Anomaly Detection and Fraud Analytics Virtual Internship
In this advanced virtual internship, students will learn how to build machine learning models for detecting anomalies, outliers, and potential fraudulent activities in complex datasets. The focus will be on applications in financial services, cybersecurity, and manufacturing. Students will gain hands-on experience in data preprocessing, feature engineering, model selection, and performance evaluation. By the end of the internship, they will have a strong foundation in anomaly detection and fraud analytics, preparing them for careers in data science, business intelligence, and risk management.
Track Overview
Tasks & Milestones
Anomaly Detection Use Case Analysis
AdvancedIn this task, students will research and analyze real-world use cases of anomaly detection in finance, cybersecurity, and manufacturing.
Exploratory Data Analysis for Anomaly Detection
AdvancedIn this task, students will perform exploratory data analysis on a dataset to identify potential anomalies and prepare the data for model development.
Supervised Anomaly Detection Model Development
AdvancedIn this task, students will develop and evaluate supervised machine learning models for anomaly detection using a real-world dataset.
Unsupervised Anomaly Detection Model Development
AdvancedIn this task, students will develop and evaluate unsupervised machine learning models for anomaly detection using a real-world dataset.
Prerequisites
- • Proficiency in Python programming
- • Familiarity with data analysis and machine learning concepts
- • Experience with SQL and working with relational databases
Certificate
Certificate of Completion
Earn a certificate upon successful completion