After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Spot Anomalies in Your Data Analysis
Extreme values or anomalies are present in almost any dataset, and it is critical to detect and deal with them before continuing statistical exploration. When left untouched, anomalies can easily disrupt your analyses and skew the performance of machine learning models.
Learn to Use Estimators Like Isolation Forest and Local Outlier Factor
In this course, you'll leverage Python to implement a variety of anomaly detection methods. You'll spot extreme values visually and use tested statistical techniques like Median Absolute Deviation for univariate datasets. For multivariate data, you'll learn to use estimators such as Isolation Forest, k-Nearest-Neighbors, and Local Outlier Factor. You'll also learn how to ensemble multiple outlier classifiers into a low-risk final estimator. You'll walk away with an essential data science tool in your belt: anomaly detection with Python.
Expand Your Python Statistical Toolkit
Better anomaly detection means better understanding of your data, and particularly, better root cause analysis and communication around system behavior. Adding this skill to your existing Python repertoire will help you with data cleaning, fraud detection, and identifying system disturbances.
Prerequisites
Curriculum
Course outline
1
Detecting Univariate Outliers
This chapter covers techniques to detect outliers in 1-dimensional data using histograms, scatterplots, box plots, z-scores, and modified z-scores.
- What are anomalies and outliers?50 XP
- Print a 5-number summary100 XP
- Histograms for outlier detection100 XP
- Scatterplots for outlier detection100 XP
- Box plots and IQR50 XP
- Boxplots for outlier detection100 XP
- Calculating outlier limits with IQR100 XP
- Using outlier limits for filtering100 XP
- Using z-scores for Anomaly Detection50 XP
- Finding outliers with z-scores100 XP
- Using modified z-scores with PyOD100 XP
2
Isolation Forests with PyOD
In this chapter, you’ll learn the ins and outs of how the Isolation Forest algorithm works. Explore how Isolation Trees are built, the essential parameters of PyOD's IForest and how to tune them, and how to interpret the output of IForest using outlier probability scores.
3
Distance and Density-based Algorithms
After a tree-based outlier classifier, you will explore a class of distance and density-based detectors. KNN and Local Outlier Factor classifiers have been proven highly effective in this area, and you will learn how to use them.
4
Time Series Anomaly Detection and Outlier Ensembles
In this chapter, you’ll learn how to perform anomaly detection on time series datasets and make your predictions more stable and trustworthy using outlier ensembles.
Anomaly Detection in Python
Course
Complete
