Course Outline
Module I: NumPy – Working with Array Data
1. Creating vectors and matrices using `numpy.array`
2. Array shapes, dimension changes, and data size
3. Basic array operations – indexing, slicing, broadcasting
4. Mathematical functions – element-wise operations
5. Aggregations and statistics (sum, mean, std, etc.)
Module II: Pandas – Tabular Data and Processing
1. Basic data structures: `Series` and `DataFrame`
2. Loading data from `.csv` files
3. Exploring a DataFrame – head, describe, info
4. Filtering records and selecting columns
5. Modifying data: adding, removing, and transforming columns
6. Creating new columns based on existing data
7. Sorting and organizing data
8. Grouping and aggregations
9. Working with large datasets – chunking, data type optimization
Module III: Data Visualization – Matplotlib and Seaborn
1. Differences between matplotlib and seaborn – when to use each library
2. Most important chart types
3. Configuring and styling charts – axes, legends, colors, sizes
4. Creating charts using Pandas data
Module IV: Exploratory Data Analysis (EDA)
1. Introduction to EDA – goals and analysis process
2. Sample analysis on a real dataset
3. Practical project
Requirements
Testimonials (2)
Learning that the QGIS and a tool that can used by other different professionals such land survey
Bame Duncan Koko - Bentel Technologies (Pty) Ltd
Course - QGIS for Geographic Information System
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