Data Anomaly Detection

Data Anomaly Detection

0%

1. What is Anomaly Detection?

Let's delve into the concept of data anomalies, their classification, and their impact on decision-making. Throughout this exploration, we will gain a comprehensive understanding of these topics, accompanied by real-life examples to illustrate their significance.

General Information

Types of Anomalies

How Outliers Influence On Prediction Results

What Should We Do With Detected Outliers

2. Statistical Methods in Anomaly Detection

Statistical methods play a crucial role in various domains, including finance, cybersecurity, manufacturing, and healthcare, where detecting anomalies can prevent fraud, ensure data quality, and improve overall system reliability. These methods are particularly valuable when dealing with large datasets and complex systems, as they provide a systematic and quantitative approach to anomaly detection.

Rule-based Approach

Challenge: Rule-based Approach

Median Absolute Deviation

Challenge: Outlier Detection Using MAD Rule

3. Machine Learning Techniques

Now, let's explore how machine learning methods can effectively address anomalies. We will examine various techniques, including clustering, regularization, dropout, and more, that are employed for this purpose.

Challenge: Using DBSCAN Clustering to Detect Outliers

Challenge: Solving Task Using Regularisation

Contenido del Curso

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?

General Information Types of Anomalies How Outliers Influence On Prediction Results What Should We Do With Detected Outliers

2. Statistical Methods in Anomaly Detection

Rule-based Approach Challenge: Rule-based Approach 1.5 IQR Rule 3-Sigma Rule Median Absolute Deviation Challenge: Outlier Detection Using MAD Rule

3. Machine Learning Techniques

Clustering Challenge: Using DBSCAN Clustering to Detect Outliers Regularisation Challenge: Solving Task Using Regularisation Autoencoders

Challenge: Rule-based Approach

Tarea

Swipe to show code editor

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

Calculate the Euclidean distance for each data point in the dataset.
If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
Create a list to store the identified outliers and print the list.

_{Once you've completed this task, click the button below the code to check your solution.}

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2

toggle bottom row

Challenge: Rule-based Approach

Tarea

Swipe to show code editor

Your task is to create a function that identifies outliers based on the Euclidean distance between each data point and the mean value of the dataset:

Calculate the Euclidean distance for each data point in the dataset.
If the calculated distance of a data point falls outside a predefined range, classify it as an outlier.
Create a list to store the identified outliers and print the list.

_{Once you've completed this task, click the button below the code to check your solution.}

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

some-alt