Management Analytics Certificate

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Business Analytics Fundamentals in the Palm of Your Hand

Big Data, Digital Innovation, Business Analytics and Management Analytics are crucial tools for the upcoming challenges for businesses. Digitization is changing the business world fast and fundamentally.

In order to keep up with these developments and leverage them for your company's success, we offer you our flexible and modular Business Analytics Certificate. You can either benefit from the whole intense ten-week program or book single classes to pinpoint and address specific topics in your business. All courses are taught online.

Understand the relevance of Business Analytics for strategic decision-making. Get to know methods and tools for data analysis. Learn how to identify new business opportunities and create value for your own business.

Upcoming Information Events

24. Jun 2020

Get to know our new modular and flexible open course program - the Management…

The Certificate at a Glance

  • Target Group: Decision-makers, project and data managers, business development managers
  • Application Requirements: An academic degree and fluency in English
  • Application Process: Online Application
  • Application Deadline: August 28th, 2020
  • Programm Start & Duration: October 5th - December 10th, 2020
  • Program Structure: 10-week program (Monday-Friday): 11 online courses, 2 online tutorials
  • Price for Entire Certificate: 12,000 € + 19% VAT*
  • Price for Single Course Day: 800 € + 19% VAT*

* Please note that we are able to grant a special discount offer to our current participants and MBS alumni:

  • Price for Entire Certificate: 9,000 € + 7% VAT
  • Price for Single Course Day: 500 € + 7% VAT

For further questions please get in contact with Manon Pfeifer.

Program Structure and Course Contents

  • Week 1: Data Science for Business Managers I (Oct. 5-9, 2020)

    Core course without tutorial: 4 days (Oct. 6-9, 2020)

    Contents:
    "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.

    Course Objectives:
    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.

    Course Composition and Teaching Methods:
    The participants of this course will learn the foundation of statistical analytical methods, and will apply them to real-world cases. This course covers a wide range of methods and their application.
    The course is split in nine parts:

    1. Data Science for Managers
    2. Value creation through data analysis – how data help to transform the company.
    3. A/B Testing and experiments
    4. Ethical data management
    5. Linear Regression I
      1. Understand relationships
      2. “Eye-Conometrics”: Graphics and economic elationships
      3. Introduction to equation estimation
      4. Making sense out of the residuals
      5. From data to action
    6. Linear Regression II
      1. Understanding complex relationships in the company
      2. Observe what others cannot see
    7. Regression diagnostics
      1. Using statistical tools to improve the company’s performance
      2. Differences between useful and useless tools
    8. Predictive Analytics
      1. Incorporate time to your models
      2. Time series
      3. Machine learning to account for time in your models
    9. Working with unstructured data
      1. The real work of a data scientist: data preparation
      2. Generate value out of unstructured data.

    These concepts and tools will be introduced in class. Multi-Competence Teams (MCTs) formed by the students will solve cases in which they will have the opportunity to apply the learned concepts. By doing this, they will acquire skills that will help them increase their performance in corporate and entrepreneurial environments.

    Lecturers:

    • Prof. Florian Stahl (1 day)
    • Dr. José Parra-Moyano (3 days)

     

     

  • Week 2: Analytical and Critical Thinking (Oct. 12-16, 2020)

    Core course without tutorial: 2 days (Oct. 15-16, 2020)

    Contents:
    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.

    Course Objectives:
    The aim of the course is to provide participants with essential thought-provoking impulses for a critical and re-sponsible 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

    Lecturer:

    • Prof. Christina Schamp

     

  • Week 3: Data Management (Oct. 19-23, 2020)

    Core course without tutorial: 2 days (Oct. 20-21, 2020)

    Contents:
    In the course Data Management, participants learn the basics and concepts as well as tools and practices of data management of structured and unstructured large data sets from a busi-ness perspective. The participants of the course learn the basics of data storage as well as basic procedures of data cleansing and data retrieval in order to facilitate data usage and im-prove data quality in companies. In addition, the course covers the basics of metadata and metadata standards, methods for solving basic data integration problems as well as data mod-els and software architectures for the integration of different data types. Furthermore, implica-tions of data management from an organizational perspective are discussed and explained.


    Course Objectives:
    The aim of the course is to provide participants with the most important concepts, tools and methods of managing large and heterogeneous data in companies, allowing them to make valuable use of data in the company:

    •  Understanding alternative database concepts: Concepts behind NoSQL and NewSQL data-bases, NoSQL tools and products
    •  Fundamentals of metadata and metadata standards
    •  Data Warehousing and Business Intelligence: Methods for solving fundamental data integra-tion problems
    •  Data Integration: Data models and software architectures for the integration of different data types
    •  Measurement of data quality
    •  Data governance and organizational aspects of data management

    Lecturer:

    • Prof. Florian Stahl
  • Week 4: Decision-Making under Uncertainty (Oct. 26-30, 2020)

    Core course without tutorial: 2 days (Oct. 26-27, 2020)

    Contents:
    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 perfect as well as imperfect information. The participants of the course learn how to structure and improve decision processes to make better decisions. Despite introducing basic concepts to deal with decision problems under uncertainty, participants also learn how to implement practice-oriented decision problems using commonly used software packages.

