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Bite-Sized Data Science with Python and Pandas: Introduction
Welcome, information about this course
Introduction (1:59)
Setting up Python and Libraries
If you already have Python installed (2:14)
File and command to install all necessary libraries at once, with pip
New Lecture
Links to help you install pip
The libraries, explained (2:33)
If you want to install Python and the libraries at once (1:33)
Our data set: the Parkinson's Telemedicine Dataset
Downloading the data (2:32)
A quick explanation of the dataset (2:12)
Starting our analysis
Starting a new iPython Notebook (5:44)
Loading the data into our iPython Notebook (3:47)
Manipulating data with pandas, the data analysis library
DataFrames are data tables (2:26)
Series are single rows or columns of data (4:17)
Slicing DataFrames to get the data we need (2:53)
Keeping track of the variable names we need (3:57)
Coding Exercise: summary statistics 0:
Visualizing the data to understand it better before modeling
Looking at the data's distributions with box plots and histograms (6:26)
Seeing multicolinearity with a scatter plot matrix (3:22)
Coding exercise: a single correlation
Transforming the data to prepare it for modeling
Taking care of multicolinearity (1:54)
Log transforming data to take care of skewed distributions (7:31)
Coding exercise: practicing apply()
Modeling the data
Applying a multiple regression to answer the ultimate question (4:41)
Conclusion
Thank you (1:32)
Data and iPython notebook
Log transforming data to take care of skewed distributions
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