Big Data Analytics in Engineering

Course Description

EGN4060C – Big Data Analytics in Engineering is a 3-credit-hour upper-division engineering course that develops students' competency in the analysis and use of large-scale engineering datasets. The course addresses the increasingly central role of data in modern engineering practice — sensor-based systems, IoT-enabled equipment, manufacturing quality data, infrastructure monitoring, biomedical instrumentation, simulation outputs, and other sources of engineering data at scale that exceed the capacity of traditional analysis approaches. The course covers data acquisition and management, data preprocessing and cleaning, statistical analysis at scale, machine learning fundamentals applied to engineering data, data visualization, and the integration of big data analysis with engineering decision-making.

The "C" lab indicator denotes integrated lecture and laboratory components, with extensive hands-on work using contemporary data analysis tools (Python with NumPy, pandas, scikit-learn, and visualization libraries; R; SQL for database work; Apache Spark or similar for distributed computing where included; cloud platforms such as AWS, Azure, or GCP at introductory level where included). Coursework typically combines lecture and example-based instruction with substantial programming projects, often including capstone-style projects analyzing real engineering datasets.

EGN4060C is a Florida common course offered at approximately 3 Florida institutions. As a relatively recent course addressing rapidly evolving content, the specific emphasis varies among institutions and changes over time. Students should consult their specific institution for the current syllabus and emphasis. EGN4060C transfers as the equivalent course at all Florida public postsecondary institutions per SCNS articulation policy where the receiving institution accepts the course.

Learning Outcomes

Required Outcomes

Upon successful completion of this course, students will be able to:

• Apply foundational data analytics concepts to engineering contexts, including the data analytics workflow (acquisition, cleaning, exploration, modeling, communication); the four V's of big data (volume, velocity, variety, veracity); the engineering value of data analytics.
• Apply data acquisition from engineering sources, including sensor data; database extraction (SQL); web APIs; file formats commonly encountered in engineering (CSV, JSON, XML, HDF5, Parquet); time series and streaming data sources.
• Apply data preprocessing and cleaning, including handling missing data; outlier detection and treatment; data type conversions; data normalization and standardization; the engineering implications of preprocessing decisions.
• Apply exploratory data analysis (EDA), including descriptive statistics; distribution visualization (histograms, density plots, box plots); relationship visualization (scatter plots, correlation matrices, heatmaps); time series visualization; the role of EDA in subsequent analysis.
• Apply statistical analysis at scale, including hypothesis testing, regression analysis (linear, multiple, logistic), and ANOVA — extending introductory statistics to engineering datasets at scale.
• Apply machine learning fundamentals to engineering problems, including supervised learning (regression, classification — logistic regression, decision trees, random forests, gradient boosting at introductory level); unsupervised learning (clustering — k-means, hierarchical; dimensionality reduction — PCA); the appropriate choice of method for engineering contexts.
• Apply model evaluation and validation, including train-test split; cross-validation; appropriate metrics for regression (RMSE, MAE, R²) and classification (accuracy, precision, recall, F1, ROC-AUC); the recognition of overfitting and underfitting; bias-variance tradeoff.
• Apply data visualization at intermediate level, including the principles of effective data visualization; the proper choice of chart type; the design of dashboards for engineering decision-making.
• Apply SQL and database fundamentals, including relational database concepts; SELECT, JOIN, GROUP BY, aggregation; the integration of SQL with Python or R analysis workflows.
• Apply programming for data analytics using Python with NumPy, pandas, matplotlib/seaborn/Plotly, and scikit-learn (most common); R with tidyverse and caret/tidymodels; or comparable institutional choice.
• Apply data analytics ethics and responsible practice, including data privacy, the limitations of data-driven decisions, the recognition of bias in data and models, the engineer's responsibility for data-driven decisions.
• Apply data analytics to engineering case studies reflecting the program's emphasis (manufacturing quality, predictive maintenance, infrastructure monitoring, energy systems, biomedical signal analysis, environmental data).

Optional Outcomes

• Apply distributed computing at introductory level, including Apache Spark or comparable platforms for analyzing data exceeding single-machine capacity.
• Apply cloud computing platforms at introductory level (AWS, Azure, GCP), including the use of cloud-based data storage and computation.
• Apply introductory deep learning, including artificial neural networks at conceptual level, common architectures (feedforward, convolutional, recurrent), and frameworks (TensorFlow, PyTorch) at introductory level.
• Apply time series analysis at intermediate level, including ARIMA models, forecasting, and engineering applications.
• Apply natural language processing at introductory level for engineering applications (technical document analysis, maintenance log analysis).
• Engage with specific engineering domains at greater depth (predictive maintenance, digital twins, IoT for smart infrastructure, biomedical signal processing).

