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Bite-Sized Data Science with Python and Pandas: Introduction
Год выпуска: 2015
Производитель: Udemy
Сайт производителя: udemy.com/bite-sized-data-science-with-python-introduction
Автор: Troy Shu
Продолжительность: 1:00
Тип раздаваемого материала: Видеоклипы
Язык: Английский
Описание: Learn the basics of data science with Python, with this short course designed for students to follow along, and built around a concrete, real-world dataset
Listening to theoretical examples is never fun, and I've always liked actually applying what I learn to concrete examples, so this course is built around us analyzing a real-life dataset together. The dataset we'll be using is the "Parkinson's Disease Telemedicine dataset", and our goal will be to see if we can predict the severity of Parkinson's Disease in patients from just a dozen simple measurements, which would be a vast improvement over the current time consuming process that doctors and patients have to go through.

Содержание

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
Text
Lecture 4
Links to help you install pip
Text
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
Text
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
Text
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()
Text
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
Text
Файлы примеров: отсутствуют
Формат видео: MP4
Видео: AVC, 1280x720, 16:9, 29.97fps, 1235kbps
Аудио: AAC, 48kHz, 58kbps, stereo

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