Data science is the study of extracting value from data. It combines insights, techniques, and tools from several disciplines including, among others, computer science, statistics and applied mathematics. This course provides an introduction to data science for students from all disciplines including those with no prior analytics or big data experience to solve business problems through a data-driven approach. It will focus on developing new insights and understanding of business performance based on data and statistical methods (6hrs). In the second part, a panel of real data will be analyzed (6hrs). Students will be able to identify new trends and business opportunities as well as make strategic decisions using data and stats.
This subject is aimed at students with little or no programming experience. The course is mostly theoretical, though basic Python code will be presented to support the analysis of some real case studies with concrete examples.
The course will include the following topics:
- Data Analytic Thinking
- Data processing and big data
- Data science and data mining
- Business problems and data science solutions
- From business problems to data mining tasks
- Data mining process
- Data science toolboxes
- Data science and business strategy
The course is structured with theoretical and practical parts. Lessons and exercises will be integrated: for each concept, we will see theory and practice. Students will apply data science methods and tools learned during the course to define business strategy recommendations by leveraging the analysis of real-world data sets.
 “Data Science for Business: What you need to know about data mining and data-analytic thinking”, by Foster Provost, Tom Fawcett, Publisher: O’Reilly Media, Inc., ISBN: 9781449361327.