Major Topics

Required Topics

• The Engineering Data Revolution: The increasing role of data in engineering practice; sensor-based systems and IoT; manufacturing quality data; infrastructure monitoring; the four V's of big data (volume, velocity, variety, veracity); the relationship between traditional engineering analysis and data analytics.
• The Data Analytics Workflow: Problem formulation; data acquisition; data preprocessing; exploratory data analysis; modeling; evaluation; communication; iteration; the iterative nature of data analytics work.
• Data Acquisition for Engineering: Sensor data; database extraction; web APIs; common engineering file formats (CSV, JSON, XML, HDF5 for large numerical data, Parquet for analytical databases); time series and streaming data; the integration with engineering instrumentation.
• Data Storage and Management: Flat files; relational databases; document stores; column-oriented databases; the choice between storage approaches; introduction to data lakes and data warehouses at conceptual level.
• SQL and Relational Databases: Tables, rows, columns; primary and foreign keys; SELECT statements; WHERE clauses; JOIN operations (inner, left, right, full); GROUP BY and aggregation; the integration of SQL with Python or R workflows.
• Data Preprocessing: Handling missing data (deletion, imputation); outlier detection (statistical, IQR-based, distance-based); outlier treatment; data type conversions; encoding categorical variables (one-hot, label, target); feature scaling (normalization, standardization); the engineering implications of each choice.
• Exploratory Data Analysis (EDA): Descriptive statistics (univariate, bivariate); univariate distribution visualization (histogram, density, box plot); bivariate visualization (scatter, correlation heatmap); time series plots; the role of EDA in informing subsequent analysis; recognizing patterns and anomalies.
• Statistical Analysis at Scale: The application of foundational statistical methods (hypothesis testing, regression, ANOVA) to engineering datasets; sample size considerations; the multiple-comparisons problem; the role of false discovery rate.
• Machine Learning — Foundations: The supervised vs. unsupervised distinction; the regression vs. classification distinction; the train-test paradigm; the bias-variance tradeoff; the relationship between machine learning and statistics.
• Supervised Learning — Regression: Linear regression (review from foundational statistics); regularized regression (ridge, lasso); decision tree regression; random forest regression; gradient boosting at introductory level; the choice of method.
• Supervised Learning — Classification: Logistic regression; k-nearest neighbors (k-NN); decision tree classification; random forest classification; support vector machines (SVM) at conceptual level; gradient boosting; the choice of method.
• Unsupervised Learning — Clustering: K-means clustering; hierarchical clustering; the choice of number of clusters; the engineering applications.
• Unsupervised Learning — Dimensionality Reduction: Principal component analysis (PCA); the engineering value of dimensionality reduction; the relationship to feature engineering.
• Model Evaluation: Train-test-validation split; k-fold cross-validation; metrics for regression (RMSE, MAE, R², MAPE); metrics for classification (accuracy, precision, recall, F1, ROC-AUC, confusion matrix); the appropriate metric for the engineering context.
• Overfitting and Underfitting: The bias-variance tradeoff; regularization as a tool for managing overfitting; cross-validation as a tool for diagnosis; the engineering implications.
• Feature Engineering: The creation of engineered features from raw data; domain knowledge in feature design; common feature engineering patterns (interactions, polynomial features, time-based features for time series).
• Data Visualization: The principles of effective data visualization; the choice of chart type; the design of dashboards; tools for engineering visualization (matplotlib, seaborn, Plotly for Python; ggplot2 for R; Tableau and Power BI for business intelligence).
• Programming for Data Analytics — Python Ecosystem: NumPy for numerical arrays; pandas for tabular data manipulation; matplotlib/seaborn/Plotly for visualization; scikit-learn for machine learning; the integration of these tools in typical engineering data analytics workflows.
• Programming for Data Analytics — R Ecosystem (Where Used): The tidyverse (dplyr, tidyr, ggplot2); caret or tidymodels for machine learning; R Markdown for reproducible analysis.
• Data Analytics Ethics: Data privacy considerations; the limitations of data-driven decisions; the recognition of bias in data and models; the engineer's responsibility for data-driven decisions; the failure modes of data analytics (the danger of correlation without causation; the danger of confounding variables; the danger of model-driven decisions in domains the model was not designed for).
• Engineering Case Studies: Substantive engineering applications integrating the workflow — predictive maintenance from sensor data; manufacturing quality control with statistical learning; infrastructure monitoring with structural health monitoring data; energy systems analysis; biomedical signal analysis (ECG, EEG, etc.); environmental data analysis.

