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BIGDIVE6 DAILY PROGRAM

BIGDIVE6 SCHEDULE DAY-BY-DAY
 
*Schedule definition is still in progress. Organizers might do changes and modifications before lessons start.
 
 

FIRST WEEK SCHEDULE

MONDAY, JUNE 19th
11:00 Welcome at Rinascimenti Sociali
11:30-13:30 Introduction & Get-together
13:30-14:30 Lunch
14:30-15:30 Data Science Intro by ISI Foundation
15:30-16:30 Dev Intro + Git + Python Notebook essentials
TUESDAY, JUNE 20th
09:30-11:30 Enrico Ferro – ISMB Lecture about Data Driven Innovation
The guest lecture, owned by Enrico Ferro, is aimed at discussing and covering following topics:
Introduction to Data-Driven Innovation, Economics of Information, Business models for data-intensive businesses, Data-Driven Governance, Tackling Societal Challenges.
11:30-12:30 Break
12:30-13:30 DataViz Introduction: good and bad examples.
13:30-14:30 Lunch
14:30-16:30 DataViz intensive with D3.JS
WEDNESDAY, JUNE 21th
09:30-11:00 DataViz intensive with D3.JS
11:00-11:30 Break
11:30-13:30 DataViz intensive with D3.JS
13:30-14:30 Lunch
14:30-16:30 DataViz intensive with D3.JS
16:30-17:30 Python Basic [Optional]
THURSDAY, JUNE 22th
09:30-11:00 DataViz intensive with D3.JS
11:00-11:30 Break
11:30-13:30 DataViz intensive with D3.JS
13:30-14:30 Lunch
14:30-16:30 DataViz intensive with D3.JS
16:30-17:30 Python Basic [Optional]
FRIDAY, JUNE 23th
09:30-11:00 Maarten Lambrechts lecture – A 10 year journey into dataviz
A selection of my projects and the tools behind them. In this session, data journalist and data visualization consultant Maarten Lambrechts shares his tortuous path into the field of data visualization. He will show a selection of his work, produced over the course of the past 10 years, and tell a bit about the background, the process and the tools he used. This journey into dataviz will show you how the field evolved, how the tools matured and how you can benefit from learning new data manipulation, visualization and web development tools yourself.
11:00-11:30 Break
11:30-12:30 Maarten Lambrechts lecture about DataViz
12:30-13:30 DataViz Workgroup
13:30-14:30 Lunch
14:30-16:30 DataViz Workgroup

SECOND WEEK PROGRAM

MONDAY, JUNE 26th
09:30-11:00 Python – Deep Overview
11:00-11:30 Break
11:30-13:30 Python – Deep Overview
13:30-14:30 Lunch
14:30-17:30 Dataset Presentation
TUESDAY, JUNE 27th
09:30-11:00 Scientific Python (Numpy/Scipy/Pandas)
11:00-11:30 Break
11:30-13:30 Scientific Python (Numpy/Scipy/Pandas)
13:30-14:30 Lunch
14:30-16:30 Statistics & Data Analysis
16:30-17:30 Homework & Teamwork
WEDNESDAY, JUNE 28th
09:30-11:00 Statistics & Data Analysis
11:00-11:30 Break
11:30-13:30 Applied Scientific Python
13:30-14:30 Lunch
14:30-16:30 MongoDB introduction
16:30-17:30 Homework & Teamwork
THURSDAY, JUNE 29th
09:30-11:00 Parallel computing with Dask
11:00-11:30 Break
11:30-13:30 Parallel computing with Dask
13:30-14:30 Lunch
14:30-16:30 Alan Perotti lecture – What makes a good algorithm?
When dealing with big data, the scalability and complexity of algorithms is crucial. In this talk we will address the problem and we will see some basic tips.
16:30-17:30 Homework & Teamwork
FRIDAY, JUNE 30th
09:30-11:00 Data Analysis Exercise
11:00-11:30 Break
11:30-13:30 Data Analysis Exercise
13:30-14:30 Lunch
14:30-16:30 Piero Molino Lecture – Word Embeddings: Past, present and Future
Word Embeddings are both a hot research topic and a useful tool for NLP practitioners, as they provide representations used in many intermediate tasks, like part-of-speech tagging, syntactic parsing or
named entity recognition, as well as end to end tasks like text classification, sentiment analysis and question answering.
The recent attention to this topic started in 2013 when the original word2vec paper was published at NIPS with an efficient and scalable implementation, but a lot of research was carried out on the topic
since the 50s in computer science, cognitive science, and computational linguistics. The first part of the talk will focus on this body of work, with the aim of distilling ideas and learned lessons many practitioners and machine learning researchers may not be unaware of. The second part of the talk will focus on recent developments and novel methods, highlighting interesting directions that are being
explored lately, like the compositionality of meaning, representing words as probability distributions and how to learn representations of knowledge graphs.
16:30-17:30 Homework & Teamwork

