Learning objectives
a. Knowledge and understanding:
The purpose is to deal in a quantitative way the relevant information for
the firm using advanced computer programming. The data can come from different sources (internal or sample
surveys or web sites) . The final goal is to provide a rational support for decision
making using computer programming.
b. Applying knowledge and understanding
At the end of the course, the students will have acquired skills on the following topics:
- Basic statistical analysis;
- Multivariate statistical analysis;
- Dimension reduction techniques;
- Classification using supervised and unsupervised methods.
Skills and understanding skills applied
At the end of the course the student will be able to:
- understand the different phases which are at the root of statistical analysis of data
- translate the conceptual tools into empirical rules for the management of data coming from different sources and in different formats;
- plan and manage a statistical survey and understand what are the advantages and disadvantages of the different techniques of data collection
- develop distinctive skills in the area of statistical data analysis.
c. Making judgements
At the end of the course the student will be able to:
- evaluate the best statistical techniques to use;
- identify the best practices in managing data coming from different sources;
- evaluate the effectiveness of the different statistical techniques.
d. Communication skills:
Through lectures managed in an interactive way, company testimonials and group work, the student will be able to: - clearly communicate, in a concise, timely and coherent manner, to different interlocutors (both academic and business), information and concepts (including complex ones) related to statistical data analysis;
- communicate effectively using an appropriate technical language;
e. Learning skills
The course aims to transfer the ability to translate the statistical principles into empirical rules of decision. The main topics are detailed through the presentation of successful data analysis case studies. At the end of the course the students will have gained the ability to expand and update the level and range of the knowledge acquired from lessons and course textbooks.
Prerequisites
Basic knowledge of mathematics and statistics
Course unit content
Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB and to computer programming.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis. Sentiment analysis. Introduction to machine learning.
Full programme
Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB and to computer programming.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis. Introduction to machine learning and sentiment analysis.
Bibliography
Material downloadable from web site http://www.riani.it/SDE
Teaching methods
Frontal lessons with PC and practical lesson using Matlab. Additional material can be downloaded from the web site of the course http://www.riani.it/SDE
Assessment methods and criteria
Exam using the computer.
Knowledge and understanding are assessed by
methodological questions. The ability of
applying knowledge and understanding are assessed by questions on
the interpretation of results. Learning skills
are assessed by questions on the conclusions to be drawn from an
analysis.
Further information is downloadable from http://www.riani.it/SDE.
The range of the votes is 0-30.
The vote "30 cum laude"
will be awarded to particularly deserving students who, in addition to having complied with the requisites necessary to obtain the full evaluation, in the performance of the test have overall demonstrated an appreciable systematic knowledge of the topic, an excellent ability to apply the knowledge acquired to the specific problem in question, a considerable autonomy of judgement, as well as a particular care in the formal drafting of the essay.
During the exam it is not possible to keep notes, mobiles and it is not possible to use Internet resources.
The results of the exam will be published on the usual ESSE3 platform within 3 days.
EXAM AFTER CORONA VIRUS
The exam will be on line. The rules of the exam remain unchanged.
The link to upload the documents on line and the link to join the exam will be shown in the web page of the course http://www.riani.it/SDE
Other information
Additional information can be found from the web site http://www.riani.it/SDE
2030 agenda goals for sustainable development
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