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Exploring and Analyzing Data in Python

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

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4 Hours16 Videos52 Exercises71,398 Learners4150 XP

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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

    Free

    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.

    Play Chapter Now
    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
    Filter
    100 xp
  2. 2

    Distributions

    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.

    Play Chapter Now
    Probability mass functions
    50 xp
    Make a PMF
    100 xp
    Plot a PMF
    100 xp
    Cumulative distribution functions
    50 xp
    Make a CDF
    100 xp
    Compute IQR
    100 xp
    Plot a CDF
    100 xp
    Comparing distributions
    50 xp
    Distribution of education
    50 xp
    Extract education levels
    100 xp
    Plot income CDFs
    100 xp
    Modeling distributions
    50 xp
    Distribution of income
    100 xp
    Comparing CDFs
    100 xp
    Comparing PDFs
    100 xp
  3. 3

    Relationships

    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.

    Play Chapter Now
    Exploring relationships
    50 xp
    PMF of age
    100 xp
    Scatter plot
    100 xp
    Jittering
    100 xp
    Visualizing relationships
    50 xp
    Height and weight
    100 xp
    Distribution of income
    100 xp
    Income and height
    100 xp
    Correlation
    50 xp
    Computing correlations
    100 xp
    Interpreting correlations
    50 xp
    Simple regression
    50 xp
    Income and vegetables
    100 xp
    Fit a line
    100 xp
  4. 4

    Multivariate Thinking

    Explore multivariate relationships using multiple regression to describe non-linear relationships and logistic regression to explain and predict binary variables.

    Play Chapter Now
    Limits of simple regression
    50 xp
    Regression and causation
    50 xp
    Using StatsModels
    100 xp
    Multiple regression
    50 xp
    Plot income and education
    100 xp
    Non-linear model of education
    100 xp
    Visualizing regression results
    50 xp
    Making predictions
    100 xp
    Visualizing predictions
    100 xp
    Logistic regression
    50 xp
    Predicting a binary variable
    100 xp
    Next steps
    50 xp

Datasets

National Survey of Family Growth (NSFG)General Social Survey (GSS)Behavioral Risk Factor Surveillance System (BRFSS)

Collaborators

Chester Ismay
Yashas Roy

Prerequisites

Python Data Science Toolbox (Part 2)
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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