Intelligent Data Analytics 2023
PhD in Computational Quantitative Biology
Aim and Description of the Course
A Bio-inspired artificial intelligence and data analytics has shown the potential to truly improve the work of research centres, industries and businesses. Recognising the enormous value of the data collected and the importance of extracting information from it has been a milestone in knowledge engineering. This has highlighted the importance of having data analysis expertise for extracting meaningful information from often unstructured data, and advanced capabilities to learn and obtaining knowledge from the data.
This course focuses on the foudamental, advances and application of Data Analytics and Information Extraction for Scientific Computing.
Data analysis methods and techniques will be introduced. Laboratory sessions will be included. Python programming methods and libraries dedicated to data analysis will be explained in detail. Both open source and commercial data processing tools will be used.
The course is structured in twelve lessons: a one-hour introductory lesson and eleven two-hour lessons.
It will be the held on the Microsoft Teams Platform.
Team link for course registration and lessons is:
Teacher Bio
Flora Amato is Associate Professor at the Department of Electrical Engineering and Information Technology of University of Naples Federico II, where she has been working since 2006. Her research activities include both theoretical and experimental work in Artificial Intelligence, Knowledge Management and Information Integration. She has been a visiting researcher at the Department de Ciències de la Computació of the Universitat Politècnica de Catalunya and at the Department of Computer Science of the University of Maryland (USA). She has participated as Scientific manager, Work Package or Task coordinator in many international and national projects. She is the author of more than 180 research papers, published in Conference Proceedings and International Journals such as IEEE Transaction on Industrial Informatics, IEEE Transaction on Automation Science and Engineering, Elsevier Journal of Knowledge-Based Systems, Pattern Recognition Letters and Computer and Security.
Programme
Lecture Series Part I: General aspects, Data Analysis Python programming lab
Introduction and Overview of the Course (Flora Amato)
20th Mar 10:30-12:30
Understanding Data, Exploratory Data Analysis (Flora Amato)
22nd Mar 8.30-10.30
Lab Session: Data analysis and manipulation, useful Libraries (Flora Amato)
23rd Mar 10:30-12:30
The stage of Data Analysis, Recent Trends in Data Mining, CRISP-DM (CRoss Industry Standard Process for Data Mining) methodology, The IEEE 70xx standard (Flora Amato)
29th Mar 8:30-10:30
Lab Session: Data Preparation and Exploration (Flora Amato)
30th Mar 10:30-12:30
Lab Session: Data Characterization (Flora Amato)
5th Apr 8:30-10:30
Lecture Series Part II: Data discovery and Interactive Dashboards using tools.
Data discovery and Interactive Dashboards with Microsoft Power BI tool. Data Warehouse. Report Creation. (Flora Amato)
12th Apr 8:30-10:30
Combine Data from Multiple Sources, Connect to a GitHub repo and Use of Cognitive Services in Power BI. (Flora Amato)
13th Apr 10:30-12:30
Transform, shape, and model data, Understand model relationships, and Multidimensional Models. (Flora Amato)
19th Apr 8:30-10:30
Analytics, Data view, Relationship view. (Flora Amato)
20th Apr 10:30-12:30
Measures, Metrics (Flora Amato)
26th Apr 8:30-10:30
Time series analysis, Data Forecasting (Flora Amato)
27th Apr 10:30-12:30