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Becoming a management analytics student at Reichman University and Mannheim Business School means that you will study both at one of the most prolific entrepreneurial tech hubs and in the heart of the European economy, with the potential to gain access to and future employment at a budding startup, hidden champion, or leading corporation. Data scientists are in great demand at companies of all sizes and in all industries. According to a World Economic Forum study, data analysts and scientists, AI and machine learning specialists, as well as big data and digital transformation specialists are among the top 10 most promising job profiles for the coming years. They address data-driven decision-making problems in various areas of the business such as finance, marketing, operations, human resources, and business intelligence.
Introductory courses like ‘Data Science for Business’ or ‘Data Literacy & Data Intuition’ lay the foundation for transforming data into meaningful insights and actions. Advanced courses then help you deepen your knowledge in specific areas such as Machine Learning, UX Design, Financial Analytics, or Marketing Analytics.
Working in a team of fellow students, you will apply the specialist knowledge and methodologies acquired throughout your studies in a comprehensive practical project at one of our partner companies.
During your studies, you will acquire skills in the latest programming languages such as Python and R.
Essential program elements such as the Business Analytics Master Project require that you work with classmates in heterogeneous teams, just as you would at your workplace. Soft skills courses foster the necessary key qualities for being a digital driver in your company.
In the course of the modules, you will regularly get insights from industry experts and opportunities to visit companies in Israel and Germany to learn about their data-driven projects.
Become a digital transformation expert. Here you will find a detailed description of the individual modules and course components of the Master in Management Analytics.
“Data Science for Business I: From Data To Value” is designed to introduce participants to key concepts, tools, and practices of data science from a managerial perspective. In today's business world, managers are constantly surrounded by data. This is the same data considered the new oil of the 21st century, and in many cases, this flood of data is overwhelming. To obtain greater business value from the firm's data resources, managers are expected to understand the capabilities of Data Science and how they can be integrated with their business strategy. This course aims to treat the gap many managers face in coping with the understanding of how the technology of data can be used to enrich products and services and solve business problems.
The goal of the course is to equip managers with an intuitive understanding of Data Science concepts and how those can be used to improve their business strategy. Another 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 in both Excel and Python.
In today's business world, managers are constantly surrounded by data. This is the same data considered the new oil of the 21st century, and in many cases, this flood of data is overwhelming. To obtain greater business value from the firm's data resources, managers are expected to understand the capabilities of Data Science and how they can be integrated with their business strategy. This course aims to treat the gap many managers face in coping with the understanding of how the technology of data can be used to enrich products and services and solve business problems. The goal of the course is to equip managers with an intuitive understanding of Data Science concepts and how those can be used to improve their business strategy.
“Data Science for Business II: Predictive and Prescriptive Analytics” is designed to deepen the knowledge of the participants in the use of analytical and computational tools to generate knowledge. The course aims to provide students a profound introduction in how to use machine learning, clustering techniques, classifiers, and network science tools, such that the students can generate various managerial insights and knowledge out of data. While the course has a solid theoretical foundation, it is designed to be a hands-on course in which the students apply the learnings from the first day onwards, solving cases and problems. The software used in class to solve the cases is Python or R.
In this course participants will continue their exposure to state-of-the-art methodologies for data science. The main objective of the course is that the students learn how to generate information, knowledge, and wisdom from data by applying advanced data-science methods. The overall objective of this course is that participants understand and are comfortable using advance analytics tools. The completion of this course shall position the students as experts in the managerial application of data analytics tools and set them above the average data-driven business manager.
The ubiquity of information and large amounts of data nowadays requires the increased ability to analyze and interpret them in a reflective and critical manner. Moreover, any form of data analysis, for example the use of machine learning algorithms, is inherently dependent on the impartiality of the data scientist.
