ENGR 1330 Course Notes
Welcome to ENGR 1330
0: Introduction
1: Data Science and Problem Solving:
2: Expressions
3: Data Types and Typecasting
4: User Interaction
4.1: Data Structures
5: Algorithm Building Blocks
6: Functions
7: Files
8: Vectors and Matrices (as lists)
7.1: Files from the Web (
requests.get
...
)
9: Matrix Manipulation(s) using
NumPy
10: Vector/Matrix applications (Under Construction)
11: Databases
12: Databases and PANDAS
13: PANDAS Applications (Under Construction)
14: Visual display of data
15: The
matplotlib
package
16: Exploratory Data Analysis
17: Descriptive Statistics
18: Causality, Correlation, Randomness, and Probability
19: Simulation
20: Interval Estimates by Simulation
21: Testing Hypothesis - Introductions
22: Testing Hypothesis - Comparing Collections
23: Testing Hypothesis (continued)
24: Ordinary Functions as Predictor-Response Models
25: Distribution Functions as Magnitude-Probability Models
26: Linear Regression
27: Project Planning Workshop
28: Regression Quality Assessments
29: Multiple Linear Regression
30: Regression using Exponential, Logarithmic, and Power-Law Models
31 – Classification Engines
32: Logistic Regression
33: Classification Engines - Nearest Neighbor
34: KNN Applications
35: KNN Application
36: Artifical Neural Networks
Video Archive
Appendix: Integration of Functions and Tabular Data
Appendix: Newton’s Method
repository
open issue
Index