    Course Objectives:
    The aim of the course is to provide participants with the most important concepts, tools and methods of making decisions under uncertainty and (im)perfect information. The learned con-tent helps participants to structure complex decision problems under uncertainty 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

    Lecturer:

    • Prof. Danja Sonntag

  • Week 5: Marketing Analytics (Nov. 2-6, 2020)

    Core course without tutorial: 4 days (Nov. 2-5, 2020)

    Contents:
    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.
    This course covers the three pillars of analytics – descriptive, predictive and prescriptive – within the marketing context. Students will be exposed to several methods such as linear regression, logistic regression, multinomial regression and machine learning methods (e.g., neural networks and Support Vector Machines). We will learn how to employ these methods for managerial decisions such as demand forecasting, pricing, and valuing customers. Overall, students will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable insights.

    Course Objectives:
    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 1) derive insights from data for important marketing decisions such as segmentation, targeting, brand perception and positioning, marketing resource allocation, demand forecasting, advertising and pricing; 2) master the selection and use of various models and analytical approaches, and 3) 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.

    Course Composition and Teaching Methods:
    The participants of this course will learn the application of analytical models and statistical methods to generate insights from data about consumer and customer behavior, market dynamics and competitors, and will apply them to real-world cases. This course also covers a wide range of specific analytical methods and their application in marketing management (i.e. customer management and brand management) and developing marketing strategy (i.e. pricing strategy, advertising strategy and digital marketing).

    The course is split in six lecture units:

    1. Introduction in Marketing and Marketing Analytics

    • Difference between Normative/Prescriptive and Descriptive/Predictive Analytics

    Marketing Insights

    2. Consumer and Customer Analytics: Analyzing and Predicting Preferences and Choice

    • Binary Brand and Product Choice
    • Multinomial Brand and Product Choice
    • Conjoint Analysis 
    • Analyzing and Modeling Purchase Quantity and Timing

    3. Market Analytics: Analyzing and Predicting Aggregated Demand and Competition

    • Product and Brand Sales
    • Market Basket Analysis
    • Forecasting New Product Sales
    • Market and Customer Segmentation

    Marketing Management: Increasing Efficiency of Marketing & Competitive Advantage through Analytics

    4. Customer Management

    • Customer Relationship Management (CRM) analytics
    • Customer Lifetime Value in Different Situations
    • Allocating resources between acquisition and retention
    • Customer Journey Analytics

    5. Brand Management

    • Measuring Brand Perception Using Big Data
    • Brand Audit through Social Listening

    Marking Strategy: Increasing Efficiency of Marketing Instruments through Analytics

    6. Advertising Analytics

    • Effectiveness 
    • Media selection 
    • Attribution models 

    These concepts and tools will be introduced in class. Multi-Competence Teams (MCTs) formed by the students will solve cases in which they will have the opportunity to apply the learned concepts. By doing this, they will acquire skills that will help them increase their performance in marketing environments.

    Lecturer:

    • Prof. Florian Stahl
  • Week 6: Data Science for Business Managers II (Nov. 9-13, 2020)

    Core course without tutorial: 4 days (Nov. 9-12, 2020)

    Contents:
    "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 de-signed 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.

    Course Objectives:
    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.

    Course Composition and Teaching Methods:
    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 participants of this course will learn the foundation of statistical analytical methods, and will apply them to real-world cases. This course covers a wide range of methods and their application.
    The course is split in dour parts. Each part has a teaching class in the morning and a case session in the afternoon. In the afternoon session of the third day, students will solve a graded case that they will present in the morning of the fourth day.

    1. Predictive machine learning – Complex time forecasting using ML algorithms
    2. Machine learning for classification I – Clustering and Trees
    3. Machine learning for classification II – Naive Bayesian and Ensemble methods
    4. Network Science – Connecting the dots

    These concepts and tools will be introduced in class. Multi-Competence Teams (MCTs) formed by the students will solve cases in which they will have the opportunity to apply the learned concepts. By doing this, they will acquire skills that will help them increase their performance in corporate and entrepreneurial environments.

