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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
4.5 (11 ratings)
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56 students enrolled
Created by Troy Shu
Last updated 12/2015
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  • 1 hour on-demand video
  • 6 Articles
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
Manipulate and transform data series and tables in Python
Build a multiple regression model in Python
Use iPython Notebook for research and analysis in Python
Visualize data to glean insights from it, in Python
View Curriculum
  • Students should have experience writing, at a minimum, basic programs in Python

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!

Who is the target audience?
  • This course is best suited for students who already have a basic understanding of both Python and statistics
  • This course is for students who like learning with real-life, concrete examples, and following along by programming on their own computers
  • This course is for students who want to learn the basics of data manipulation and visualization, and statistical model building in Python
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Curriculum For This Course
Expand All 24 Lectures Collapse All 24 Lectures 01:03:25
Welcome, information about this course
1 Lecture 01:59
Setting up Python and Libraries
5 Lectures 06:41

File and command to install all necessary libraries at once, with pip

Links to help you install pip

Our data set: the Parkinson's Telemedicine Dataset
2 Lectures 04:44

A quick explanation of the dataset
Starting our analysis
2 Lectures 09:31
Starting a new iPython Notebook

Loading the data into our iPython Notebook
Manipulating data with pandas, the data analysis library
5 Lectures 14:04
DataFrames are data tables

Series are single rows or columns of data

Slicing DataFrames to get the data we need

Keeping track of the variable names we need

Coding Exercise: summary statistics
Visualizing the data to understand it better before modeling
3 Lectures 10:08
Looking at the data's distributions with box plots and histograms

Seeing multicolinearity with a scatter plot matrix

Coding exercise: a single correlation
Transforming the data to prepare it for modeling
3 Lectures 09:44
Taking care of multicolinearity

Log transforming data to take care of skewed distributions

Coding exercise: practicing apply()
Modeling the data
1 Lecture 04:41
Applying a multiple regression to answer the ultimate question
2 Lectures 01:43
Thank you

Download the data and iPython notebook that was used throughout this lecture
About the Instructor
4.5 Average rating
11 Reviews
56 Students
1 Course
Founder of Terragon, building data-driven products

Troy Shu has worked on Wall Street, at a startup, and has now started his own company, building lots of data-driven products and doing tons of data analysis in Python along the way.

He currently runs his own consulting business, building data-driven products for other companies. Before that, Troy worked at a lending startup called Bond Street, where he built the company's risk models and developed the "MVP" (minimum viable product) for the automated loan underwriting platform. He has also worked at a hedge fund where he built stock picking algorithms and launched a new hedge fund. Troy double majored in Computer Science and Economics, with concentrations in Statistics and Finance, at the University of Pennsylvania.

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