Management Analytics in Practice Certificate

Courses and Course Content

• Understanding Analytics in Management

  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.

• Python Q&A

  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.

• European GDPR Session

  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 & Machine Learning Lab

  AI & 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.

• Data Science Lab

  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.