    Lecturer:

    • Dr. José Parra-Moyano
  • Week 7: Machine Learning (Nov. 16-20, 2020)

    Core course without tutorial: 2 days (Nov. 17-18, 2020)

    Contents:
    In the course Machine Learning, participants learn the basic concepts of data-driven learning. They are introduced to machine learning tools and best practices, as well as to machine learning problems and tasks commonly found in companies and more generally in the domain of business. The participants learn how to translate data-driven business problems into machine learning use cases and how to test these use cases for feasibility and impact. The course also covers aspects of implementation of a machine learning project in a business environment and also touches on responsibility and ethical aspects of using machine learning within the company. The participants of the course will learn 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). They will also be introduced to more recent methods based on deep learning (e.g., autoencoders, feed-forward networks, convolutional networks and recurrent networks). 

    Course Objectives:
    The aim of the course is to equip participants with the foundational knowledge on important concepts, tools and methods of supervised and unsupervised machine learning methods, al-lowing them to make valuable use of labeled and unlabeled data in the company. The following are, inter alia, the knowledge granules and skills that the participants are expected to acquire through this course:

    • Clustering and anomaly detection using Scikit-Learn
    • Sparse representations and matrix decomposition (use-case: recommender system)
    • Supervised Classification and Regression with Scikit-Learn
    • Unsupervised Deep Learning using PyTorch
    • Supervised Deep Learning using PyTorch

    The participants will be exposed to real-world use cases that require application of different machine learning methods and functionality and will learn to plan, justify, and implement a successful problem solving strategy based on one or more machine learning models.

     

    Lecturers:

    • Prof. Margret Keuper
    • Prof. Goran Glavaš

       

  • Week 8: FinTech and Blockchain (Nov. 23-27, 2020)

    Core course without tutorial: 2 days (Nov. 24-25, 2020)

    Contents:
    Financial Technology (FinTech) 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.

    Course Objectives:
    Financial Technology (FinTech) 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.

    Course Composition and Teaching Methods:
    The participants of this course will learn the foundation of blockchain protocols, and understand their technical, economical, and managerial implications.
    The course is designed as a combination of theory, business cases and workshops in which students will themselves face unsolved managerial challenges and explore how blockchain technology can (or cannot) solve them. The course is split 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.

    These concepts and tools will be introduced in class. Multi-Competence Teams (MCTs) formed by the students will solve cases in which they will have the opportunity to apply the learned concepts. By doing this, they will acquire skills that will help them increase their performance in corporate and entrepreneurial environments.

     

    Lecturer:

    • Dr. José Parra-Moyano
  • Week 9: Data Visualization and Text Analysis (Nov. 30-Dec. 4, 2020)

    Core courses: Data Visualization: 2 days (Nov. 30- Dec. 1, 2020); Text Analysis: 2 days (Dec. 3-4, 2020)

    Contents - Text Analysis:
    In the digital age, techniques to automatically process textual content have become ubiquitous. In the course Text Analysis, participants learn 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 textual data. The participants of the course learn the basics of natural language processing – text preprocessing, syntactic analysis (capturing structure) and semantic analysis (capturing meaning) of text -- as well as the common approaches for tasks and use-cases commonly found in companies and generally in the business setting: information extraction, document classification, business process mining, and sentiment analysis. While the course will introduce the necessary theoretical concepts, 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.

    Course Objectives - Text Analysis:
    The aim of the course is to equip participants with the understanding of most important concepts, tools and methods of processing textual data and extracting actionable information from text. The focus on company use cases and applications from the business domain will enable participants to add business value in company settings abundant with text data. The following are the expected learning outcomes of the course: 

    1.    Understanding of the basic building blocks of text mining and natural language processing systems: text preprocessing, capturing structure in text (syntax), capturing meaning of text (semantics).
    2.    Knowledge and understanding of effective approaches to tasks frequently found in business use-cases and scenarios:   

    • Document analysis: document clustering and classification
    • Information extraction, e.g., (named) entity extraction
    • (Business) process mining from textual descriptions
    • Sentiment analysis and processing of social media text

    3.    Familiarity with tools and libraries for text processing and information extraction from text (as well as related machine learning libraries): e.g., scikit-learn, SpaCy, NLTK, Keras
    4.    Knowledge of metrics for evaluating the quality of text analysis methods and tools

    Lecturer:

    • Prof. Goran Glavaš
    • 2nd lecturer tba

  • Week 10: Computer Vision & Image Mining (Dec. 7-10, 2020)

    Core course without tutorial: 2 days (Dec. 8-9, 2020)

    Contents:
    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 this course, participants learn basic methods and tools of computer vision and image mining. This includes methods of 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.

