Processi Cognitivi e Tecnologie (Anno Accademico 2022/2023) - Cyberpsychology (in partnership con la Berlin School of Business and Innovation - BSBI)

Data Science

CFU: 10

Lingua contenuti:Inglese

Descrizione dell'insegnamento

The aim of this course is to introduce students to the concepts of Data Science, Data Scientist and Big Data. Particular attention will be paid to the presence of data in our lives as well as to its production. Furthermore, the different types of databases and different analysis techniques will be presented. In this context the basic concepts of statistics will be reviewed and the use of R, one of the most popular open-source statistical software, and the integrated development environment (IDE) Rstudio will be discussed.

Also, practical examples of Machine Learning, Sentiment Analysis, Data Mining and Network Analysis will be presented, and the limitations and advantages of big data will be highlighted. Throughout the course, particular attention will be paid to applications in the clinical field.

Indicative Content:

 Techniques of analysis in Data Science

 Introduction to R

 Correlation and Correlation matrix

 Network analysis: practical example

Scopi

The aims of this unit are:

A1 Provide students with knowledge about the presence of data in our lives as well as to its

production

A2 Developing an adequate knowledge for learners about the concepts of Data Science,

Data Scientist and Big Data

A3 Making students familiar with the different types of databases and different analysis

techniques

A4 Ensuring that students know about the concept of statistics as well as the use of R

On satisfactory completion of the unit, you will be able to:

LO1 Analyse the potential of a database.

LO2 Have a better understanding on the use of Machine Learning, Sentiment Analysis, Data

Mining and Network Analysis

LO3 Better understand the methods and how to choose the most appropriate technique in

terms of the type of data and the characteristics of each project

LO4 Critically analyze and correctly interpret the research results of other scientists

Testi

READING LISTS

Essential

 Foster, I., Ghani, R., Jarmin, R.S., Kreuter, F., & Lane, J. (Eds.). (2020). Big Data and Social Science:

Data Science Methods and Tools for Research and Practice (2nd ed.). Chapman and Hall/CRC.

 Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach; O'Reilly Media, Inc.".

 Tyagi, A.K. (Ed.). (2021). Data Science and Data Analytics: Opportunities and Challenges (1st ed.).

Chapman and Hall/CRC.

 Wickham, H., & Grolemund, G. (2017). R for data science: Import. Tidy, transform, visualize, and model

data, 1.

Recommended

 Huang, S., & Deng, H. (2021). Data Analytics: A Small Data Approach (1st ed.). Chapman and

Hall/CRC.

 Roiger, R.J. (2017). Data Mining: A Tutorial-Based Primer, Second Edition (2nd ed.). Chapman and

Hall/CRC.

 Memon, Q.A., & Khoja, S.A. (Eds.). (2019). Data Science: Theory, Analysis and Applications (1st ed.).

CRC Press.

 Irizarry, R.A. (2019). Introduction to Data Science: Data Analysis and Prediction Algorithms with R (1st

ed.). Chapman and Hall/CRC.

 Ratner, B. (2017). Statistical and Machine-Learning Data Mining: Techniques for Better Predictive

Modeling and Analysis of Big Data, Third Edition (3rd ed.). Chapman and Hall/CRC.

Recommended

Journal Resources:

 SAGE Journals

 APA Journals

 Elsevier

 Pub Med

 Nature Neuroscience

 Neuron

 British Journal of Psychology

 International Journal of Data Science and Analytics

 Neuroscience Informatics

Docente

Nessun Docente attualmente disponibile per questo corso

Elenco delle lezioni

• Lezione n. 1: Data Science and Data Scientists		Valentina Panetta
• Lezione n. 2: Big Data		Valentina Panetta
• Lezione n. 3: Analysis Techniques in Data Science		Valentina Panetta
• Lezione n. 4: Introduction to R		Ilaria Simonelli
• Lezione n. 5: An introduction the calculation of probability		Ilaria Simonelli
• Lezione n. 6: Types of data bases		Ilaria Simonelli
• Lezione n. 7: Frequency, double entry tables and the use of R		Ilaria Simonelli
• Lezione n. 8: An overview of descriptive statistics		Valentina Panetta
• Lezione n. 9: Correlation and correlation matrix		Ilaria Simonelli
• Lezione n. 10: Graphical representations in R		Ilaria Simonelli
• Lezione n. 11: Analysis techniques: regressions		Valentina Panetta
• Lezione n. 12: Introduction to principal component analysis and clustering techniques		Valentina Panetta
• Lezione n. 13: Data science in the clinical field		Valentina Panetta
• Lezione n. 14: Data mining: a practical example		Valentina Panetta
• Lezione n. 15: Sentiment analysis		Valentina Panetta
• Lezione n. 16: Network analysis: a practical example		Valentina Panetta
• Lezione n. 17: Practical application with R: machine learning		Valentina Panetta
• Lezione n. 18: R Markdown		Valentina Panetta

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Processi Cognitivi e Tecnologie (Anno Accademico 2022/2023) - Cyberpsychology (in partnership con la Berlin School of Business and Innovation - BSBI)

Data Science