When you're three hours into an incident with three hundred thousand log lines, "look at the logs" is not an action plan. Data science techniques exist to reduce that problem to something tractable. This isn't about replacing IR tools. It's about augmenting them with analysis patterns that handle scale, identify structure in noisy data, and compress the time between "data dump" and "here's what happened." Timeline Reconstruction with Pandas Building a complete attack timeline is often the first priority in IR. Evidence comes from multiple sources: Windows Security events, Zeek connection logs, Sysmon events, file system timestamps. Getting them into a single chronological view manually is error-prone. pandas handles this well. The key is normalizing timestamps to UTC and merging sources on time: import pandas as pd from evtx import PyEvtxParser import json def load_windows_events ( path , event_ids = None ): parser = PyEvtxParser ( path ) records = [ json . loads ( r [ ' data ' ]) for r in parser .…