This course is designed to sensitize the participants to how cognitive biases - independent of the intentions of the data scientist - can impair critical thinking when dealing with data. The course draws on the psychology of critical thinking and explains the most common forms of cognitive biases. The course shows the implications for the collection, analysis and interpretation of data by analyzing a variety of examples from everyday management, the media or scientific publications. Finally, the course explains which "debiasing" techniques have proven to be effective in improving the quality of thinking and decision-making and how effective and undistorted reasoning can be used when dealing with data. The aim of the course is to provide participants with essential thought-provoking impulses for a critical and responsible approach to the collection, analysis and interpretation of data.
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.
In the last twenty-five years, Israel has gained prominence as one of the leading technology and innovation centers in data analytics. The strategy, entrepreneurship and innovation course will make participants acquainted with the data analytics ecosystem in Israel. It will allow students to follow a hands-on process of initiating, testing and implementing a data related product/service new venture from the ideation stages to the preparation of a business plan and presentation to investors. Participants will learn practical models, tools and methodologies critical to the creation of data centered start-up companies, including subject matters that focus on the strategy and competitive advantages of such startups, product-market strategy, entrepreneurial finance, and business models.
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.
This course is designed in order to expose the students to the effects of technological developments evolution in the financial world. In this course we will discuss the change in the traditional way of investment management, either for institutions or for private investors, due to the use of quantitative investing and alternative data sets. Investment tools, which were available only to professional investors, are now available to private investors. Therefore, it is important to understand their essence and the challenges in the process of using them.
Upon completion of the course, the students will:
Financial accounting is concerned with reporting a company's financial position, operating results, and cash flows to investors, creditors, and other economic decision-makers. However, many people see financial statements as a mass of detail that conveys little or no information about how well a company is really doing. This course attempts to dispel that misconception by illuminating an often confusing and intimidating subject. Although it is true that accounting information sometimes obscures more than it enlightens, there is much that is useful (and even essential) to those interested in making rational investment and credit-granting decisions.
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.
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.
This course introduces students to advanced Machine Learning techniques in Python. Our focus will be on classification algorithms (SVM, Logistic Regression), regression algorithms (Linear Regression, Lasso, Ridge), and ensemble methods (Random Forest). Moreover, we will explore the software tools that constitute the Python data science ecosystem.
The course aims to provide a basic understanding and practical hands-on tools for utilizing the user experience design to achieve better digital products. We plan to understand the state of mind needed to create and measure winning user experience. We will learn how to define interface goals correctly and how to evaluate a good design, we will get to know the human factors causing our users to see the screen in a different way that we do, we will learn how to place elements correctly on a desktop screen and how to communicate with users, and we will explore the guidelines of planning our mobile UX strategy and how to rethink our interfaces when they come to smartphones. The participants will evaluate an existing interface, redesign it, create a prototype and perform usability tests to assess it, and we will wrap it up with practical tips on managing UX innovation.
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.
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:
Performing in Organizations: Career success depends not only on performance in terms of deliverables and meeting objectives, but also on “performance” on the organizational stage: building coalitions, mapping/managing social networks, understanding/changing cultures, and, most importantly, working in teams. The goal of this course is to enhance students’ interpersonal skills to help them successfully navigate the social side of their organizational and professional life.
These will be the three main themes of the course:
Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Two business related domains where Deep Learning models have proven to be very successful are (1) Text Analytics and Text Mining (2) Computer Vision and Image Mining. This course covers business related applications in these two areas.
Computer Vision allows companies to extract information from visual data, such as photos or video sequences. In some areas, machines have already surpassed human performance in terms of object recognition. Applications range from error detection in high-speed assembly lines to autonomous robots and the identification of products or people in social media.
In the digital age, techniques to automatically process textual content have become ubiquitous. The term text analytics refers to methods for analyzing unstructured textual data such as customer opinions, social media text, wikis or forums, contracts and legal documents, books or even company archives. The course covers the basic concepts of text mining and Natural Language Processing (NLP) as well as tools and best practices for automatically obtaining actionable information from large amounts of unstructured text.
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.