Excellent — this is a rich and highly practical module for mechanical engineers, since _data analysis and visualization_ tie directly into interpreting experiments, simulations, and sensor data.


It follows your course flow from importing → cleaning/filtering → visualization, and integrates _NumPy, SciPy,_ and _Matplotlib_ progressively.

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# **Module 4: Data Analysis and Processing**

## **Overview**

This module introduces methods for handling, cleaning, and visualizing scientific and experimental data in Python.  
Students will learn to:

- Import data from various sources (CSV, Excel, sensors, simulations)
- Detect and correct data errors or noise
- Filter and smooth signals
- Extract meaningful patterns and trends
- Create clear, professional-quality figures for reports

**Primary Libraries:**  
`NumPy`, `Pandas`, `SciPy.signal`, `SciPy.ndimage`, `Matplotlib`, `Seaborn`

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## **Lecture 1 — Introduction to Data and Scientific Datasets**

**Learning objectives:**

- Understand what makes data “scientific” (units, precision, metadata)
- Recognize types of data: time-series, experimental, simulation, and imaging data
- Identify challenges in data processing (missing data, noise, outliers)
- Overview of the data-analysis workflow

**Activities & Examples:**

- Load small CSV datasets using `numpy.loadtxt()` and `pandas.read_csv()`
- Discuss real ME examples: strain gauge data, thermocouple readings, pressure transducers

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## **Lecture 2 — Importing and Managing Data**

**Learning objectives:**

- Import data from CSV, Excel, and text files using Pandas
- Handle headers, delimiters, and units
- Combine and merge multiple datasets
- Manage data with time or index labels

**Hands-on examples:**

- Combine data from multiple experimental runs
- Import time-stamped data and plot quick trends

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## **Lecture 3 — Data Cleaning and Preprocessing**

**Learning objectives:**

- Detect and handle missing or invalid data
- Identify and remove outliers
- Apply smoothing and detrending
- Unit consistency and scaling

**Techniques & Tools:**

- `pandas.isna()`, `dropna()`, and `fillna()`
- Statistical checks with `numpy.mean()`, `numpy.std()`
- Z-score outlier removal
- Case study: noisy strain vs. time dataset

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## **Lecture 4 — Data Filtering and Signal Processing (SciPy)**

**Learning objectives:**

- Understand why and when filtering is needed
- Apply low-pass, high-pass, and band-pass filters
- Implement moving-average and Savitzky–Golay filters
- Compare frequency vs. time-domain filtering

**Toolbox Focus:** `scipy.signal`  
**Example Applications:**

- Filter noisy vibration data from accelerometers
- Remove DC offset from force measurements

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## **Lecture 5 — Image and Spatial Data Processing (Optional/Extension)**

**Learning objectives:**

- Introduce `scipy.ndimage` for image-based data
- Perform smoothing, edge detection, and segmentation
- Apply spatial filtering to thermal images or contour data

**Applications:**

- Heat distribution image analysis
- Flow visualization from CFD contour plots

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## **Lecture 6 — Data Visualization and Presentation**

**Learning objectives:**

- Create scientific plots using `matplotlib.pyplot`
- Customize figures (labels, legends, styles, subplots)
- Plot multi-dimensional and time-series data
- Combine plots and export for reports

**Extensions:**

- Intro to `seaborn` for statistical visualization
- Plotting uncertainty and error bars

**Capstone Exercise:**

- Load experimental dataset → clean → filter → visualize results  
    (Example: force–displacement data → stress–strain curve with trendline)

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## **Optional Lab/Project Ideas**

- Clean and visualize experimental data from a tensile test
- Filter and interpret vibration data from a rotating machine
- Plot temperature variation in a heat exchanger experiment
- Generate report-quality figures comparing experimental and simulation data