The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues. The course covers terminology and concepts, tools and methods, use cases and success stories of data science applications. The course explains what is Data Science and why it is so hyped. It discusses 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, and 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.
学習内容
What is data science
How data science, machine learning, and data-driven innovation can benefit business outcomes
Foundational concepts and intuitions about machine learning techniques
習得するスキル
シラバス - 本コースの学習内容
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.
講師
Marco Brambilla
Associate Professor
Emanuele Della Valle
prof.EIT Digital について
ミラノ工科大学（Politecnico di Milano）について
よくある質問
いつ講座や課題にアクセスできるようになりますか？
修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題（該当する場合）にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。
修了証を購入すると何を行えるようになりますか？
修了証を購入する際、コースのすべての教材（採点課題を含む）にアクセスできます。コースを完了すると、電子修了証が成果のページに追加されます。そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
返金ポリシーについて教えてください。
学資援助はありますか？
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
