Section 1: Welcome, information about this course
Lecture 1
Introduction
01:59
Section 2: Setting up Python and Libraries
Lecture 2
If you already have Python installed
02:15
Lecture 3
File and command to install all necessary libraries at once, with pip
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Lecture 4
Links to help you install pip
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Lecture 5
The libraries, explained
02:33
Lecture 6
If you want to install Python and the libraries at once
01:33
Section 3: Our data set: the Parkinson's Telemedicine Dataset
Lecture 7
Downloading the data
02:32
Lecture 8
A quick explanation of the dataset
02:12
Section 4: Starting our analysis
Lecture 9
Starting a new iPython Notebook
05:44
Lecture 10
Loading the data into our iPython Notebook
03:47
Section 5: Manipulating data with pandas, the data analysis library
Lecture 11
DataFrames are data tables
02:26
Lecture 12
Series are single rows or columns of data
04:17
Lecture 13
Slicing DataFrames to get the data we need
02:53
Lecture 14
Keeping track of the variable names we need
03:57
Lecture 15
Coding Exercise: summary statistics
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Section 6: Visualizing the data to understand it better before modeling
Lecture 16
Looking at the data's distributions with box plots and histograms
06:26
Lecture 17
Seeing multicolinearity with a scatter plot matrix
03:22
Lecture 18
Coding exercise: a single correlation
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Section 7: Transforming the data to prepare it for modeling
Lecture 19
Taking care of multicolinearity
01:55
Lecture 20
Log transforming data to take care of skewed distributions
07:31
Lecture 21
Coding exercise: practicing apply()
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Section 8: Modeling the data
Lecture 22
Applying a multiple regression to answer the ultimate question
04:41
Section 9: Conclusion
Lecture 23
Thank you
01:32
Lecture 24
Download the data and iPython notebook that was used throughout this lecture
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