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Coursera Project Network

Exploratory Data Analysis

Coursera Project Network via Coursera


In this 1-hour long project-based course, you will learn exploratory data analysis techniques and create visual methods to analyze trends, patterns, and relationships in the data. By the end of this project, you will have applied EDA on a real-world dataset.

This class is for learners who want to use Python for applying data visualization and data analysis, and for learners who are currently taking a basic machine learning course or have already finished a machine learning course and are searching for a practical data visualization and analysis project course. Also, this project provides learners with basic knowledge about exploratory analysis and improves their skills in creating maps which helps them in fulfilling their career goals by adding this project to their portfolios.


  • Exploratory Data Analysis
    • Welcome to this project-based course on Exploratory Data Analysis in Python. In this project, you will learn EDA techniques and create visual methods to analyze trends, patterns, and relationships in the data for your data science projects. By the end of this project, you will have applied EDA on real-world dataset.

Taught by

Roger Peng


4.0 rating, based on 38 Class Central reviews

4.2 rating at Coursera based on 142 ratings

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  • Life is Study
    The first 2 weeks of the course provide a thorough overview of plotting in R using the base graphical package, the lattice package and the ggplot2 package. Week 3 takes a sudden detour into data clustering and the fairly advanced topics of principal…
  • A painful, dull offline course on plotting & clustering in R slapped online with minimal conversion like the rest of JHU's execrable Data Science specialisation*. Hard only due to the appalling pedagogy. (Have these guys heard of labs? Apparently not...)

    *Which, tragically, is apparently one of Coursera's top moneyspinners. Sigh.
  • Anonymous
    Another boring course you'll have to slog through. It's half learning a few things about making plots, half topics that been better covered elsewhere (k-mean). You can actually graduate those courses with horrible programming. As usual you'll learn more by surfing stack-overflow than by the videos. I've done half the assignments before looking at the vids.
  • Brandt Pence
    This is the fourth course in the Data Science specialization. The course covers exploratory analyses in R, primarily making figures using the three most common packages: base R, lattice, and ggplot2. The instructors also manage to throw hierarchical…
  • This is a good starting point for any data analysis work, and the course covers the basics, and a bit more, rather well. It's a bit light on what you should do with the information you gather from your data exploration though.
  • Michal
    The course is a part of very good 'data science with R' program (don't know current name cause it changes) available at Coursera.

    The program is quite massive, it contains about 8 courses but is really thorough and well presented. It is designed with even complete beginners in mind, so may start it without any prior knowledge.
  • Markus Stenemo
    Quite good, quite basic for those who want to review their knowledge. Should be good for those with no previous experience.
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