Methods and Tools for Data Analytics

Link al trailer del corso: https://www.edvance.it/edvance/app/home
Target: studentesse e studenti di CdS triennali
Lingua: eng
Il corso appartiene ad una serie?: No
Breve Descrizione:

This course introduces the fundamental methods and tools for data analytics, guiding students step by step through the key stages of working with data.

 

Starting from raw datasets, students learn how to understand their structure and assess their quality, preparing them for analysis through cleaning and preprocessing. The modules explore how to analyze data using statistical techniques, identifying patterns, relationships, and potential issues, such as outliers or misleading aggregations.

 

Finally, the course focuses on data visualization, showing how to effectively communicate insights through clear and meaningful charts. Throughout the lessons, emphasis is placed not only on applying methods, but also on choosing the right tools based on the questions you want to answer and the audience you are addressing.

 

By the end of the course, students are able to transform data into meaningful insights and communicate them effectively to support informed decision-making.
Informazioni Base:

The course “Methods and Tools for Data Analytics” is organized into three parts, each composed of several modules. Together, they guide you through the key steps of working with data: understanding what data is and how to assess its quality, analyzing data using appropriate methods, and communicating results effectively through the right visualizations. A more detailed description follows:

Part 1: Introduction to Data

The first part focuses on understanding what data is, how to assess its quality, and prepare it for analysis through cleaning and preprocessing.

  • Module 1: Introduction to Data

  • Module 2: Data Quality

  • Module 3: Data Preprocessing

Part 2: Data Analysis 

The second part focuses on analyzing data using statistical techniques, with the goal of identifying patterns, relationships, and potential issues.

  • Module 4: Summary Statistics

  • Module 5: Multivariate Summary Statistics

Part 3: Data Visualization

The third part is dedicated to data visualization, showing how to communicate insights effectively through clear and meaningful charts.

  • Module 6: Introduction to Data Visualization

  • Module 7: Basic Visualizations

  • Module 8: Advanced Visualizations

Each module includes video lectures, notebooks, and additional references, providing both the necessary background and complementary perspectives on the concepts discussed.
Risultati Attesi:

Knowledge and understanding

  • Describe the main types of data and their characteristics in the context of data analysis.

  • Explain issues related to data quality and the methods used to assess it.

  • Interpret univariate and multivariate descriptive statistical measures and their role in data exploration.

  • Explain the concepts of correlation and causation in the context of multivariate analysis.

  • Select appropriate types of charts based on the context.

 

Applying knowledge and understanding

  • Use data cleaning and preprocessing techniques to prepare datasets for analysis.

  • Calculate and use descriptive statistics to summarize data.

  • Analyze relationships between variables, distinguishing between correlation and causation.

  • Construct appropriate visualizations to represent distributions and relationships in data.

  • Select analysis and visualization methods based on the problem, data type, and objectives.

 

Making judgments

  • Evaluate the quality and reliability of a dataset in relation to the goals of the analysis.

  • Assess the correctness and transparency in the use of data and visualizations.

  • Argue critically about results derived from data analysis.

  • Distinguish between correlation and causation in different analytical contexts.

  • Select appropriate methods and representations to avoid misleading interpretations.

 

Communication skills

  • Interpret and discuss charts and tables clearly and effectively.

  • Present analysis results using appropriate visualizations.

  • Explain statistical concepts and results in an accessible way to non-expert audiences.

  • Use clear and appropriate language to communicate data and results.

  • Reason about methodological and interpretative choices made during the analysis.

 

Learning skills

  • Recognize problematic situations related to the misuse of data and visualizations.

  • Select tools and digital resources to further develop data analysis skills.

  • Compare different representations of the same dataset to assess their effectiveness.

  • Plan self-education and continuous development in data methods and tools.
Strategia di valutazione:

Superamento di quiz.
Prerequisiti:

Non ci sono prerequisiti.
Livello EQF: EQF Level 6
ISCED-F: 01 Education
Categoria: Tecnologie dell'Informazione e della Comunicazione
SDGs: QUALITY EDUCATION
Docenti:

Alessia Antelmi
Carico Lavoro Totale (in ore/settimana): 7
Numero settimane del corso: 1
Contatti:

alessia.antelmi@unito.it