Automotive Data Analytics Services

The Vendor is required to provide data analytics in automotive technology uses information from vehicles, test labs, and manufacturing systems to help engineers and technicians make smarter decisions.
- Modern cars generate huge amounts of data through sensors and onboard computers, and analytics turn that raw information into insights. 
In testing and validation, it helps team’s spot issues faster, compare designs, and understand how parts perform under different conditions—speeding up development, improving accuracy, and reducing cost. 
- As vehicles become more connected and software‑driven, analytics is essential for improving quality, enabling features like driver‑assist systems, and building safer, smarter cars.
- Course content
•    Learning outcomes and course content outline indicate the intended focus and possible topics. 
•    The instructional designer, with input from subject‑matter experts, may revise outcomes, content, or delivery sequence to strengthen the curriculum. 
•    Any changes must be reviewed and approved by the faculty representative.
- Course outline
•    Explain how automotive systems generate data and how technicians and engineers use it in design, testing, validation, quality control, and service diagnostics,
•    Clean, preprocess, and visualize automotive datasets using python and engineering tools
•    Analyze can‑style signals, telematics data, durability data, manufacturing quality data, driving and service data
•    Apply exploratory data analysis, correlation, regression, and basic machine‑learning techniques
•    Build dashboards that communicate engineering insights to technical and non‑technical audiences
•    Evaluate model performance and interpret results in an engineering context
•    Use data to support design decisions, identify root causes, and recommend improvements
•    Complete a full automotive analytics workflow from raw data to final presentation
- Proposed weekly course outline
1. Introduction to automotive data analytics
•    Lecture: role of data in modern vehicles; types of automotive datasets (can, telematics, durability, quality). 
•    Lab: explore a vehicle data set (CSV) using excel and python; load data, create simple plots.
2. Automotive data pipelines and connected vehicle systems
•    Lecture: telematics, OTA updates, data pipelines, cloud ingestion. 
•    Lab: analyze GPS + speed telemetry; map routes and speed profiles.
3. Data quality in automotive engineering
•    Lecture: missing data, noise, inconsistent signals, and sensor drift. 
•    Lab: identify missing values and noise in can‑style signals using python.
4. Data cleaning and preprocessing
•    Lecture: filtering, smoothing, interpolation, signal alignment. 
•    Lab: clean and preprocess can data; apply smoothing and signal averaging.
5. Exploratory data analysis (EDA)
•    Lecture: descriptive statistics, distributions, engineering interpretation. 
•    Lab: analyze durability or test data; compute summary statistics.
6. Correlation, regression and trend analysis
•    Lecture: identifying relationships, anomalies, and failure patterns. 
•    Lab: perform correlation analysis and simple regression on durability or warranty data.
7. Data visualization for engineering
•    Lecture: time‑series plots, scatter plots, histograms, engineering storytelling. 
•    Lab: create engineering‑focused visualizations using matplotlib.
8. Dashboards for engineering teams
•    Lecture: communicating insights; dashboards for design reviews and quality teams. 
•    Lab: export python results and build a basic dashboard in power bi or tableau.
9. Introduction to predictive analytics
•    Lecture: machine learning basics for technicians; regression models. 
•    Lab: build a simple regression model to predict component performance.
10. Classification and clustering in automotive use cases
•    Lecture: warranty risk classification, driver behavior clustering, anomaly detection. 
•    Lab: perform driver behavior clustering or warranty risk classification using guided python code.
11. Model evaluation and engineering interpretation
•    Lecture: accuracy vs. engineering usefulness; overfitting; confusion matrices. 
•    Lab: evaluate model results and discuss engineering implications.
12. Data‑driven product development
•    Lecture: how engineering teams use data in design reviews and validation. 
•    Lab: analyze test‑track data to compare design alternatives.
13. Manufacturing and quality analytics
•    Lecture: SPC, defect tracking, heat maps, and control charts. 
•    Lab: analyze assembly‑line defect data; identify trends and recommend improvements.
14. Capstone project: data preparation and EDA
•    Lecture: project planning, dataset selection, defining engineering questions. 
•    Lab: clean and explore the chosen dataset; begin analysis.
15. Capstone project: visualization and insights
•    Lecture: engineering storytelling; preparing for a design review. 
•    Lab: build dashboards, finalize analysis, and prepare presentation.
16. Capstone project presentations
•    Lecture: industry pathways in automotive data analytics. 
•    Lab: final project presentations to a mock engineering review panel.