Exploratory Data Analysis With Python and Pandas

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In this Guided Project, you will:

Apply practical Exploratory Data Analysis (EDA) techniques on any tabular dataset using Python packages such as Pandas and Numpy.

Produce data visualizations using Seaborn and Matplotlib

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2-hour long project-based course, you will learn how to perform Exploratory Data Analysis (EDA) in Python. You will use external Python packages such as Pandas, Numpy, Matplotlib, Seaborn etc. to conduct univariate analysis, bivariate analysis, correlation analysis and identify and handle duplicate/missing data. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Python ProgrammingData AnalysisPandasExploratory Data AnalysisEDA

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Initial Data Exploration: Read in data, take a glimpse at a few rows, calculate some summary statistics.

  2. Univariate Analysis: Analyze continuous and categorical variables, one variable at a time.

  3. Bivariate Analysis: Looking at the relationship between two variables at a time.

  4. Identify and Handling Duplicate and Missing Data: Find and remove duplicate rows, and replace missing values with their mean and mode.

  5. Correlation Analysis: Looking at the correlation of numerical variables in the dataset and interpreting the numbers.

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.