Bite-Sized Data Science with Python and Pandas-Introduction – Troy Shu

Salepage link: At HERE. Archive: http://archive.is/wip/rzjz2

Bite-Sized Data Science with Python and Pandas: Introduction

Follow along as we analyze a real-life dataset and learn data science with Python and Pandas

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.

This course will provide a good introduction to several different aspects of data science, and all in Python, one of the most popular and powerful languages used by data scientists today.

You’ll learn how to:

• Set up your data analysis research environment (in an iPython notebook)
• Visualize the data to understand it better
• Manipulate and transform data to prepare it for modeling
• Apply a statistical model to the data

The course is comprised of short lectures which walk you through the data analysis, as you follow along. There are also several coding exercises throughout to test your knowledge!

Check out the course to learn data science with Python today!Course Curriculum

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)t
• 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