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You can download Supplemental Materials, Lesson Videos, and Transcripts from Downloadables (in the bottom-right corner of the Classroom) or from the Dashboard (the first option on the navigation bar on the left hand side).
You can download these files to work on the programming assignments on your own computer
In this lesson you'll learn what data science is, what it means to be a data scientist, and get started with some hands-on exercises by jumping into some Python code to learn the Pandas library. You'll also start making and testing your own hypotheses by working with the Titanic survivors data set!
In this lesson you'll learn how to clean up data that you obtain from various sources in order to make it useable in analysis.
In this lesson you'll learn various ways to analyze data, including parametric tests (such as Welch's t-test), nonparametric tests (such as the Mann-Whitney U test), and linear regression.
In this lesson you'll learn the importance of representing data in visual form. You'll learn about some of the common pitfalls in data visualization as well as study different methods of representation. You'll also look at one of the historical classics of data visualization, Minard's map of Napoleon's Russian campaign.
In this lesson you'll be introduced to the MapReduce paradigm for data processing: how it works, and what sorts of problems it is useful for. (For a more detailed study of the MapReduce paradigm, take a look at our Intro to Hadoop and MapReduce course!)
This page has important information about project submission and evaluation.
Follow this link to access the final project.
Thanks to Dave Holtz and Cheng-Han Lee for their work in making this course!