Corso Data Science GRATUITO (settembre 2022)
Durata: | 248 ore |
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Periodo: | Dal 26 settembre al 22 novembre 2022 |
Orario: | Dal lun al ven dalle 09.00 alle 16.00 (pausa dalle 13.00 alle 14.00) |
Sede: | Il corso Data Science si svolgerà ONLINE tramite piattaforma webinar |
Partecipanti: | Verranno selezionati massimo 15 partecipanti |
Scopo: | ASSUNZIONE |
Prezzo: | GRATUITO |
Prerequisiti: | Laurea in indirizzi tecnici, basi di programmazione |
Destinatari: | Tutti coloro che si occupano di dati. |
In breve: | Il mondo di oggi produce più dati che mai! Riuscire a trasformarle in informazioni utili è una nuova abilità chiave. Questo corso ti introduce al Data Science pratico, in ti verrà svelato il mistero che circonda l'argomento. Spiegheremo i principi degli algoritmi popolari, mostrandoti come usarli nelle tue applicazioni pratiche. Il corso è composto da tre diversi moduli, base, intermedio e avanzato, per uno sforzo totale di quasi 250 ore (6 ore/giorno). Questo corso è rivolto a tutti coloro che si occupano di dati. Non richiede alcuna programmazione informatica, sebbene sia necessaria una certa esperienza nell'uso dei computer per le attività quotidiane. La matematica del liceo è un prerequisito, insieme ad alcuni concetti di statistica elementare (come medie e varianze). Nelle 8 settimane verrà utilizzato software gratuito. |
Topics ( part-1 / weeks 1-2)
What data science is & Where it can be applied
How simple classification algorithms work
What their strengths and weaknesses are
In what ways real-life classification methods are more complex
How to evaluate a classifier’s performance
What “overfitting” is and how you can combat it
How ensemble techniques can combine the result of different algorithms
Data Science and ethical/legal consideration
Achievements ( part-1 / weeks 1-2)
Demonstrate use of Data Science for key data mining tasks
Evaluate the performance of a classifier on new, unseen, instances
Explain how data scientists can unwittingly overestimate the performance of their system
Identify learning methods that are based on different flavors of simplicity
Apply many different learning methods to a dataset of your choice
Interpret the output produced by classification methods
Describe the principles behind many modern machine learning methods
Compare the decision boundaries produced by different classification algorithms
Debate ethical issues raised by mining personal data
Topics ( part-2 / weeks 3-4-5)
Running large-scale data science experiments
Constructing and executing knowledge flow
Processing very large datasets
Analyzing collections of textual documents
Mining association rules
Preprocessing data using a range of filters
Automatic methods of attribute selection
Clustering data
Taking account of different decision costs
Producing learning curves
Optimizing learning parameters in data
Achievements ( part-2 / weeks 3-4-5)
Compare the performance of different data science
methods on a wide range of datasets
Solve data science problems on huge datasets
Identify the advantages of supervised vs unsupervised
discretization
Classify documents using various techniques
Explain how association rules can be generated and used
Perform attribute selection by wrapping a classifier inside a
cross-validation loop
Develop effective sets of attributes for text classification
problems
Design and evaluate multi-layer neural networks
Calculate optimal parameter values for a given learning
system
Demonstrate how to set up learning tasks as a knowledge flow
Apply equal-width and equal-frequency binning for discretizing
numeric attributes
Evaluate different trade-offs between error rates in 2-class
classification
Debate the correspondence between decision trees and
decision rules
Discuss techniques for representing, generating, and
evaluating clusters
Describe different techniques for searching through subsets of
attributes
Explain cost-sensitive evaluation, cost-sensitive classification,
and cost-sensitive learning and assess the volume of training
data needed for mining tasks
Topics ( part-3 / weeks 6-7-8)
Time series analysis
Data stream mining
Incremental classifiers
Evolving data streams
Support vector machines
Distributed data science
Map-reduce framework
Scripting data science in R, Python and Groovy
Applications: Soil analysis, Sentiment analysis,
Bioinformatics, MRI neuroimaging, Image classification
Achievements ( part-3 / weeks 6-7-8)
Discuss the use of lagged variables in time series forecasting
Identify several different applications of data science
Evaluate the performance of classifiers under conditions of
concept drift
Calculate optimal parameter values for non-linear support
vector machines
Design R, Python and Groovy scripts
Explore the use of overlay data in time series forecasting
Classify tweets using various techniques
Demonstrate how to set up learning tasks as a knowledge flow
Apply equal-width and equal-frequency binning for discretizing
numeric attributes
Experiment with distributed implementations of classifiers and
clusterers
Compare incremental and non-incremental implementations of
classifiers
Apply Python libraries to produce sophisticated visualizations
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