Management Analytics in Practice Certificate

Courses and Course Content

• Understanding Analytics in Management, October 13-14, 2023

  Management Analytics / Leveraging big data for management decisions and transforming companies into data-driven organizations

  Big data and artificial intelligence are an important driver for the digital transformation of companies and today’s society. Therefore, the first module introduces the perspective of digitization and business transformation that runs through the entire program – how to create growth and business value, such as more sales or profits, with big data and artificial intelligence. We discuss how organizations can use big data and artificial intelligence to create and expand competitive advantages.

  Contents of the module/course:

  • Data Literacy & Data Intuition (Developing an understanding of psychological biases in decision-making; Learning how to ask questions that can be answered through the analysis of data; Enriching the data-driven dialogue)                   
  • Management of Big Data (collection and storage of big data; pre-processing of big data; data quality – defining and measuring the quality of big data; data governance; data security)
  • Data Visualization (the psychology of perception; designing effective diagrams; visualizing large and unstructured data; telling effective reports with data)
  • Building a Data Science and Analytics Team (Basic concepts for building and leading a high-performing analytics team)
  • Data Ethics (Data Ownership, Anonymity; Data Validity; Algorithmic Fairness)

  At the end of the course, participants will be able to make meaningful use of the data and information available in organizations and to develop management strategies and programs based on both analytical arguments and quantitative metrics. The overall goal of the first module is to train analytical thinking skills by discussing proven and successful big data and artificial intelligence applications from participants’ industries and business areas.

• Data Science Lab, November 10-11, 2023

  Data Science Lab for a Competitive Advantage / Creating and maintaining competitive advantages with data science

  In this course, participants will be introduced to the most modern data science methods. The overarching goal of the course is for participants to learn how to analyze and gain insights from data using various data science methods and to feel comfortable incorporating (big) data into day-to-day work. In addition, the participants learn how to model data using certain functions and how to determine the future development of company KPIs on the basis of data.

  By the end of the course, participants will know all the essential analytical tools that will help them use data science to extract insights from data in a robust, correct and actionable way. Although the course has a solid theoretical basis, it is designed in such a way that participants apply what they have learned in practice from day one, solving cases and problems in the fields of marketing, logistics, HR or production. The course starts with the basics of statistics and ends with the introduction of machine learning models to include temporal components in predictive analytics.

  Please note: Date is subject to change.

• European GDPR Session, November 24, 2023

  In companies, there is often a great amount of uncertainty how data can be used from a legal perspective, so that it doesn’t violate laws such as GDPR. Due to this uncertainty, companies often decide to completely discontinue the use of data and thus stop the often necessary transformation. In this module you will get an introduction to the legal framework and be shown that companies have far greater freedom to use data for analysis, growth and value creation.

• AI and Machine Learning Lab, December 8-9, 2023

  AI and Machine Learning is changing the way companies do business. In particular, unstructured data such as images, texts and videos are rapidly gaining in importance. In this course, you will acquire fundamental knowledge of a wide range of AI and machine learning methods. In addition, you will gain basic knowledge of a wide range of methods for the automatic analysis of textual content (from lexicon-based methods to more recent transformer-based deep neural networks). In addition, you will gain insights into various real-world applications and ethical considerations related to machine learning.

  Goals of the course:

  • Understand the basics of AI and machine learning
  • Getting to know numerous practical examples of leading companies
  • Learn about responsible AI and machine learning, biases, and challenges
  • Building a toolbox of different supervised and unsupervised machine learning methods
  • Learn numerous practical examples from leading companies in the field of text analysis
  • Understand how machines “read” – and generate – text
  • Learn about different machine learning methods and neural network architectures for text analysis tasks
  • Learn how to apply different machine learning approaches to solving real-world problems

  The course offers a mix of theoretical foundations, several applied case studies, real-world examples and break-out sessions. Overall, you will experience an interactive learning environment.

• Python Tutorial, October 15-26, 2023

  This course introduces you to the fundamentals of the Python programming language, with a focus on topics required to apply Python in a data science context. Because of its ease of use and abundance of data science libraries, Python is a valuable tool for 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 by showing illustrative examples of Python code that run interactively in the presentation. Programming exercises are offered to deepen the topics discussed.

• Elective 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.

• Elective 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.

• Elective Financial Analytics

  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.

• Elective 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.