THIRD WEEK PROGRAM

MONDAY, JULY 3rd
09:30-11:00 D3.JS advanced
11:00-11:30 Break
11:30-13:30 D3.JS advanced
13:30-14:30 Lunch
14:30-17:00 Machine Learning Theory
17:30-17:30 Homework & Teamwork
TUESDAY, JULY 4th
09:30-11:00 MongoDB in practice
11:00-11:30 Break
11:30-12:30 MongoDB in practice
12:30-13:30 MongoDB & Python
13:30-14:30 Lunch
14:30-17:30 Nexa Center Lecture – Data Quality: concepts, measurements and case studies
Abstract: The lecture will give a introduction on the subject of data quality, with an eye on normative aspects too. The ISO/IEC standards 25012 and 25024 will be presented to illustrate how to model and measure the quality of structured data. Examples and exercises with Open Data will conclude the seminar.
WEDNESDAY, JULY 5th
09:30-11:00 Machine Learning in practice
11:00-11:30 Break
11:30-13:30 Machine Learning in practice
13:30-14:30 Lunch
14:30-16:30 Time to set-up your final project
16:30-17:30 Homework & Teamwork
THURSDAY, JULY 6th
09:30-11:00 Python in Cloud: AWS
11:00-11:30 Break
11:30-12:30 Python in Cloud: AWS
12:30-13:30 Machine Learning in practice
13:30-14:30 Lunch
14:30-16:30 Machine Learning in practice
16:30-17:30 Homework & Teamwork
FRIDAY, JULY 7th
09:30-11:00 Machine Learning – Kaggle challenge
11:00-11:30 Break
11:30-13:30 Machine Learning – Kaggle challenge
13:30-14:30 Lunch
14:30-16:30 Twitter API
16:30-17:30 Homework & Teamwork

FOURTH WEEK PROGRAM

MONDAY, JULY 10th
09:30-11:00 Spark Introduction
11:00-11:30 Break
11:30-13:30 Spark Introduction
13:30-14:30 Lunch
14:30-16:30 Francesco Bonchi Lecture – Mining Information Propagation Data
Abstract: With the success of online social networks and microblogging platforms such as Facebook, Tumblr, and Twitter, the phenomenon of influence-driven propagation, has recently attracted the interest of computer scientists, sociologists, information technologists, and marketing specialists. In this talk we will take a data mining perspective, discussing what (and how) can be learned from a social network and a database of traces of past propagation over the social network. Starting from one of the key problems in this area, i.e. the identification of influential users, we will provide a brief overview of our recent contributions in this area. We will expose the connection between the phenomenon of information propagation and the existence of communities in social network, and we will go deeper in this new research topic arising at the overlap of information propagation analysis and community detection.
16:30-17:30 Homework & Teamwork
TUESDAY, JULY 11th
09:30-11:00 Network Science – Theory and Practice
11:00-11:30 Break
11:30-13:30 Network Science – Theory and Practice
13:30-14:30 Lunch
14:30-16:30 Spark DataFrames
14:30-16:30 Homework & Teamwork
WEDNESDAY, JULY 12th
09:30-11:00 Time to work on Final Project
11:00-11:30 Break
11:30-13:30 Time to work on Final Project
13:30-14:30 Lunch
14:30-16:30 Network Science – Theory and Practice
16:30-17:30 Homework & Teamwork
THURSDAY, JULY 13th
09:30-11:00 Network Science – Theory and Practice
11:00-11:30 Break
11:30-13:30 Network Science – Theory and Practice
13:30-14:30 Lunch
14:30-16:30 Lecture – INTEL for DEEP LEARNING
Mustafa Aldemir started working as a software engineer at a Turkish startup while studying his Bachelor. After his Masters he worked as an embedded software engineer at Siemens for 4 years. He spent 1 year developing mobile banking applications at ING Bank. And finally, he joined Intel as a software engineer in 2016. His focus topics are Internet of Things and Artificial Intelligence. He has been supporting partner companies to develop IoT solutions with Intel hardware. And also, he has been delivering seminars & workshops on IoT and AI.
In the lecture he will cover the following topics: basics of Deep Learning, value proposition of Intel for Deep Learning, Intel Deep Learning SDK, Intel optimized libraries (Caffe, Tensorflow, etc), Nervana and HW accelerators.
FRIDAY, JULY 14th
09:30-13:30 CELI LECTURE: Natural Language Processing and Artificial Intelligence – Overview and Applications to Natural Disaster Management
Abstract: this class will be divided into a theory part and a practice one.
During the theory lesson, we will provide an overview of current trends in Artificial Intelligence, with emphasis on Natural Language Processing applications. We will present use cases within the broad scenario offered by the I-REACT innovation project, funded by the European Commission, aiming to use social media, smartphones and wearables to improve natural disaster management.
During the practice part, students will be able to use the Sophia Analytics text mining platform on real emergency-related data to extract useful information from social media contents.
13:30-14:30 Lunch
14:30-16:30 Spark Practice Mllib + GraphX

FIFTH WEEK PROGRAM

MONDAY, JULY 17th
09:30-10:00 Setting-UP the FINAL RUSH!
10:00-13:30 Final project workgroup
13:30-14:30 Lunch
14:30-17:30 Final project workgroup
TUESDAY, JULY 18th
09:30-13:30 Final project workgroup
13:30-14:30 Lunch
14:30-17:30 Final project workgroup
WEDNESDAY, JULY 19th
09:30-13:30 Final project workgroup
13:30-14:30 Lunch
14:30-16:00 Lecture by Davide Dante Del Vecchio: An effective CyberSecurity strategy in a Continuously changing company
Abstract: Tips and tricks of a cybersecurity manager to survive in a company that is continuously changing
16:00-17:30 Final project workgroup
THURSDAY, JULY 20th
09:00-13:30 Final project finalization
13:30-14:30 Lunch
14:30-**:** Final project finalization
FRIDAY, JULY 21th
10:00-16:00 FINAL PRESENTATION + One2one meeting with Data Sponsors