Key Information
About the content
Through a combination of lectures, business case studies, and hands-on learning this course provides an introduction to data analytics techniques and their application in business.
The case studies explored will illustrate how companies are leveraging different sources of data, including “big data,” with different analytical techniques, to improve performance. You will receive hands-on learning through a free web-based graphical development environment that will allow you to practice using some of these tools themselves. You will also gain an understanding of the many possibilities for applying data science in business, and will be able to consider additional learning opportunities to gain further depth.
This course is an excellent resource for managers who see the opportunity to use data analytics in business but do not have the skills and background to engage with data analytics themselves.
Syllabus
- The many different data science techniques and their applicability in business via case studies
- Handling of data analytics with a graphical development environment, which makes advanced tools easily accessible without coding
- How to conduct and interpret some basic data science activities, including:
- A simple scatter plot, to visually assess relationships between two or more quantities;
- A basic SQL query, to understand how to pull data from multiple interrelated sources;
- A basic hypothesis test, to understand statistical significance and its impact;
- A basic machine learning experiment, to understand what machine learning is and how to interpret its output.
Instructors
Amitabh Sinha
Former Associate Professor of Technology and Operations
The University of Michigan
Sanjeev Kumar
Lecturer of Technology & Operations
The University of Michigan
Content Designer
Platform
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Il faut bien plus de 12 heures pour finir ce cours qui est très intéressant mais aussi très exigeant et dense, on aborde des sujets très techniques tels que le langage SQL, les probabilités ou la régression linéaire. Les "homeworks" sont aussi exigeants et demandent beaucoup de travail (essais, exercices sur Azure ML, exercices d'analyse de données). Je dirais que pour bien réussir ce cours et avoir la certification il faut 20h/25h au total, soit 3/4h par semaines sur 6 semaines.
Il faut bien plus de 12 heures pour finir ce cours qui est très intéressant mais aussi très exigeant et dense, on aborde des sujets très techniques tels que le langage SQL, les probabilités ou la régression linéaire. Les "homeworks" sont aussi exigeants et demandent beaucoup de travail (essais, exercices sur Azure ML, exercices d'analyse de données). Je dirais que pour bien réussir ce cours et avoir la certification il faut 20h/25h au total, soit 3/4h par semaines sur 6 semaines.
