ENGR 1330 Computational Thinking with Data Science
Copyright © 2021 Theodore G. Cleveland and Farhang Forghanparast
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Video Archive¶
A collection of links to author generated videos related to these notes. The collection is historical in reverse chronological order (Newest to Oldest) by categories.
Note
Potentional institutional users can see the evolution (de-evolution) of the course coverage as part of the ongoing course development cycle.
Informational and How-To … Videos¶
A quick tour of filesystems on MacOS, Windows, and Linux. File Systems
How to set browser exception on Chrome browser. For students in ENGR 1330 using Chrome. ENGR-1330-2021-HowToSetBrowserException Chrome
Installing Anaconda IDE onto a windows machine (simulated using a Windows VM hosted on a Macintosh, but native install would be same). ENGR-1330-2021-D01/D04-AnacondaOnWindows10
Anaconda IDE on an AWS Lightsail Instance, for students at TTU wishing to use AWS Cloud Computing. ENGR-1330-2021-D01/D04 AnacondaOnAWS
Lesson Videos¶
Spring 2022¶
Fall 2021¶
First meeting, Fall 2021 ENGR-1330-001-2021-3-Lesson00
Computational thinking as a problem solving process; Computing mean using CT principles to decompose the process. ENGR-1330-2021-3-Lesson01
Fundamental arithmetic, variables and expression, data types ENGR-1330-2021-3-Lesson02
Data structures: lists, dictionary, tuple ENGR-1330-2021-3-Lesson03
Input/Output concepts. Interactive input. Explicit type casting after capturing input.ENGR-1330-2021-3-Lesson04
Sequence, selection and repetition.ENGR-1330-2021-3-Lesson05
Functions; intrinsic, user defined. Saving to a file. ENGR-1330-2021-3-Lesson06
File manipulation; read/write. Access files by URL ENGR-1330-2021-3-Lesson07
Matrices as lists, a little intro to linear algebra (a lot more in the readings) ENGR-1330-2021-3-Lesson08
Introduction to numpy. Matrices using numpy. ENGR-1330-2021-3-Lesson09
Pandas (Video Failed)
Dataframes and file reading ENGR-1330-2021-3-Lesson 11
Plotting and Matplotlib introduction ENGR-1330-2021-3-Lesson12
Correlation, causality, probability ENGR-1330-2021-3-Lesson14
Descriptive statistics using primitive python, spipy, numpy, and pandas ENGR-1330-2021-3-Lesson15
Intro to hypothesis tests ENGR-1330-2021-3-Lesson16
A/B Tests, and other comparisons of collections ENGR-1330-2021-3-Lesson17
Interval Estimates ENGR-1330-2021-3-Lesson18
Introduction to RegressionENGR-1330-2021-3-Lesson19
Prediction intervals ENGR-1330-2021-3-Lesson20
Probability estimation data models ENGR-1330-2021-3-Lesson21
Introduction to classification ENGR-1330-2021-3-Lesson22
KNN Classification ENGR-1330-2021-3-Lesson23
Spring 2021¶
Introduction to CT/DS; Blackboard; How to Set Browser Exception; Anaconda; JupyterLab and notebook components ENGR-1330-2021-D01/D04-Lesson-0
Data Structures and Conditional Execution ENGR-1330-2021-D01/D04_Lesson02
Repetition, Loops ENGR-1330-2021-Lesson03
FUNctions. Fun with functions! engr1330 lesson4
Classes, Objects and Files (part 1 until network fail) engr 1330 2021 lesson5 part1
Classes, Objects, and Files. (part2 after network restore)engr 1330 2021 lesson5 part2
NumPy external package; a little linear algebra ENGR-1330-2021-Lesson7
Pandas package. Table manipulation. Read/Write. ENGR1330 Lesson8
Plotting and matplotlib engr1330 lesson9
Causation, correlation, chance and simulation. Monty Hall analysis. engr1330 lesson10
Chance, Events and Simulation. A safe way to play Russian Roulette. engr1330lesson11
Data models using special functions. engr1330 lesson12
Data Models using Probability Distribution Functions engr1330 lesson13
Hypothesis Testing (Introduction) engr1330 lesson14
Hypothesis Testing, And some debugging! engr1330 lesson15
A/B Testing (in Lab), Interval Estimates engr1330 lesson16
Interval Estimates (Continued) engr1330 lesson17
Data Modeling - ML Approaches, Introduction to Regression Tools for Prediction Engines. engr1330 lesson19
More on regression; MLS, WLS and some examples engr1330 lesson20
Target Shooting and Regression engr1330lesson21
Introduction to Classification; Logistic Regression engr1330 lesson22
Fitted Model Quality Measures; Project Questions engr1330 lesson23
Classification Engines; KNN egnr1330 lesson24
Classification by KNN engr1330 lesson25
Random Forests, SVMs engr1330 lesson26
Fall 2020¶
KNN(Fall 2020) engr1330 lesson24
Regression Trees, Random Forests (Fall 2020) engr1330 lesson25