What does working with time‑series anomaly detection look like at an internship level?
Hey,
I’ll soon be starting an internship where I’ll be working with automotive sensor data (time‑series) in anomaly detection, and I want to prepare properly before I begin.
What should I review or practice beforehand?
Which anomaly detection methods are actually used in real projects (Isolation Forest, Autoencoders, LSTMs, statistical thresholds, etc.)? Are there any others worth knowing?
What tools are typically used for data processing — mostly Pandas, or more Spark when datasets get large?
Do you recommend any courses or resources to get up to speed quickly?
I’d really appreciate any advice from people who’ve worked with time‑series anomalies, especially in automotive or IoT.
u/General_Schedule_574 — 28 days ago