SM/0084 - DATA AND MODELS
Academic Year 2021/2022
Free text for the University
MASSIMO DI FRANCESCO (Tit.)
- Teaching style
- Lingua Insegnamento
|[60/61] COMPUTER SCIENCE||[61/00 - Ord. 2016] PERCORSO COMUNE||6||48|
1. Knowledge and understanding skills.
This course is provided to the students attending the 2nd year of the Bachelor Degree in Computer Science. The course is designed to introduce students to the fundamental techniques of using data and quantitative models using the concepts of probability, statistical inference, simulation and optimization. The various topics are introduced from realistic case studies or examples, which are adopted to infer the fundamental concepts for addressing problems successfully. Next, the methods are run and the outcomes are discussed. Generally speaking, this course aims to teach students how these quantitative methods can make a difference in several areas, when they are wisely used.
2. Ability to apply knowledge and understanding.
Students must demonstrate their problem-solving ability. They must identify and use the fundamental concepts to solve realistic problems.
3. Autonomy of judgment.
Students will develop autonomy of judgment by analysing the relevance of the motivating case-studies, discussing the utilization of data, checking the viability of the proposed methodologies and discussing outcomes.
4. Communicative Skills.
Students are requested to present the solutions of problems in an ordered and coherent way.
5. Learning Skills.
The course provides students with adequate preparation to understand more advanced books of Probability, Statistics and Operations Research. Therefore, it enables them to expand their knowledge autonomously in the future.
1. Knowledge. The course requires a good understanding of the basic concepts of Mathematics, which can be learned in the first year of the Bachelor Degree program in Computer Science.
2. Skill. Students should be able to apply basic methodologies of mathematical analysis (e.g. graph of elementary functions, calculation of derivatives and integrals).
3. Competences. No prior technical expertise is required.
Prerequisite courses. According the regulation of the Course in Computer Science, the exam of “Matematica Discreta” must be taken and passed before the exam of “Dati e Modelli”.
Decision Analysis (2 hours)
Fundamentals of Discrete Probability (18 hours)
Continuous Probability Distributions and their Applications (6 hours)
Statistical sampling (8 hours)
Simulation Modeling: Concepts and Practice (2 hours)
Regression models: Concepts and Practice (6 hours)
Linear and discrete optimization (6 hours)
The course consists of 48 hours of lectures. Recitation periods are run for 24 hours to review and reinforce material covered in the lectures. Finally, the professor provides regular support to students throughout the course by ad-hoc meetings and e-mails.
Verification of learning
Students are requested to demonstrate their knowledge of the specific terminology, the concepts presented during lectures and the know-how to use them for solving realistic problems. Students are evaluated by a written exam on the first part of the program and three write-ups on Simulation or Linear Regression and (Linear or Discrete) Optimization. An optional oral interview could be requested by the student to increase the final mark. The professor could also ask for the interview in order to evaluate the student more carefully.
The written exam has 3 or 4 exercises and results in 22 points at most. The write-ups must be typically made in cooperation with another student, are supervised by the tutor or the professor and contribute to 9 points in the final mark (3 points for each write-up). The optional interview on the theoretical topics results in at most 3 points of the maximum final mark. The final mark is the sum of the former points.
• The final mark ranges between 18/30 and 22/30 in the case of sufficient knowledge of the specific terminology, correct application of the methodological concepts and sufficient presentation of the concepts and results.
• The final mark ranges between 22/30 and 26/30 in the case of good knowledge of the terminology, good application of the methodological concepts and a good presentation of concepts and results.
• The final mark ranges between 27/30 and 30 cum laude in the case of an excellent mastery of specific terminology, a critical application of the methodological concepts and a clear display of concepts and results.
Students are recommended to check their level of preparation during recitations. They will test their skills by practicing with exercises and comparing their results to those presented by the Professor or the Teaching Assistant.
Dimitris Bertsimas and Robert Freund "Data, Models and Decisions, The fundamentals of Management Science", 2004, Dynamic Ideas. ISBN0-9759146-0-X
M.R. Spiegel, J. Shiller, R. Alu Srinivasan. Probability and Statistics – Third Edition. McGraw-Hill, 2009. ISBN: 978-0-07-154426-9
The main teaching-supporting tool is the elearning platform (https://elearning.unica.it/), where additional information is available (e.g. a course diary reporting the topics of each lesson and further teaching files). Additional online support will be provided according to the evolution of the COVID-19 pandemic.