1 files changed, 135 insertions, 0 deletions
diff --git a/tutorials/module_4/outline.md b/tutorials/module_4/outline.md
new file mode 100644
index 0000000..354a2f0
--- /dev/null
+++ b/tutorials/module_4/outline.md
@@ -0,0 +1,135 @@
+
+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.
+
+---
+
+# **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`
+
+---
+
+## **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
+
+---
+
+## **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
+
+---
+
+## **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
+
+---
+
+## **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
+
+---
+
+## **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
+
+---
+
+## **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)
+
+---
+
+## **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