BERGAMO
Dati Generali
Periodo di attività
Syllabus
Obiettivi Formativi
The course introduces the main base Statistics methods and tools for facing the analysis of data of any type.
During our lectures, we will gradually introduce the main steps of a professional data analysis: from collecting data, to setting and defining/classifying datasets and their content, to the first phase of exploratory analysis. Numerical and graphical tools will be deeply introduced and jointly used in order to detect and fix data issues as well as to explore data contents from a multifocal perspective by means of univariate, bivariate and multivariate techniques.
In parallel with the introduction of theoretical concepts and methods, students will be constanctly guided in learning how to properly use software (such as Excel, SAS Studio and Tableau) for practical purposes and for real case studies in order to explore, to synthesize, and to give value to the available information. Consequently, theory lectures and practice labs will be proposed in parallel, in order to help student to immediately apply, in practice, the tools and methods that will be introduced. This learn-by-doing approach will help students not only in better understanding the theory, but also in choosing proper techniques, in properly using them and in critically interpreting and assessing the obtained results in a practical perspective.
Students will acquire a deep knowledge of statistical tools, methods and software: this will allow them to develop professional advanced analyses that will create a relevant added value for their businesses.
Prerequisiti
No prerequisites: this is meant to be a base Statistics course. We will build your statistical knowledge together, step by step.
Metodi didattici
The course consists of frontal interactive lectures stimulating students' contribution and of laboratory sessions, where the introduced techniques are practically implemented using software such as Excel, SAS Studio and Tableau. The ability of students in critically and practically interpreting results will also be encouraged.
Verifica Apprendimento
Written exam of about 100 to 120 minutes. The exam is made by a theoretical part (tests and open questions requiring short answers, other types of questions) and by a practical part (exercises or short applications). The practical part also includes exercises to be developed using SAS Studio and/or Excel.
Other activities could be proposed during the course, in order to integrate the final score.
The exam results (on a 0 to 31 scale) will be published online and will be sent to the students by email (after this, the student is allowed to reject online his/her evaluation). The detailed scores will be published on the eLearning page of the course.
The instructor reserves the right to require an additional oral examination, even after the written examination has been completed, whenever this is deemed necessary to verify the student's effective acquisition of expected knowledge and competencies or to ensure the appropriateness of the final grade.
Contenuti
• Setting data: defining data and variables keywords, setting datasets; data collection and main sources; sampling methods.
• Enhancing quality: how to detect and treat data issues: errors, outliers, missing data.
• Content exploration: data visualization principles and practice; visual analytics software and tools.
• Synthesizing data: tables; main ratios; numerical descriptive measures (level and variability).
• Probability theory: main discrete and continuous distribution; confidence interval estimation.
• Statistical tests: main hypotheses testing (one and two-sample tests, one way ANOVA, chi-square).
• Bivariate to multivariate: correlation analysis; simple and multiple regression analysis; goodness of fit assessment.
• Temporal data: number indexes (simple and complex); time series analysis (for forecasting).
• Multivariate techniques: similarity, distances, main clustering algorithms.
• INTRODUCED SOFTWARE (throughout the whole course): Excel; SAS Studio (SAS); Tableau/PowerBI.