Programmstruktur und Curriculum

• Kurse

  In den ersten drei Modulen legen Einführungskurse die fachliche Basis in den drei Kernbereichen Methoden, Business und Technologie. Danach behandeln Kurse eine breite Palette an Themen, die Ihnen dabei helfen, datengetriebene Businessmodelle zu entwickeln, die digitale Transformation voranzutreiben und Entscheidungen und Strategien zu verbessern. Kursthemen sind zum Beispiel: Data Science for Business, Artificial Intelligence and Machine Learning Fundamentals, Strategic Management, Organizational Change – Creating a Data-Driven Culture, Blockchain in Business Management, Marketing Analytics, Managing (Big) Data, Data Ethics.

• Zertifikate

  Während des Studiums können Sie ein Zertifikat in einer aktuellen Programmiersprache erwerben. Sie können zwischen Python und R wählen.

• Business Analytics Masterprojekt

  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 oder Sie erstellen einen Business Plan für ein neues Produkt oder Unternehmen. Aktuelle Projektbeispiele:

  • Implementing a performance dashboard for better decision-making in the sports industry
  • Increasing the use and monetization of data through the introduction of data-driven processes and business models
  • Data analytics in the field of commercial vehicles – practical application and strategic roadmap
  • Develop an algorithm which recommends the perfect roadmap for each customer, including which services and products should be offered at which point of time
  • Political analytics – optimizing politically oriented marketing campaigns

• Soft Skills

  Teamarbeit bei wesentlichen Studienelementen wie der Masterarbeit und Soft-Skill-Kurse fördern die Stärkung der für Führungspositionen notwendigen Schlüsselqualifikationen.

• Field Trip

  Eine mehrtägige Exkursion mit Ihrer Klasse zu einem Analytics Hub in Deutschland bietet Ihnen vielfältige Gelegenheiten zum Lernen, Ihren Horizont zu erweitern und Ihr Netzwerk auszubauen. Die Unterkunft ist inklusive.

• Social Class Project

  Das Social Class 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. 

Business

• Strategic Management: Competitive Advantage through Data

  The flood of data and analytics methods has created pressure to 'know more' before making decisions. At the same time, the pace of business continues to accelerate toward the speed of light. This course starts with outlining 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 in the age of big data and the strategic management of businesses in changing, competitive and uncertain but more and more data-driven 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 through the use of data and the integration of data science in the companies DNA.

  In this course you develop capabilities to tackle business problems and to develop management strategies with a whole new data-driven mindset. In particular, you will learn how to define and establish data strategies that go beyond a set of rules and create a plan for winning in a competitive landscape. However, a data strategy often implies that a business model and whole organization will be transformed. For that, you learn how to identify, define and implement data-driven business models that have taken entire industries by surprise.

  The course aims to enable you to

  • acquire the language, concepts, models, mapping and analytical tools that are used in dealing with strategic issues in the age of big data,
  • 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 and
  • build a big data strategy to generate the business value and deep insights that an organization needs to thrive in a competitive business environment.

• 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 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

  • 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, you will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable marketing insights.

• Financial Analytics and FinTech

  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.

  • 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 analyzed to create models for financial and macro forecasting, quantitative trading and dynamic risk management. 

• 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.

• Blockchain in Business Management

  Blockchain technologies are expected to massively impact our economy and our society. The market capitalization of bitcoin and all existing cryptocurrencies reached 1 trillion US dollars in 2022 and the business value-add of blockchain is expected to exceed 4.1 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 you can lead the discussions about the managerial implications of blockchain and financial technology.

  The objective of this course is to offer you the ability to understand, propose, critically analyze, and influence innovations in the field of blockchain technologies and blockchain-based systems. The ultimate goal of the course is to train you 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:

  • From Bitcoin to blockchain: the protocol behind the engine
  • Managerial applications of blockchain
  • Workshop: Blockchain as a solution to unsolved challenges
  • 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. You 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.
  • You learn the methods of randomized experimentation, A/B testing and causal inference for the marketing strategy in a practice-oriented way.

• 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 Change – Creating a Data-Driven Culture

  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.

• Strategic Innovation and New Data-Driven Business Models

  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.

  • 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. 
  • The course also covers decision-making behavior and innovation processes, including adoption versus diffusion, consumer preferences and new product diffusion.
  • In addition, in this course you learn how companies can leverage information networks and big data to innovate their current business models or to develop new ones. Furthermore, you discuss examples on how companies can leverage internal and external data to generate new business models

Technologie

• Certificate in Programming in R or Python

• Managing (Big) Data: From Database Technology and Data Governance to Data Security and Data Quality

  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.

  • In this course you learn the basics of data storage and database management systems, metadata and metadata standards. Also, methods for solving basic data integration problems as well as data models and software architectures for the integration of different data types are covered.
  • A main focus of this course lies on big data management in terms of ensuring a high level of data quality and accessibility for business intelligence and big data analytics applications. In particular you acquire basic skills in data cleansing and data quality to facilitate data use and ensure data quality in companies and in data science projects. 
  • In addition, the following topics will be taught in a practice-oriented way, using company examples: Relational databases, document-based databases, NoSQL, data manipulation, or access to data sources.

• Data Ethics

  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:

  • Develop fluency in the key ethical terms and concepts related to data science.
  • Learn about algorithmic and data-driven approaches for mitigating biases in AI/ML systems
  • Reason through problems with no clear answer in a systematic manner, taking and defending different viewpoints, and justifying your conclusions in a rigorous manner.
  • Listen, understand and communicate with people of varying opinions, viewpoints, and ideas. Disagreement and debate is expected, as is respectful open communication.

• Data, Analytics and Digital Transformation: From Technology and Data to Process and Organizational Change

  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.

• 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.

• 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.

Methoden

• 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 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 reflect critically on your own analytical thinking and data handling,
  • to assess external information and analyses with regard to their credibility and quality and
  • to communicate more effectively in your 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: From Data to Value

  “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:

  • To develop an appropriate data strategy in order to align efforts and support the business goals.
  • To set up the technology foundations and architecture to manage and operate the data challenges.
  • To establish an operating model to effectively design, build, deploy and operate Data & Analytics initiatives.
  • To implement the right environment and change management to achieve greater data literacy and a data-driven decision-making culture.

  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.

• Data Science for Business II: Predictive and Prescriptive Analytics

  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. 

• Decision Making under Uncertainty

  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: 

  • 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 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

• Artificial Intelligence and Machine Learning Fundamentals

  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.

  • 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.

• Machine Learning Lab: Computer Vision and Natural-Language Processing (NLP)

  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.

  • The course covers the basics of natural language processing – text preprocessing, syntactic analysis (capturing structure) and semantic analysis (capturing meaning) of text. In particular, this 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
  • In this course you learn further basic methods and tools of computer vision. This includes 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.
  • 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 data.