Data Science for Business Innovation

This is your chance to learn all about Data Science for Business innovation and future-proof your career. Match your business experience tech and analytics! The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and managers to foster data-driven innovation. The course explains what Data Science is and why it is so hyped. You will learn: * the value that Data Science can create * the main classes of problems that Data Science can solve * the difference is between descriptive, predictive, and prescriptive analytics * the roles of machine learning and artificial intelligence. From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories, and realistic examples. Following this nano-course, if you wish to further deepen your data science knowledge, you can attend the Data Science for Business Innovation live course https://professionalschool.eitdigital.eu/data-science-for-business-innovation

• Introduction to Data-driven Business
• This module introduces the course and offers some basic overview of the topics. It presents the crucial concepts related to data science and big data and provides an outlook on how to use them in real world settings for increasing business value.
• Terminology and Foundational Concepts
• In this module, you will learn the foundational concepts of machine learning and data science. You will understand how these techniques can be useful in terms of increased business value for organizations, thanks to the discussion of a very well known success story, namely Netflix, which can be deemed as a completely data-driven business. You will also understand how machine learning is different from programming.
• Data Science Methods for Business
• In this module, you will learn the concepts and intuitions about the basic approaches for data analysis, including linear regression, naive Bayes, decision trees, clustering, and logistic regression. All the methods are presented starting from typical business uses and are covered in an intuitive way through a guided explanation of how the approach works on simple examples.
• Challenges and Conclusions
• This module summarizes the concepts learned so far and introduces a set of challenges and risks that data-savvy managers must take into account when deciding for a data-driven strategy.