Statistics for Big Data for Economics and Business
A.Y. 2021/2022
Learning objectives
This course aims at introducing and illustrating specific statistical, IT and machine learning methodologies for the analysis of Big Data in economic, business and financial applications. The course will focus mainly on the Python programming language, which is by far the most used in Big Data applications, but some parts will be devoted to the R language and other more classical languages such as Java. On the statistical side, supervised and unsupervised statistical learning themes will be proposed with some reference to Bayesian statistics.
Expected learning outcomes
At the end of the course, students will have acquired adequate statistical and programming skills allowing for mastering the tools necessary for the analysis of Big Data and the extrapolation of information of interest in the economic, business and financial fields.
Lesson period: Third trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Third trimester
Teaching methods.
Classes will be held both in presence and on the Microsoft Teams platform in synchronous mode.
Syllabus and reference material.
The syllabus and the reference material will not change in case classes will return to be held "in person".
Verification of learning and evaluation criteria.
The exam will take place in presence with a multiple choice test.
The exam, in particular, will be aimed at:
- ensure the achievement of objectives in terms of knowledge and understanding;
- ascertain the ability to apply knowledge and understanding through the discussion of specific cases in which topics of the course will be applied;
- verify the student's autonomy in developing their own attitudes on the topics of the course.
Classes will be held both in presence and on the Microsoft Teams platform in synchronous mode.
Syllabus and reference material.
The syllabus and the reference material will not change in case classes will return to be held "in person".
Verification of learning and evaluation criteria.
The exam will take place in presence with a multiple choice test.
The exam, in particular, will be aimed at:
- ensure the achievement of objectives in terms of knowledge and understanding;
- ascertain the ability to apply knowledge and understanding through the discussion of specific cases in which topics of the course will be applied;
- verify the student's autonomy in developing their own attitudes on the topics of the course.
Course syllabus
FIRST PART :
1) DATA SCIENCE TECHNIQUES: supervised models
1.1 Multiple linear regression model
1.2 Generalized linear models (logit, probit and tobit)
2) DATA SCIENCE TECHNIQUES: unsupervised models
2.1 cluster analysis
2.2 principal components analysis
2.3 cross-validation
2.4 text mining
SECOND PART :
1) Introduction to programming in R and Python for statistical and economic applications
2) Introduction to cloud computing
3) Introduction to web scraping
4) Introduction to relational and non-relational databases
5) Introduction to Hadoop for big data processing
1) DATA SCIENCE TECHNIQUES: supervised models
1.1 Multiple linear regression model
1.2 Generalized linear models (logit, probit and tobit)
2) DATA SCIENCE TECHNIQUES: unsupervised models
2.1 cluster analysis
2.2 principal components analysis
2.3 cross-validation
2.4 text mining
SECOND PART :
1) Introduction to programming in R and Python for statistical and economic applications
2) Introduction to cloud computing
3) Introduction to web scraping
4) Introduction to relational and non-relational databases
5) Introduction to Hadoop for big data processing
Prerequisites for admission
Knowledge of basic statistical and mathematical techniques. Knowledge of some programming techniques is useful but not essential.
Teaching methods
Classes will be carried out with the active involvement of the students, especially in the programming part. They will often be invited to actively follow (i.e. also on their personal laptops) steps of computer programs proposed in the classroom together with the teacher, in a "what-if" approach. They will also work in gro ups to share and increase the effectiveness of their active learning.
Teaching Resources
James, Witten, Hastie, Tibshirani (2013). Introduction to Statistical Learning, Springer
Wiktorski, (2019). Data-intensive Systems, Springer.
Sosinsky (2010). Cloud Computing Bible, Wiley
Raschka, Mirjalili (2013). Python Machine Learning
Wiktorski, (2019). Data-intensive Systems, Springer.
Sosinsky (2010). Cloud Computing Bible, Wiley
Raschka, Mirjalili (2013). Python Machine Learning
Assessment methods and Criteria
The exam will consist of a test with questions involving multiple answers. During the course some assignments will be proposed (both in the classroom and to be returned in the short term) which will contribute to the final score.
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
Lessons: 40 hours
Professor:
Manzi Giancarlo