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Exploratory Data Analysis in Python

Learn how to explore, visualize, and extract insights from data.

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4 Hours16 Videos52 Exercises51,646 Learners4150 XPData Analyst TrackData Scientist Track

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Course Description

How do we get from data to answers? Exploratory data analysis is a process for exploring datasets, answering questions, and visualizing results. This course presents the tools you need to clean and validate data, to visualize distributions and relationships between variables, and to use regression models to predict and explain. You'll explore data related to demographics and health, including the National Survey of Family Growth and the General Social Survey. But the methods you learn apply to all areas of science, engineering, and business. You'll use Pandas, a powerful library for working with data, and other core Python libraries including NumPy and SciPy, StatsModels for regression, and Matplotlib for visualization. With these tools and skills, you will be prepared to work with real data, make discoveries, and present compelling results.

  1. 1

    Read, clean, and validate


    The first step of almost any data project is to read the data, check for errors and special cases, and prepare data for analysis. This is exactly what you'll do in this chapter, while working with a dataset obtained from the National Survey of Family Growth.

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    DataFrames and Series
    50 xp
    Read the codebook
    50 xp
    Exploring the NSFG data
    100 xp
    Clean and Validate
    50 xp
    Validate a variable
    50 xp
    Clean a variable
    100 xp
    Compute a variable
    100 xp
    Filter and visualize
    50 xp
    Make a histogram
    100 xp
    Compute birth weight
    100 xp
    100 xp
  2. 2


    In the first chapter, having cleaned and validated your data, you began exploring it by using histograms to visualize distributions. In this chapter, you'll learn how to represent distributions using Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs). You'll learn when to use each of them, and why, while working with a new dataset obtained from the General Social Survey.

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  3. 3


    Up until this point, you've only looked at one variable at a time. In this chapter, you'll explore relationships between variables two at a time, using scatter plots and other visualizations to extract insights from a new dataset obtained from the Behavioral Risk Factor Surveillance Survey (BRFSS). You'll also learn how to quantify those relationships using correlation and simple regression.

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In the following tracks

Data Analyst Data Scientist


yashasYashas RoychesterChester Ismay
Allen Downey Headshot

Allen Downey

Professor, Olin College

I am a Professor of Computer Science at Olin College in Needham MA, and the author of Think Python, Think Bayes, Think Stats and several other books related to computer science and data science. Previously I taught at Wellesley College and Colby College, and in 2009 I was a Visiting Scientist at Google, Inc. I have a Ph.D. from U.C. Berkeley and B.S. and M.S. degrees from MIT. I write a blog about Bayesian statistics and related topics called Probably Overthinking It. Several of my books are published by O’Reilly Media and all are available under free licenses from Green Tea Press.
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Lloyds Banking Group

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Decision Science Analytics, USAA