Optional Topics

• Distributed Computing: Apache Spark for large-scale data processing; PySpark integration with Python workflows; the principles of distributed computation.
• Cloud Computing: AWS (EC2, S3, RDS, SageMaker), Azure (Machine Learning, Synapse), GCP (BigQuery, Vertex AI) at introductory level; the integration of cloud platforms with engineering data work.
• Deep Learning: Artificial neural networks at conceptual level; feedforward, convolutional (CNN), and recurrent (RNN/LSTM) architectures; TensorFlow and PyTorch at introductory level; the engineering applications.
• Time Series Analysis: ARIMA models; seasonality and trend; forecasting; LSTM-based forecasting at introductory level.
• Natural Language Processing: Text data preprocessing; vectorization (TF-IDF, word embeddings); engineering applications (technical document classification, maintenance log analysis).
• Engineering Domain Applications: Predictive maintenance (vibration analysis, fault detection); digital twins (virtual representations of physical assets); smart manufacturing; biomedical signal processing (ECG analysis, sleep stage detection); environmental monitoring; smart cities and infrastructure.

Resources & Tools

• Common Texts: Python for Data Analysis (McKinney — pandas creator); An Introduction to Statistical Learning with Applications in R/Python (James/Witten/Hastie/Tibshirani — ISLR/ISLP, free); Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Géron); Data Science from Scratch (Grus); Mining of Massive Datasets (Leskovec/Rajaraman/Ullman, free)
• Online Resources: Python for Data Science by VanderPlas (free); Coursera and edX courses on data science; Kaggle Learn (free, hands-on data science tutorials); the scikit-learn documentation; pandas documentation
• Software: Python (free, open source) with NumPy, pandas, matplotlib, seaborn, Plotly, scikit-learn; Jupyter notebooks; R with tidyverse, ggplot2, caret/tidymodels; SQL (any dialect — typically PostgreSQL or MySQL or SQLite for institutional teaching); cloud platforms where included (AWS, Azure, GCP — institutional accounts)
• Datasets: Kaggle competitions and datasets; UCI Machine Learning Repository; engineering domain-specific datasets (NASA aircraft engine prognostics dataset; SECOM semiconductor manufacturing dataset; PHM Society competitions data)
• Reference Resources: Towards Data Science (Medium publication); Distill.pub (free, visual explanations); Google's Machine Learning Crash Course; Florida Institute of Technology, USF, UF, FIU data science programs and resources

Career Pathways

EGN4060C supports career pathways at the intersection of engineering and data analytics — increasingly central to many engineering disciplines:

• Manufacturing Engineering — Predictive Maintenance and Quality — The application of data analytics to manufacturing operations.
• Civil and Infrastructure Engineering — Smart Infrastructure — Structural health monitoring, smart cities, asset management.
• Energy Engineering — Smart Grid and Energy Analytics — Florida Power and Light, Duke Energy Florida, renewable energy integration.
• Biomedical Engineering — Biomedical Data Analytics — Clinical data analysis, medical device data, biomedical signal processing.
• Aerospace Engineering — Engineering Data Analytics — Aircraft and spacecraft sensor data, prognostics, performance optimization; relevant to Florida's aerospace sector.
• Environmental Engineering — Environmental Data Analytics — Sensor networks, remote sensing, environmental monitoring (Florida-specific applications include water quality monitoring, hurricane data analysis).
• Engineering Data Science Roles — Hybrid roles combining domain engineering expertise with data analytics skills; increasingly demand-side from major employers.
• Graduate Engineering Study — Foundation for graduate study in data-intensive engineering disciplines.
• Pre-Industry Career Preparation — Direct preparation for industry roles in data-driven engineering organizations.

Special Information

The Rapidly Evolving Nature of the Field

Big data analytics is a rapidly evolving field, and EGN4060C content varies significantly across Florida institutions and changes over time. The specific tools, methods, and emphasis taught in EGN4060C reflect contemporary practice at the time the course is offered. Students should expect that the foundational concepts (the analytics workflow, statistical foundations, machine learning principles) will remain relevant throughout their careers while the specific tools, libraries, and platforms will continue to evolve.

The Engineering-Data Science Boundary

EGN4060C addresses data analytics specifically in engineering contexts, distinguishing it from generic data science courses. Engineering data analytics integrates engineering domain knowledge with statistical and computational methods — recognizing that engineering data has structure (physical relationships, engineering units, conservation laws) that pure data science approaches may not respect. Students who understand both domains have substantial career advantages.

General Education and Transfer

EGN4060C is a Florida common course number that transfers as the equivalent course at all Florida public postsecondary institutions per SCNS articulation policy where the receiving institution accepts the course.

Position in the Engineering Curriculum

EGN4060C is typically taken in the third or fourth year of engineering study, after foundational mathematics, foundational programming, and statistics. The course often serves as a senior elective or capstone-supporting elective. Some Florida programs offer it as a required course in specialized engineering tracks (e.g., manufacturing systems, biomedical engineering, smart infrastructure).

Course Format

EGN4060C is offered in face-to-face, hybrid, and increasingly online formats. The programming-intensive nature of the course translates well to online delivery; many institutions offer fully online sections.

Continuing Education

The data analytics skills developed in EGN4060C support entry-level engineering data analytics careers, but practitioners typically continue to develop these skills throughout their careers through continued professional learning, online courses, conferences, and (often) graduate education. The field rewards continuous learning.