DATA 200: Introduction to Data Analytics

This repository contains python notebooks for Data Science classes. It has previously contained notebooks in support of Introduction to Data Analytics class at Buena Vista University only. It now includes material that was not a part of the original class.

Below is a brief summary of the notebooks:

01 -- 03 -- NumPy vs. Python, vectorized computation, slicing, etc.
04 -- Obtaining data via web scraping with examples
05 -- Working with API's: US Census, Twitter, Foursquare
06 -- Data Cleaning, Preprocessing, and Feature Extraction
07 -- Dealing with Unstructured Data: Bag of words, Count vectorizer, and TF.IDF vectorizer
08 -- Unsupervised learning: K-Means clustering with examples
09 -- Unsupervised learning: Principal Component Analysis for Dimensionality Reduction
0x -- Techniques for data visualization
10 -- Supervised learning: Linear and Logistic Regressions, Gradient Descent, Backpropagation
11 -- TensorFlow: Intro and regressions examples
12 -- Supervised learning: Artificial Neural Networks, Gradient Descent, Backpropagation, TensorFlow
13 -- Case Study: Building a spam filter with TfidfVectorizer, PCA, LogisticRegression/ANN
14 -- Transfer learning with Keras models
15 -- Linear regression: Statistical Perspective

Name		Name	Last commit message	Last commit date
Latest commit History 79 Commits
data		data
01 -- NumPy vs. Conventional Python.ipynb		01 -- NumPy vs. Conventional Python.ipynb
02 -- GameOfLife.ipynb		02 -- GameOfLife.ipynb
03 -- Gray Scott equations modeling.ipynb		03 -- Gray Scott equations modeling.ipynb
04 -- Obtaining Data -- Intro to Web Scraping.ipynb		04 -- Obtaining Data -- Intro to Web Scraping.ipynb
05 -- Working with APIs.ipynb		05 -- Working with APIs.ipynb
06 -- Data Cleaning Preprocessing Feature Extraction.ipynb		06 -- Data Cleaning Preprocessing Feature Extraction.ipynb
07 -- Unstructured Data.ipynb		07 -- Unstructured Data.ipynb
08 -- Unsupervised learning, part 1 Clustering.ipynb		08 -- Unsupervised learning, part 1 Clustering.ipynb
09 -- Unsupervised learning, part 2 Dimensionality Reduction.ipynb		09 -- Unsupervised learning, part 2 Dimensionality Reduction.ipynb
0x -- Data Visualization.ipynb		0x -- Data Visualization.ipynb
10 -- Supervised Learning Linear and Logistic Regression.ipynb		10 -- Supervised Learning Linear and Logistic Regression.ipynb
11 -- Supervised Learning Linear and Logistic Regression with TensorFlow.ipynb		11 -- Supervised Learning Linear and Logistic Regression with TensorFlow.ipynb
12 -- Supervised Learning Artificial Neural Networks.ipynb		12 -- Supervised Learning Artificial Neural Networks.ipynb
13 -- Case Study -- Spam Detection.ipynb		13 -- Case Study -- Spam Detection.ipynb
14 -- Keras and Transfer Learning.ipynb		14 -- Keras and Transfer Learning.ipynb
15 -- Linear Regression (statistical approach).ipynb		15 -- Linear Regression (statistical approach).ipynb
16 -- Linear Regression -- Other Considerations (statistical approach).ipynb		16 -- Linear Regression -- Other Considerations (statistical approach).ipynb
18 -- Boosting.ipynb		18 -- Boosting.ipynb
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
HW 1 -- Solutions.ipynb		HW 1 -- Solutions.ipynb
HW 2 -- Solutions.ipynb		HW 2 -- Solutions.ipynb
HW 5 -- Solutions.ipynb		HW 5 -- Solutions.ipynb
LICENSE		LICENSE
MT Exam -- Prep.ipynb		MT Exam -- Prep.ipynb
README.md		README.md
_config.yml		_config.yml
app01 -- ROC visualization.ipynb		app01 -- ROC visualization.ipynb
mnist.mp4		mnist.mp4

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DATA 200: Introduction to Data Analytics

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