatically process textual and image content have become ubiquitous. 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 course “Machine Learning Lab: Computer Vision and Natural-Language Processing (NLP)” covers the basic concepts of text and image mining, natural language processing and computer vision as well as tools and best practices for automatically obtaining actionable information from large amounts of unstructured text, images and videos.