    Course Objectives:
    The aim of the course is to provide participants with the most important concepts, tools and methods of computer vision and image mining, allowing them to make valuable use of visual data in the company:

    • Feature Extraction
    • Image Classification
    • Object Detection
    • Instance recognition
    • Motion Estimation
    • Object Tracking

    Lecturer:

    • Prof. Margret Keuper

Lecturers and Experts

Our courses are taught by high-profile academics, which have gathered extensive knowledge about Management Analytics through their studies and practical experiences. Please find a short overview in the following section.

Prof. Dr. Florian Stahl

  • Academic Director for the Mannheim Master in Management Analytics
  • Chair holder Quantitative Marketing and Consumer Analytics at the University of Mannheim

Research and teaching engagements:

  • Assistant Professor of Quantitative Marketing in the Department of Business Administration at the University of Zürich
  • Postdoctoral research fellow at Columbia Business School in New York
  • PhD in Business Economics from the University of St. Gallen
  • Master’s degree in Economics from the University of Zürich

Academic Awards:

  • IJRM Best Paper Award 2014
  • Winner Robert D. Buzzell MSI Best Paper Award 2012
  • Winner H. Paul Root Award 2012
  • Finalist Harold H. Maynard Award 2012

Expertise and focus:

  • Digital Marketing
  • Social Media and Social Networks
  • Consumer Behavior and Consumer Choice Models
  • Branding and Brand Management
  • Pricing and Price Strategies

 

Prof. Dr. Margret Keuper

Prof. Dr. Margret Keuper is an assistant professor (Juniorprofessorin) at the University of Mannheim. Her research is focused on image processing and computer vision. She obtained her doctorate in December 2012 from the University of Freiburg, under the supervision of Prof. Thomas Brox. She worked as a postdoctoral researcher at the University of Freiburg and as an invited reseacher at Max-Planck-Institute for Informatics in Saarbrücken and has been appointed Juniorprofessorin at the University of Mannheim in April 2017. Her research encompasses grouping problems inherent in computer vision applications such as image and motion segmentation, semantic segmentation, both on single images and videos and multiple object tracking.

Her research results have been published in renowned, peer-reviewed computer vision conferences and journals. She is a PC member of major computer vision and machine learning conferences (ICCV, ECCV, CVPR, NeurIPS, GCPR, AAAI) and has written reviews for renowned journals (TPAMI, TIP).

Prof. Dr. Christina Schamp

Christina Schamp holds a Bachelor's degree in Business Administration from the University of St. Gallen, Switzerland and a Master's degree in Social Psychology from the London School of Economics, UK. Before she earned her doctorate at the University of Hamburg at the Institute for Marketing & Customer Insights, she worked as a consultant at McKinsey and Company for several years. In 2019 Christina Schamp moved to the University of Mannheim as assistant professor for empirical research methods.

Fields of interest / research:

Christina Schamp's research projects deal with consumer decision making related to societal change, for example the growing ethical awareness of consumers or the increasing influence of social media. 

Her current research projects focus on moral decision-making in the digital sphere and investigate phenomena at the interface between technological trends and ethical issues. Christina Schamp's research results have been published in high-ranking journals such as the Journal of the Academy of Marketing Science and the International Journal of Research in Marketing.

Prof. Dr. Goran Glavaš

Prof. Dr. Goran Glavaš holds the position of assistant professor (Juniorprofessor) of Text Analytics for Interdisciplinary Research at the University of Mannheim.

Fields of interest / research:

  • Lexical and Computational Semantics
  • Information Extraction
  • Multilingual and Cross-lingual NLP
  • NLP Applications for Social Sciences and Humanities
  • Information Retrieval & Web Search
  • Text Analytics
  • Knowledge Management
  • Web Mining

Prof. Dr. Danja R. Sonntag

Danja Sonntag is assistant professor (Juniorprofessorin) of Operations Management since February of 2018.

In 2017, she completed her doctorate at Otto von Guericke University Magdeburg with a dissertation on the topic of “Safety stock determination in production systems with random yield and positive lead times”.

Fields of interest / research:

  • Inventory management with stochastic production yield
  • Multi-Echelon Inventory Systems

Dr. José Parra-Moyano

José Parra-Moyano is an Assistant Professor for Blockchain and Digitalization at the Copenhagen Business School. As an economist trained in statistical methods, he is an expert in the application of technology in business administration, and has several academic publications on this topic in peerreviewed journals. Besides that, José has been appointed as Global Shaper by the World Economic Forum, is a Research Fellow at the Blockchain Centre of the University College in London, and has been listed as FORBES 30 UNDER 30. José gives courses about blockchain, statistics, and business analytics in different universities at master and MBA level, and advises companies and startups in the development of their strategies and in the integration of technology into their core business.

Contact Person

Manon Jana Pfeifer
Admissions Mannheim Master in Management Analytics
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