TOPIC
Infrastructure and platforms for Data Science
SUBTOPIC
Modeling, simulation and optimization
LANGUAGE
Spanish
ENROLL
On Demand
DURATION
5 days (20 h)
TRAINER
ITI
In this course the problem of detecting anomalies and the different aspects that characterize it will be presented. The most popular and successful methodologies will be presented, highlighting the advantages and disadvantages of each. Emphasis will be placed on methods applicable to time series.
Program:
Unit 1. What is an anomaly?
Unit 2. The Anomaly Detection Problem
Unit 3. Classification of methodologies
Unit 4. Assembling models
Unit 5. Open source tools
Unit 6. Python use cases
Program:
Unit 1. What is an anomaly?
Unit 2. The Anomaly Detection Problem
Unit 3. Classification of methodologies
Unit 4. Assembling models
Unit 5. Open source tools
Unit 6. Python use cases
Keywords:
Anomaly detection, Machine Learning, Neural Networks, Statistical methods, Time Series Analytics
LEARNING OUTCOMES
Knowledge of the different problems associated with an anomaly detection project.
Knowledge of families of commonly used models.
Understand the advantages and disadvantages of different anomaly detection methodologies.
Use of specialized Python libraries to detect anomalies in tabular data and time series.
Familiarity with other open source tools.
Knowledge of real use cases.
PRE-REQUISITES
ML, Python, Numpy
TARGET AUDIENCE
Professional Data Science
COURSE TYPE
Lecture and Hands-on
MATERIALS
Slides, practical exercises
