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Python Excel and Power BI Crash Engineering Reporting

30 Chapters
View Course Samples Online
01
Engineering reporting workflow overview
role of Python, Excel, and Power BI in crash data pipelines, from raw logs to interactive dashboards.
reportingworkflow
02
Crash data sources and formats
parsing CSV, JSON, and Excel crash dumps with pandas, handling timestamps, severity codes, and missing values.
CSVJSON
03
Excel automation with openpyxl
reading and writing crash report workbooks, styling cells, and generating summary sheets programmatically.
automationopenpyxl
04
Advanced Excel manipulation with pandas and xlsxwriter
multi-sheet consolidation, pivot tables, and conditional formatting for crash metrics.
Advancedmanipulation
05
Data cleaning for crash records
deduplication, outlier detection, normalization of fault codes, and validation rules using Python.
cleaningdeduplication
06
Building a crash data model in Power BI
importing Excel outputs, defining relationships, and creating calculated columns with DAX.
Buildingimporting
07
DAX fundamentals for crash reporting
measures for crash frequency, severity rates, and time-based comparisons.
DAXreporting
08
Time intelligence in Power BI
analyzing crash trends by hour, day, and month using DAX time functions and slicers.
intelligenceanalyzing
09
Interactive dashboard design for crash engineering
KPI cards, drill-through reports, and geospatial mapping of crash locations.
Interactivedashboard
10
Automating Excel report generation from Python
scheduling scripts, templating with Jinja2, and exporting formatted workbooks.
Automatinggeneration
11
Power BI data refresh automation
configuring gateways, scheduled refresh, and incremental refresh for large crash datasets.
automationconfiguring
12
Integrating Python scripts in Power BI
using Python visual and data import to apply custom crash analytics.
Integratinganalytics
13
Crash severity classification with Python
feature engineering, decision trees, and exporting predictions to Excel for Power BI.
severityclassification
14
Root cause analysis reporting
combining Python correlation analysis with Power BI decomposition trees and key influencers.
analysisreporting
15
Building a crash data pipeline
extracting from source systems, transforming with pandas, loading into Excel and Power BI.
Buildingpipeline
16
Excel as a data source for Power BI
best practices for table structures, named ranges, and avoiding refresh errors.
practicesstructures
17
Power Query for crash data preparation
merging Excel files, unpivoting columns, and handling messy crash logs.
preparationunpivoting
18
Statistical summaries in Excel and Power BI
descriptive statistics, histograms, and box plots for crash duration and impact.
Statisticalsummaries
19
Creating custom visuals for crash engineering
using Python matplotlib in Power BI and Excel charts for specialized plots.
Creatingmatplotlib
20
Report performance optimization
reducing Excel file size, optimizing DAX, and managing Power BI dataset modes.
performanceoptimization
21
Version control and collaboration for reporting assets
Git for Python scripts, Excel templates, and Power BI project files.
collaborationreporting
22
Error handling and logging in Python reporting scripts
try-except blocks, logging to files, and alerting on failures.
handlingreporting
23
Security and governance
protecting sensitive crash data in Excel and Power BI, row-level security, and compliance.
Securitygovernance
24
Deploying crash reporting solutions
packaging Python scripts as executables, publishing Power BI reports to workspaces.
Deployingreporting
25
Case study
end-to-end crash reporting for a fleet—from raw telemetry to Excel summary and Power BI dashboard.
end-to-endreporting
26
Case study
incident trend analysis—using Python for time-series decomposition and Power BI for interactive exploration.
incidentanalysis
27
Case study
crash hotspot identification—geospatial clustering in Python and mapping in Power BI.
identificationgeospatial
28
Testing and validation of reporting outputs
unit tests for Python transformations, data quality checks in Power BI.
validationreporting
29
Documentation and handover
creating technical documentation for Python scripts, Excel templates, and Power BI reports.
Documentationhandover
30
Future trends and extensibility
integrating real-time crash data, using Power BI REST API, and scaling with Python.
extensibilityintegrating