Notebooks for Python for R Users: A Data Science Approach

• Command Line
• Rodeo
• IDLE
• Jupyter
• Beaker

Introductory Python https://nbviewer.jupyter.org/gist/decisionstats/ce2c16ee98abcf328177 Selecting Data in Pandas https://nbviewer.jupyter.org/gist/decisionstats/01fc540363f1081c5358

• Yelp with Beautiful Soup http://nbviewer.ipython.org/gist/decisionstats/3385dc84c39109f49b83
• Using PyCurl for Web Scraping
• Using Scrapy for Web Scraping
• Social Media Scraping
• Cricket Analysis

• MySQL
• MongoDB
• HDFS
• Spark

• Using SQL for Groupby https://nbviewer.jupyter.org/gist/decisionstats/284a86d0541d06489e92
• Using For Loops
• Apply and Lambda
• Converting data from one format to another ( str)
• Using grepl and gsub
• Subset of a DataFrame and List
• Conditional Manipulation

• Adult DataSet http://nbviewer.ipython.org/gist/decisionstats/4142e98375445c5e4174
• Big Diamonds Dataset
• Iris Dataset

• Basic Plots using MatplotLib
• Advanced Plots using Seaborn
• Data Visualization using GGPlot http://nbviewer.ipython.org/gist/decisionstats/df98ff9df42e7764d600
• Plots using Bokeh

• Using Statsmodels (Boston Dataset)
• Using Pandas
• Using Scikit-learn

• Decision Trees
• Association Analysis
• Clustering Kmeans and Hierarchical
• Neural Networks
• ROC Curves for Models

• ETS Models
• Arima Models

• Measuring Code Speed
• Measuring Code Performance

• Word Cloud (corpus,stopwords,association,tdm)
• Sentiment Analysis

• Diamonds Dataset http://nbviewer.ipython.org/gist/decisionstats/c1684daaeecf62dd4bf4